CenGCN : centralized convolutional networks with vertex imbalance for scale-free graphs
- Authors: Xia, Feng , Wang, Lei , Tang, Tao , Chen, Xin , Kong, Xiangjie , Oatley, Giles , King, Irwin
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Knowledge and Data Engineering Vol. 35, no. 5 (2023), p. 4555-4569
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- Description: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE.
Deep learning : survey of environmental and camera impacts on internet of things images
- Authors: Kaur, Roopdeep , Karmakar, Gour , Xia, Feng , Imran, Muhammad
- Date: 2023
- Type: Text , Journal article
- Relation: Artificial Intelligence Review Vol. 56, no. 9 (2023), p. 9605-9638
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- Description: Internet of Things (IoT) images are captivating growing attention because of their wide range of applications which requires visual analysis to drive automation. However, IoT images are predominantly captured from outdoor environments and thus are inherently impacted by the camera and environmental parameters which can adversely affect corresponding applications. Deep Learning (DL) has been widely adopted in the field of image processing and computer vision and can reduce the impact of these parameters on IoT images. Albeit, there are many DL-based techniques available in the current literature for analyzing and reducing the environmental and camera impacts on IoT images. However, to the best of our knowledge, no survey paper presents state-of-the-art DL-based approaches for this purpose. Motivated by this, for the first time, we present a Systematic Literature Review (SLR) of existing DL techniques available for analyzing and reducing environmental and camera lens impacts on IoT images. As part of this SLR, firstly, we reiterate and highlight the significance of IoT images in their respective applications. Secondly, we describe the DL techniques employed for assessing the environmental and camera lens distortion impacts on IoT images. Thirdly, we illustrate how DL can be effective in reducing the impact of environmental and camera lens distortion in IoT images. Finally, along with the critical reflection on the advantages and limitations of the techniques, we also present ways to address the research challenges of existing techniques and identify some further researches to advance the relevant research areas. © 2023, The Author(s).
Exploring public sentiment during COVID-19 : a cross country analysis
- Authors: Yu, Shuo , He, Sihan , Cai, Zhen , Lee, Ivan , Naseriparsa, Mehdi , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 3 (2023), p. 1083-1094
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- Description: COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones' social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions. © 2014 IEEE.
Graph learning for anomaly analytics : algorithms, applications, and challenges
- 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.
Graph lifelong learning : a survey
- Authors: Febrinanto, Falih , Xia, Feng , Moore, Kristen , Thapa, Chandra , Aggarwal, Charu
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Computational Intelligence Magazine Vol. 18, no. 1 (2023), p. 32-51
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- Description: Graph learning is a popular approach for perfor ming machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems. © 2005-2012 IEEE.
Knowledge graphs : opportunities and challenges
- Authors: Peng, Ciyuan , Xia, Feng , Naseriparsa, Mehdi , Osborne, Francesco
- Date: 2023
- Type: Text , Journal article
- Relation: Artificial Intelligence Review Vol. 56, no. 11 (2023), p. 13071-13102
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- Description: With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs. © 2023, The Author(s).
Lost at starting line : predicting maladaptation of university freshmen based on educational big data
- Authors: Guo, Teng , Bai, Xiaomei , Zhen, Shihao , Abid, Shagufta , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: Journal of the Association for Information Science and Technology Vol. 74, no. 1 (2023), p. 17-32
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- Description: The transition from secondary education to higher education could be challenging for most freshmen. For students who fail to adjust to university life smoothly, their status may worsen if the university cannot offer timely and proper guidance. Helping students adapt to university life is a long-term goal for any academic institution. Therefore, understanding the nature of the maladaptation phenomenon and the early prediction of “at-risk” students are crucial tasks that urgently need to be tackled effectively. This article aims to analyze the relevant factors that affect the maladaptation phenomenon and predict this phenomenon in advance. We develop a prediction framework (MAladaptive STudEnt pRediction, MASTER) for the early prediction of students with maladaptation. First, our framework uses the SMOTE (Synthetic Minority Oversampling Technique) algorithm to solve the data label imbalance issue. Moreover, a novel ensemble algorithm, priority forest, is proposed for outputting ranks instead of binary results, which enables us to perform proactive interventions in a prioritized manner where limited education resources are available. Experimental results on real-world education datasets demonstrate that the MASTER framework outperforms other state-of-art methods. © 2022 The Authors. Journal of the Association for Information Science and Technology published by Wiley Periodicals LLC on behalf of Association for Information Science and Technology.
MIRROR : Mining implicit relationships via structure-enhanced graph convolutional networks
- Authors: Liu, Jiaying , Xia, Feng , Ren, Jing , Xu, Bo , Pang, Guanson , Chi, Lianhua
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 17, no. 4 (2023), p.
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- Description: Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a clear and generic definition of implicit relationships. Then, we formalize the problem and propose an efficient solution, namely MIRROR, a graph convolutional network (GCN) model to infer implicit ties under explicit connections. MIRROR captures rich information in learning node-level representations by incorporating attributes from heterogeneous neighbors. Furthermore, MIRROR is tolerant of missing node attribute information because it is able to utilize network structure. We empirically evaluate MIRROR on four different genres of networks, achieving state-of-the-art performance for target relations mining. The underlying information revealed by MIRROR contributes to enriching existing knowledge and leading to novel domain insights. © 2023 Association for Computing Machinery.
MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks
- Authors: Ni, Zhichen , Chen, Honglong , Li, Zhe , Wang, Xiaomeng , Yan, Na , Liu, Weifeng , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE/ACM Transactions on Computational Biology and Bioinformatics Vol. 20, no. 4 (2023), p. 2376-2386
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- Description: With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE.
TAP : Traffic Accident Profiling via multi-task spatio-temporal graph representation learning
- Authors: Liu, Zhi , Chen, Yang , Xia, Feng , Bian, Jixin , Zhu, Bing , Shen, Guojiang , Kong, Xiangjie
- Date: 2023
- Type: Text , Journal article
- Relation: ACM Transactions on Knowledge Discovery from Data Vol. 17, no. 4 (2023), p.
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- Description: Predicting traffic accidents can help traffic management departments respond to sudden traffic situations promptly, improve drivers' vigilance, and reduce losses caused by traffic accidents. However, the causality of traffic accidents is complex and difficult to analyze. Most existing traffic accident prediction methods do not consider the dynamic spatio-temporal correlation of traffic data, which leads to unsatisfactory prediction accuracy. To address this issue, we propose a multi-task learning framework (TAP) based on the Spatio-temporal Variational Graph Auto-Encoders (ST-VGAE) for traffic accident profiling. We firstly capture the dynamic spatio-temporal correlation of traffic conditions through a spatio-temporal graph convolutional encoder and embed it as a low-latitude vector. Then, we use a multi-task learning scheme to combine external factors to generate the traffic accident profiling. Furthermore, we propose a traffic accident profiling application framework based on edge computing. This method increases the speed of calculation by offloading the calculation of traffic accident profiling to edge nodes. Finally, the experimental results on real datasets demonstrate that TAP outperforms other state-of-the-art baselines. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
The effect of facial perception and academic performance on social centrality
- Authors: Zhang, Dongyu , Peng, Ciyuan , Chang, Xiaojun , Xia, Feng
- Date: 2023
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 10, no. 3 (2023), p. 970-981
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- Description: Facial perception is of significant influence on the positions of people in social networks. Particularly, students' facial traits can affect their social centrality in educational settings (e.g., students looking intelligent can attract more friends). However, in educational environments, the social biases associated with appearances have alarming consequences, and little research has been done to investigate the effect of facial perception on social networks. Therefore, it is necessary to comprehensively analyze the influence of perceived facial traits on students' status in social interaction. In this article, we explore the effect of facial perception on the social centrality of students in social networks. Because students' social centrality is based on both their study ability and facial traits, this study does a comparative analysis of how facial perception and academic performance influence the social centrality of students. Subsequently, the experimental results demonstrate that facial perception, as well as academic performance, closely correlates with the social centrality of students. Finally, this study contributes to a comprehensive and deep understanding of social networks by analyzing facial trait-based social biases. © 2014 IEEE.
Trust-aware detection of malicious users in dating social networks
- 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.
A generative adversarial active learning method for effective outlier detection
- Authors: Bah, Mohamed , Zhang, Ji , Yu, Ting , Xia, Feng , Li, Zhao , Zhou, Shuigeng , Wang, Hongzhi
- Date: 2022
- Type: Text , Conference paper
- Relation: 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022, Virtual, online, 31 October-2 November 2022, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2022-October, p. 131-139
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- Description: Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem. © 2022 IEEE.
A reliable image quality assessment metric : evaluation using camera impacts
- Authors: Kaur, Roopdeep , Karmakar, Gour , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Pattern Recognition and Image Analysis Vol. 32, no. 3 (2022), p. 551-560
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- Description: Abstract: Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, especially from the outdoor environment are impacted by various parameters such as lens blur, dirty lens, and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as oriented FAST and rotated BRIEF, and structural content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as mean square error, peak signal-to-noise ratio, structural content, oriented FAST, and rotated BRIEF, and structural similarity index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of datasets having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness, and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we propose a new image quality assessment metric that fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster–Shafer theory. Our proposed metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all the above-mentioned camera impacts, our proposed metric exhibits 100% assessment reliability, which includes an enormous improvement over other metrics. © 2022, Pleiades Publishing, Ltd.
Abnormal entity-aware knowledge graph completion
- 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
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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.
CHIEF : clustering With higher-order motifs in big networks
- Authors: Xia, Feng , Yu, Shuo , Liu, Chengfei , Li, Jianxin , Lee, Ivan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Network Science and Engineering Vol. 9, no. 3 (2022), p. 990-1005
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- Description: Clustering network vertices is an enabler of various applications such as social computing and Internet of Things. However, challenges arise for clustering when networks increase in scale. This paper proposes CHIEF (Clustering with HIgher-ordEr motiFs), a solution which consists of two motif clustering techniques: standard acceleration CHIEF-ST and approximate acceleration CHIEF-AP. Both algorithms firstly find the maximal $k$-edge-connected subgraphs within the target networks to lower the network scale by optimizing the network structure with maximal $k$-edge-connected subgraphs, and then use heterogeneous four-node motifs clustering in higher-order dense networks. For CHIEF-ST, we illustrate that all target motifs will be kept after this procedure when the minimum node degree of the target motif is equal or greater than $k$. For CHIEF-AP, we prove that the eigenvalues of the adjacency matrix and the Laplacian matrix are relatively stable after this step. CHIEF offers an improved efficiency of motif clustering for big networks, and it verifies higher-order motif significance. Experiments on real and synthetic networks demonstrate that the proposed solutions outperform baseline approaches in large network analysis, and higher-order motifs outperform traditional triangle motifs in clustering. © 2022 IEEE Computer Society. All rights reserved.
Collaborative filtering with network representation learning for citation recommendation
- Authors: Wang, Wei , Tang, Tao , Xia, Feng , Gong, Zhiguo , Chen, Zhikui , Liu, Huan
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Big Data Vol. 8, no. 5 (2022), p. 1233-1246
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- Description: Citation recommendation plays an important role in the context of scholarly big data, where finding relevant papers has become more difficult because of information overload. Applying traditional collaborative filtering (CF) to citation recommendation is challenging due to the cold start problem and the lack of paper ratings. To address these challenges, in this article, we propose a collaborative filtering with network representation learning framework for citation recommendation, namely CNCRec, which is a hybrid user-based CF considering both paper content and network topology. It aims at recommending citations in heterogeneous academic information networks. CNCRec creates the paper rating matrix based on attributed citation network representation learning, where the attributes are topics extracted from the paper text information. Meanwhile, the learned representations of attributed collaboration network is utilized to improve the selection of nearest neighbors. By harnessing the power of network representation learning, CNCRec is able to make full use of the whole citation network topology compared with previous context-aware network-based models. Extensive experiments on both DBLP and APS datasets show that the proposed method outperforms state-of-the-art methods in terms of precision, recall, and MRR (Mean Reciprocal Rank). Moreover, CNCRec can better solve the data sparsity problem compared with other CF-based baselines. © 2015 IEEE.
Collaborative team recognition : a core plus extension structure
- Authors: Yu, Shuo , Alqahtani, Fayez , Tolba, Amr , Lee, Ivan , Jia, Tao , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Journal of Informetrics Vol. 16, no. 4 (2022), p.
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- Description: Scientific collaboration is a significant behavior in knowledge creation and idea exchange. To tackle large and complex research questions, a trend of team formation has been observed in recent decades. In this study, we focus on recognizing collaborative teams and exploring inner patterns using scholarly big graph data. We propose a collaborative team recognition (CORE) model with a "core + extension"team structure to recognize collaborative teams in large academic networks. In CORE, we combine an effective evaluation index called the collaboration intensity index with a series of structural features to recognize collaborative teams in which members are in close collaboration relationships. Then, CORE is used to guide the core team members to their extension members. CORE can also serve as the foundation for team-based research. The simulation results indicate that CORE reveals inner patterns of scientific collaboration: senior scholars have broad collaborative relationships and fixed collaboration patterns, which are the underlying mechanisms of team assembly. The experimental results demonstrate that CORE is promising compared with state-of-the-art methods. © 2022 Elsevier Ltd.
COVID-19 datasets : a brief overview
- 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.
Deep graph learning for anomalous citation detection
- Authors: Liu, Jiaying , Xia, Feng , Feng, Xu , Ren, Jing , Liu, Huand
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 33, no. 6 (2022), p. 2543-2557
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- Description: Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. © 2012 IEEE.