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.
Quantifying success in science : an overview
- Bai, Xiaomei, Pan, Habxiao, Hou, Jie, Guo, Teng, Lee, Ivan, Xia, Feng
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
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- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.
- Authors: Bai, Xiaomei , Pan, Habxiao , Hou, Jie , Guo, Teng , Lee, Ivan , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 123200-123214
- Full Text:
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- Description: Quantifying success in science plays a key role in guiding funding allocations, recruitment decisions, and rewards. Recently, a significant amount of progresses have been made towards quantifying success in science. This lack of detailed analysis and summary continues a practical issue. The literature reports the factors influencing scholarly impact and evaluation methods and indices aimed at overcoming this crucial weakness. We focus on categorizing and reviewing the current development on evaluation indices of scholarly impact, including paper impact, scholar impact, and journal impact. Besides, we summarize the issues of existing evaluation methods and indices, investigate the open issues and challenges, and provide possible solutions, including the pattern of collaboration impact, unified evaluation standards, implicit success factor mining, dynamic academic network embedding, and scholarly impact inflation. This paper should help the researchers obtaining a broader understanding of quantifying success in science, and identifying some potential research directions. © 2013 IEEE.
- Description: This work was supported in part by the Liaoning Provincial Key Research and Development Guidance Project under Grant 2018104021, and in part by the Liaoning Provincial Natural Fund Guidance Plan under Grant 20180550011.
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.
TOSNet : a topic-based optimal subnetwork identification in academic networks
- Bedru, Hayat, Zhao, Wenhong, Alrashoud, Mubarak, Tolba, Amr, Guo, He, Xia, Feng
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
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- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
- Authors: Bedru, Hayat , Zhao, Wenhong , Alrashoud, Mubarak , Tolba, Amr , Guo, He , Xia, Feng
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Access Vol. 8, no. (2020), p. 201015-201027
- Full Text:
- Reviewed:
- Description: Subnetwork identification plays a significant role in analyzing, managing, and comprehending the structure and functions in big networks. Numerous approaches have been proposed to solve the problem of subnetwork identification as well as community detection. Most of the methods focus on detecting communities by considering node attributes, edge information, or both. This study focuses on discovering subnetworks containing researchers with similar or related areas of interest or research topics. A topic- aware subnetwork identification is essential to discover potential researchers on particular research topics and provide qualitywork. Thus, we propose a topic-based optimal subnetwork identification approach (TOSNet). Based on some fundamental characteristics, this paper addresses the following problems: 1)How to discover topic-based subnetworks with a vigorous collaboration intensity? 2) How to rank the discovered subnetworks and single out one optimal subnetwork? We evaluate the performance of the proposed method against baseline methods by adopting the modularity measure, assess the accuracy based on the size of the identified subnetworks, and check the scalability for different sizes of benchmark networks. The experimental findings indicate that our approach shows excellent performance in identifying contextual subnetworks that maintain intensive collaboration amongst researchers for a particular research topic. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
API : an index for quantifying a scholar's academic potential
- Ren, Jing, Wang, Lei, Wang, Kailai, Yu, Shuo, Hou, Mingliang, Lee, Ivan, Kong, Xiangje, Xia, Feng
- Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangje , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
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- Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.
- Authors: Ren, Jing , Wang, Lei , Wang, Kailai , Yu, Shuo , Hou, Mingliang , Lee, Ivan , Kong, Xiangje , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 178675-178684
- Full Text:
- Reviewed:
- Description: In the context of big scholarly data, various metrics and indicators have been widely applied to evaluate the impact of scholars from different perspectives, such as publication counts, citations, ${h}$-index, and their variants. However, these indicators have limited capacity in characterizing prospective impacts or achievements of scholars. To solve this problem, we propose the Academic Potential Index (API) to quantify scholar's academic potential. Furthermore, an algorithm is devised to calculate the value of API. It should be noted that API is a dynamic index throughout scholar's academic career. By applying API to rank scholars, we can identify scholars who show their academic potentials during the early academic careers. With extensive experiments conducted based on the Microsoft Academic Graph dataset, it can be found that the proposed index evaluates scholars' academic potentials effectively and captures the variation tendency of their academic impacts. Besides, we also apply this index to identify rising stars in academia. Experimental results show that the proposed API can achieve superior performance in identifying potential scholars compared with three baseline methods. © 2019 IEEE.
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