Deep video anomaly detection : opportunities and challenges
- Ren, Jing, Xia, Feng, Liu, Yemeng, Lee, Ivan
- Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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- Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.
- Authors: Ren, Jing , Xia, Feng , Liu, Yemeng , Lee, Ivan
- Date: 2021
- Type: Text , Conference paper
- Relation: 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021, Virtual, Online 7-10 December 2021, IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December, p. 959-966
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- Description: Anomaly detection is a popular and vital task in various research contexts, which has been studied for several decades. To ensure the safety of people's lives and assets, video surveillance has been widely deployed in various public spaces, such as crossroads, elevators, hospitals, banks, and even in private homes. Deep learning has shown its capacity in a number of domains, ranging from acoustics, images, to natural language processing. However, it is non-trivial to devise intelligent video anomaly detection systems cause anomalies significantly differ from each other in different application scenarios. There are numerous advantages if such intelligent systems could be realised in our daily lives, such as saving human resources in a large degree, reducing financial burden on the government, and identifying the anomalous behaviours timely and accurately. Recently, many studies on extending deep learning models for solving anomaly detection problems have emerged, resulting in beneficial advances in deep video anomaly detection techniques. In this paper, we present a comprehensive review of deep learning-based methods to detect the video anomalies from a new perspective. Specifically, we summarise the opportunities and challenges of deep learning models on video anomaly detection tasks, respectively. We put forth several potential future research directions of intelligent video anomaly detection system in various application domains. Moreover, we summarise the characteristics and technical problems in current deep learning methods for video anomaly detection. © 2021 IEEE.
Graph learning for anomaly analytics : algorithms, applications, and challenges
- Ren, Jing, Xia, Feng, Lee, Ivan, Noori Hoshyar, Azadeh, Aggarwal, Charu
- Authors: Ren, Jing , Xia, Feng , Lee, Ivan , Noori Hoshyar, Azadeh , Aggarwal, Charu
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Intelligent Systems and Technology Vol. 14, no. 2 (2023), p.
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- Description: Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery.
- Authors: Ren, Jing , Xia, Feng , Lee, Ivan , Noori Hoshyar, Azadeh , Aggarwal, Charu
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Intelligent Systems and Technology Vol. 14, no. 2 (2023), p.
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- Description: Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field. © 2023 Association for Computing Machinery.
Efficient anomaly recognition using surveillance videos
- Saleem, Gulshan, Bajwa, Usama, Raza, Rana, Alqahtani, Fayez, Tolba, Amr, Xia, Feng
- Authors: Saleem, Gulshan , Bajwa, Usama , Raza, Rana , Alqahtani, Fayez , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: PeerJ Computer Science Vol. 8, no. (2022), p.
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- Description: Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model's performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources. © Copyright 2022 Saleem et al.
- Authors: Saleem, Gulshan , Bajwa, Usama , Raza, Rana , Alqahtani, Fayez , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: PeerJ Computer Science Vol. 8, no. (2022), p.
- Full Text:
- Reviewed:
- Description: Smart surveillance is a difficult task that is gaining popularity due to its direct link to human safety. Today, many indoor and outdoor surveillance systems are in use at public places and smart cities. Because these systems are expensive to deploy, these are out of reach for the vast majority of the public and private sectors. Due to the lack of a precise definition of an anomaly, automated surveillance is a challenging task, especially when large amounts of data, such as 24/7 CCTV footage, must be processed. When implementing such systems in real-time environments, the high computational resource requirements for automated surveillance becomes a major bottleneck. Another challenge is to recognize anomalies accurately as achieving high accuracy while reducing computational cost is more challenging. To address these challenge, this research is based on the developing a system that is both efficient and cost effective. Although 3D convolutional neural networks have proven to be accurate, they are prohibitively expensive for practical use, particularly in real-time surveillance. In this article, we present two contributions: a resource-efficient framework for anomaly recognition problems and two-class and multi-class anomaly recognition on spatially augmented surveillance videos. This research aims to address the problem of computation overhead while maintaining recognition accuracy. The proposed Temporal based Anomaly Recognizer (TAR) framework combines a partial shift strategy with a 2D convolutional architecture-based model, namely MobileNetV2. Extensive experiments were carried out to evaluate the model's performance on the UCF Crime dataset, with MobileNetV2 as the baseline architecture; it achieved an accuracy of 88% which is 2.47% increased performance than available state-of-the-art. The proposed framework achieves 52.7% accuracy for multiclass anomaly recognition on the UCF Crime2Local dataset. The proposed model has been tested in real-time camera stream settings and can handle six streams simultaneously without the need for additional resources. © Copyright 2022 Saleem et al.
Trust-aware detection of malicious users in dating social networks
- Shen, Xingfa, Lv, Wentao, Qiu, Jianhui, Kaur, Achhardeep, Xiao, Fengjun, Xia, Feng
- Authors: Shen, Xingfa , Lv, Wentao , Qiu, Jianhui , Kaur, Achhardeep , Xiao, Fengjun , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 5 (2023), p. 2587-2598
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- Description: Online dating is an increasingly thriving business which boosts billion-dollar revenues and attracts users in the tens of millions. Despite its popularity, internet dating is not exempt from the concerns about privacy and trust posed by the revelation of potentially sensitive data as well as the exposure to self-reported (and hence potentially distorted) information. The increasing popularity of online dating networks leads to an increase in security concerns and challenges, as well as harmful actions and attacks, such as creating fake accounts, phishing on these networks. To maintain the safety of legitimate online dating users, it is critical to recognize and isolate criminal people as soon as possible. However, researchers concerning malicious user detection in dating social networks are merely a few. To address some key challenges in this space, we propose a trust-aware detection framework to detect malicious users based on different kinds of data from a real dating site. In particular, we develop a user trust model to distinguish between malicious and legitimate users. Furthermore, we propose a novel data-balancing method to improve the recall rate of malicious user detection. Extensive experiments have been conducted over real-world datasets. The results show that the proposed approach yields a precision of up to 59.16% and a recall rate of up to 73%, which is significantly higher than other baseline algorithms. © 2014 IEEE.
SEeMS : advanced artificial neural networks for employee learning motivation prediction
- Sin, Audrey, Islam, Sardar, Prentice, Catherine, Xia, Feng
- Authors: Sin, Audrey , Islam, Sardar , Prentice, Catherine , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 7th IEEE International conference for Convergence in Technology, I2CT 2022, Pune, India, 7-9 April 2022, Proceedings 2022 IEEE 7th International conference for Convergence in Technology (I2CT)
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- Description: Employee learning motivation is vital for employee professional development and organisational success. However, worldwide statistics show that employees are generally unmotivated to learn. This study aims to examine employee learning motivation signals to determine the best-fit model for early intervention. In this paper, we present SEeMS a Smart Employee learning Motivation System to predict employee learning motivation autonomously. An Advanced Artificial Neural Networks (AANN) with a blended activation function of Sigmoid and ReLu (bSigReLu) is proposed and compared with other learning models. Experimental results demonstrate that the proposed model outperformed conventional state-of-art models. This novel study contributes to the field of organisational behaviour and data science by extending the usage of kernels and customised activation functions to solve the employee learning motivation problem. The superiority of the algorithm makes SEeMS ideal for practical deployment. According to the predictions, organisations could design better strategies to improve employee learning motivation for targeted employees. It is the first step towards achieving an eco-system of self-motivated employee learning that contributes to employee job satisfaction, performance, and well-being, indirectly contributing to employer competitiveness. © 2022 IEEE.
Graph Force Learning
- Sun, Ke, Liu, Jiaying, Yu, Shuo, Xu, Bo, Xia, Feng
- Authors: Sun, Ke , Liu, Jiaying , Yu, Shuo , Xu, Bo , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 8th IEEE International Conference on Big Data, Big Data 2020 p. 2987-2994
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- Description: Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning. © 2020 IEEE.
- Authors: Sun, Ke , Liu, Jiaying , Yu, Shuo , Xu, Bo , Xia, Feng
- Date: 2020
- Type: Text , Conference paper
- Relation: 8th IEEE International Conference on Big Data, Big Data 2020 p. 2987-2994
- Full Text:
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- Description: Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space. Unfortunately, current studies fail to fully preserve the structural information in the feature space due to random negative sampling strategy during training. To tackle this problem, we study the problem of feature learning and novelty propose a force-based graph learning model named GForce inspired by the spring-electrical model. GForce assumes that nodes are in attractive forces and repulsive forces, thus leading to the same representation with the original structural information in feature learning. Comprehensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed framework. Furthermore, GForce opens up opportunities to use physics models to model node interaction for graph learning. © 2020 IEEE.
Network representation learning: From traditional feature learning to deep learning
- Sun, Ke, Wang, Lei, Xu, Bo, Zhao, Wenhong, Teng, Shyh, Xia, Feng
- Authors: Sun, Ke , Wang, Lei , Xu, Bo , Zhao, Wenhong , Teng, Shyh , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 205600-205617
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- Description: Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Sun, Ke , Wang, Lei , Xu, Bo , Zhao, Wenhong , Teng, Shyh , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 205600-205617
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- Description: Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
Relational structure-aware knowledge graph representation in complex space
- Sun, Ke, Yu, Shuo, Peng, Ciyuan, Wang, Yueru, Alfarraj, Osama, Tolba, Amr, Xia, Feng
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Wang, Yueru , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 11 (2022), p.
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- Description: Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Wang, Yueru , Alfarraj, Osama , Tolba, Amr , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics Vol. 10, no. 11 (2022), p.
- Full Text:
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- Description: Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
COVID-19 datasets : a brief overview
- Sun, Ke, Li, Wuyang, Saikrishna, Vidya, Chadhar, Mehmood, Xia, Feng
- Authors: Sun, Ke , Li, Wuyang , Saikrishna, Vidya , Chadhar, Mehmood , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Science and Information Systems Vol. 19, no. 3 (2022), p. 1115-1132
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- Description: The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved.
- Authors: Sun, Ke , Li, Wuyang , Saikrishna, Vidya , Chadhar, Mehmood , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Computer Science and Information Systems Vol. 19, no. 3 (2022), p. 1115-1132
- Full Text:
- Reviewed:
- Description: The outbreak of the COVID-19 pandemic affects lives and social-economic development around the world. The affecting of the pandemic has motivated researchers from different domains to find effective solutions to diagnose, prevent, and estimate the pandemic and relieve its adverse effects. Numerous COVID-19 datasets are built from these studies and are available to the public. These datasets can be used for disease diagnosis and case prediction, speeding up solving problems caused by the pandemic. To meet the needs of researchers to understand various COVID-19 datasets, we examine and provide an overview of them. We organise the majority of these datasets into three categories based on the category of ap-plications, i.e., time-series, knowledge base, and media-based datasets. Organising COVID-19 datasets into appropriate categories can help researchers hold their focus on methodology rather than the datasets. In addition, applications and COVID-19 datasets suffer from a series of problems, such as privacy and quality. We discuss these issues as well as potentials of COVID-19 datasets. © 2022, ComSIS Consortium. All rights reserved.
Abnormal entity-aware knowledge graph completion
- Sun, Ke, Yu, Shuo, Peng, Ciyuan, Li, Xiang, Naseriparsa, Mehdi, Xia, Feng
- Authors: Sun, Ke , Yu, Shuo , Peng, Ciyuan , Li, Xiang , Naseriparsa, Mehdi , Xia, Feng
- Date: 2022
- Type: Text , Conference paper
- Relation: 22nd IEEE International Conference on Data Mining Workshops, ICDMW 2022, Orlando USA, 28 November to 1 December 2022, Proceedings: 22nd IEEE International Conference on Data Mining Workshops Vol. 2022-November, p. 891-900
- Full Text: false
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- Description: In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities. © 2022 IEEE.
Deep outdated fact detection in knowledge graphs
- Tu, Huiling, Yu, Shuo, Saikrishna, Vidya, Xia, Feng, Verspoor, Karin
- Authors: Tu, Huiling , Yu, Shuo , Saikrishna, Vidya , Xia, Feng , Verspoor, Karin
- Date: 2023
- Type: Text , Conference paper
- Relation: 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 1-4 December 2023, 23rd IEEE International Conference on Data Mining Workshops Proceedings p. 1443-1452
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- Description: Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods. © 2023 IEEE.
- Authors: Tu, Huiling , Yu, Shuo , Saikrishna, Vidya , Xia, Feng , Verspoor, Karin
- Date: 2023
- Type: Text , Conference paper
- Relation: 23rd IEEE International Conference on Data Mining Workshops, ICDMW 2023, Shanghai, China, 1-4 December 2023, 23rd IEEE International Conference on Data Mining Workshops Proceedings p. 1443-1452
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- Description: Knowledge graphs (KGs) have garnered significant attention for their vast potential across diverse domains. However, the issue of outdated facts poses a challenge to KGs, affecting their overall quality as real-world information evolves. Existing solutions for outdated fact detection often rely on manual recognition. In response, this paper presents DEAN (Deep outdatEd fAct detectioN), a novel deep learning-based framework designed to identify outdated facts within KGs. DEAN distinguishes itself by capturing implicit structural information among facts through comprehensive modeling of both entities and relations. To effectively uncover latent out-of-date information, DEAN employs a contrastive approach based on a pre-defined Relations-to-Nodes (R2N) graph, weighted by the number of entities. Experimental results demonstrate the effectiveness and superiority of DEAN over state-of-the-art baseline methods. © 2023 IEEE.
Emergency warning messages dissemination in vehicular social networks: A trust based scheme
- Ullah, Noor, Kong, Xiangjie, Ning, Zhaolong, Tolba, Amr, Alrashoud, Mubarak, Xia, Feng
- Authors: Ullah, Noor , Kong, Xiangjie , Ning, Zhaolong , Tolba, Amr , Alrashoud, Mubarak , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Vehicular Communications Vol. 22 (2020)
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- Description: To ensure users' safety on the road, a plethora of dissemination schemes for Emergency Warning Messages (EWMs) have been proposed in vehicular networks. However, the issue of false alarms triggered by malicious users still poses serious challenges, such as disruption of vehicular traffic especially on highways leading to precarious effects. This paper proposes a novel Trust based Dissemination Scheme (TDS) for EWMs in Vehicular Social Networks (VSNs) to solve the aforementioned issue. To ensure the authenticity of EWMs, we exploit the user-post credibility network for identifying true and false alarms. Moreover, we develop a reputation mechanism by calculating a trust-score for each node based on its social-utility, behavior, and contribution in the network. We utilize the hybrid architecture of VSNs by employing social-groups based dissemination in Vehicle-to-Infrastructure (V2I) mode, whereas nodes' friendship-network in Vehicle-to-Vehicle (V2V) mode. We analyze the proposed scheme for accuracy by extensive simulations under varying malicious nodes ratio in the network. Furthermore, we compare the efficiency of TDS with state-of-the-art dissemination schemes in VSNs for delivery ratio, transmission delay, number of transmissions, and hop-count. The experimental results validate the significant efficacy of TDS in accuracy and aforementioned network parameters. © 2019 Elsevier Inc.
- Authors: Ullah, Noor , Kong, Xiangjie , Ning, Zhaolong , Tolba, Amr , Alrashoud, Mubarak , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Vehicular Communications Vol. 22 (2020)
- Full Text:
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- Description: To ensure users' safety on the road, a plethora of dissemination schemes for Emergency Warning Messages (EWMs) have been proposed in vehicular networks. However, the issue of false alarms triggered by malicious users still poses serious challenges, such as disruption of vehicular traffic especially on highways leading to precarious effects. This paper proposes a novel Trust based Dissemination Scheme (TDS) for EWMs in Vehicular Social Networks (VSNs) to solve the aforementioned issue. To ensure the authenticity of EWMs, we exploit the user-post credibility network for identifying true and false alarms. Moreover, we develop a reputation mechanism by calculating a trust-score for each node based on its social-utility, behavior, and contribution in the network. We utilize the hybrid architecture of VSNs by employing social-groups based dissemination in Vehicle-to-Infrastructure (V2I) mode, whereas nodes' friendship-network in Vehicle-to-Vehicle (V2V) mode. We analyze the proposed scheme for accuracy by extensive simulations under varying malicious nodes ratio in the network. Furthermore, we compare the efficiency of TDS with state-of-the-art dissemination schemes in VSNs for delivery ratio, transmission delay, number of transmissions, and hop-count. The experimental results validate the significant efficacy of TDS in accuracy and aforementioned network parameters. © 2019 Elsevier Inc.
Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks
- Ullah, Noor, Kong, Xiangjie, Lin, Limei, Alrashoud, Mubarak, Tolba, Amr, Xia, Feng
- Authors: Ullah, Noor , Kong, Xiangjie , Lin, Limei , Alrashoud, Mubarak , Tolba, Amr , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Networks Vol. 182, no. (2020), p.
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- Description: This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node's reputation calculation and update. Given that, then we estimate each node's state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs. © 2020 Elsevier B.V.
- Authors: Ullah, Noor , Kong, Xiangjie , Lin, Limei , Alrashoud, Mubarak , Tolba, Amr , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: Computer Networks Vol. 182, no. (2020), p.
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- Description: This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node's reputation calculation and update. Given that, then we estimate each node's state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs. © 2020 Elsevier B.V.
Deep matrix factorization for trust-aware recommendation in social networks
- Wan, Liangtian, Xia, Feng, Kong, Xiangjie, Hsu, Ching-Hsien, Huang, Runhe, Ma, Jianhua
- Authors: Wan, Liangtian , Xia, Feng , Kong, Xiangjie , Hsu, Ching-Hsien , Huang, Runhe , Ma, Jianhua
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Network Science and Engineering Vol. 8, no. 1 (2021), p. 511-528
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- Description: Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE.
- Authors: Wan, Liangtian , Xia, Feng , Kong, Xiangjie , Hsu, Ching-Hsien , Huang, Runhe , Ma, Jianhua
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Network Science and Engineering Vol. 8, no. 1 (2021), p. 511-528
- Full Text:
- Reviewed:
- Description: Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users. © 2013 IEEE.
MODEL : motif-based deep feature learning for link prediction
- Wang, Lei, Ren, Jing, Xu, Bo, Li, Jianxin, Luo, Wei, Xia, Feng
- Authors: Wang, Lei , Ren, Jing , Xu, Bo , Li, Jianxin , Luo, Wei , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
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- Description: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE.
- Authors: Wang, Lei , Ren, Jing , Xu, Bo , Li, Jianxin , Luo, Wei , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 7, no. 2 (2020), p. 503-516
- Full Text:
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- Description: Link prediction plays an important role in network analysis and applications. Recently, approaches for link prediction have evolved from traditional similarity-based algorithms into embedding-based algorithms. However, most existing approaches fail to exploit the fact that real-world networks are different from random networks. In particular, real-world networks are known to contain motifs, natural network building blocks reflecting the underlying network-generating processes. In this article, we propose a novel embedding algorithm that incorporates network motifs to capture higher order structures in the network. To evaluate its effectiveness for link prediction, experiments were conducted on three types of networks: social networks, biological networks, and academic networks. The results demonstrate that our algorithm outperforms both the traditional similarity-based algorithms (by 20%) and the state-of-the-art embedding-based algorithms (by 19%). © 2014 IEEE.
Scholar2vec : vector representation of scholars for lifetime collaborator prediction
- Wang, Wei, Xia, Feng, Wu, Jian, Gong, Zhiguo, Tong, Hanghang, Davison, Brian
- Authors: Wang, Wei , Xia, Feng , Wu, Jian , Gong, Zhiguo , Tong, Hanghang , Davison, Brian
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
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- Description: While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.
- Authors: Wang, Wei , Xia, Feng , Wu, Jian , Gong, Zhiguo , Tong, Hanghang , Davison, Brian
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 15, no. 3 (2021), p.
- Full Text:
- Reviewed:
- Description: While scientific collaboration is critical for a scholar, some collaborators can be more significant than others, e.g., lifetime collaborators. It has been shown that lifetime collaborators are more influential on a scholar's academic performance. However, little research has been done on investigating predicting such special relationships in academic networks. To this end, we propose Scholar2vec, a novel neural network embedding for representing scholar profiles. First, our approach creates scholars' research interest vector from textual information, such as demographics, research, and influence. After bridging research interests with a collaboration network, vector representations of scholars can be gained with graph learning. Meanwhile, since scholars are occupied with various attributes, we propose to incorporate four types of scholar attributes for learning scholar vectors. Finally, the early-stage similarity sequence based on Scholar2vec is used to predict lifetime collaborators with machine learning methods. Extensive experiments on two real-world datasets show that Scholar2vec outperforms state-of-the-art methods in lifetime collaborator prediction. Our work presents a new way to measure the similarity between two scholars by vector representation, which tackles the knowledge between network embedding and academic relationship mining. © 2021 Association for Computing Machinery.
Venue topic model-enhanced joint graph modelling for citation recommendation in scholarly big data
- Wang, Wei, Gong, Zhiguo, Ren, Jing, Xia, Feng, Lv, Zhihan, Wei, Wei
- Authors: Wang, Wei , Gong, Zhiguo , Ren, Jing , Xia, Feng , Lv, Zhihan , Wei, Wei
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
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- Description: Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.
- Authors: Wang, Wei , Gong, Zhiguo , Ren, Jing , Xia, Feng , Lv, Zhihan , Wei, Wei
- Date: 2021
- Type: Text , Journal article
- Relation: ACM Transactions on Asian and Low-Resource Language Information Processing Vol. 20, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author's local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue-venue interaction. To solve this problem, we propose an author topic model-enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues' capacity of exerting topic influence on other venues. The top-susceptibility captures venues' propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-The-Art methods. © 2020 ACM.
Vehicle trajectory clustering based on dynamic representation learning of internet of vehicles
- Wang, Wei, Xia, Feng, Nie, Hansong, Chen, Zhikui, Gong, Zhiguo
- Authors: Wang, Wei , Xia, Feng , Nie, Hansong , Chen, Zhikui , Gong, Zhiguo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
- Full Text:
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- Description: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
- Authors: Wang, Wei , Xia, Feng , Nie, Hansong , Chen, Zhikui , Gong, Zhiguo
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 22, no. 6 (2021), p. 3567-3576
- Full Text:
- Reviewed:
- Description: With the widely used Internet of Things, 5G, and smart city technologies, we are able to acquire a variety of vehicle trajectory data. These trajectory data are of great significance which can be used to extract relevant information in order to, for instance, calculate the optimal path from one position to another, detect abnormal behavior, monitor the traffic flow in a city, and predict the next position of an object. One of the key technology is to cluster vehicle trajectory. However, existing methods mainly rely on manually designed metrics which may lead to biased results. Meanwhile, the large scale of vehicle trajectory data has become a challenge because calculating these manually designed metrics will cost more time and space. To address these challenges, we propose to employ network representation learning to achieve accurate vehicle trajectory clustering. Specifically, we first construct the k-nearest neighbor-based internet of vehicles in a dynamic manner. Then we learn the low-dimensional representations of vehicles by performing dynamic network representation learning on the constructed network. Finally, using the learned vehicle vectors, vehicle trajectories are clustered with machine learning methods. Experimental results on the real-word dataset show that our method achieves the best performance compared against baseline methods. © 2000-2011 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Feng Xia” is provided in this record**
Early-stage reciprocity in sustainable scientific collaboration
- Wang, Wei, Ren, Jing, Alrashoud, Mubarak, Xia, Feng, Mao, Mengyi, Tolba, Amr
- Authors: Wang, Wei , Ren, Jing , Alrashoud, Mubarak , Xia, Feng , Mao, Mengyi , Tolba, Amr
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Informetrics Vol. 14, no. 3 (2020), p.
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- Description: Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency. © 2020 Elsevier Ltd.
- Description: The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-78. This work is partially supported by China Postdoctoral Science Foundation ( 2019M651115 ).
- Authors: Wang, Wei , Ren, Jing , Alrashoud, Mubarak , Xia, Feng , Mao, Mengyi , Tolba, Amr
- Date: 2020
- Type: Text , Journal article
- Relation: Journal of Informetrics Vol. 14, no. 3 (2020), p.
- Full Text:
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- Description: Scientific collaboration is of significant importance in tackling grand challenges and breeding innovations. Despite the increasing interest in investigating and promoting scientific collaborations, we know little about the collaboration sustainability as well as mechanisms behind it. In this paper, we set out to study the relationships between early-stage reciprocity and collaboration sustainability. By proposing and defining h-index reciprocity, we give a comprehensive statistical analysis on how reciprocity influences scientific collaboration sustainability, and find that scholars are not altruism and the key to sustainable collaboration is fairness. The unfair h-index reciprocity has an obvious negative impact on collaboration sustainability. The bigger the reciprocity difference, the less sustainable in collaboration. This work facilitates understanding sustainable collaborations and thus will benefit both individual scholar in optimizing collaboration strategies and the whole academic society in improving teamwork efficiency. © 2020 Elsevier Ltd.
- Description: The authors extend their appreciation to the International Scientific Partnership Program ISPP at King Saud University for funding this research work through ISPP-78. This work is partially supported by China Postdoctoral Science Foundation ( 2019M651115 ).
Not every couple is a pair : a supervised approach for lifetime collaborator identification
- Wang, Wei, Wan, Liangtian, Kong, Xiangjie, Gong, Zhiguo, Xia, Feng
- Authors: Wang, Wei , Wan, Liangtian , Kong, Xiangjie , Gong, Zhiguo , Xia, Feng
- Date: 2019
- Type: Text , Conference paper
- Relation: 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019, Xian, 8-12 July 2019
- Full Text: false
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- Description: While scientific collaboration can be critical for a scholar, some collaborator(s) can be more significant than others, a.k.a. lifetime collaborator(s). This work-in-progress aims to investigate whether it is possible to predict/identify lifetime collaborators given a junior scholar's early profile. For this purpose, we propose a supervised approach by leveraging scholars' local and network properties. Extensive experiments on DBLP digital library demonstrate that lifetime collaborators can be accurately predicted. The proposed model outperforms baseline models with various predictors. Our study may shed light on the exploration of scientific collaborations from the perspective of life-long collaboration. © Proceedings of the 23rd Pacific Asia Conference on Information Systems: Secure ICT Platform for the 4th Industrial Revolution, PACIS 2019.