Familiarity-based collaborative team recognition in academic social networks
- Yu, Shuo, Xia, Feng, Zhang, Chen, Wei, Haoran, Keogh, Kathleen, Chen, Honglong
- Authors: Yu, Shuo , Xia, Feng , Zhang, Chen , Wei, Haoran , Keogh, Kathleen , Chen, Honglong
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 9, no. 5 (2022), p. 1432-1445
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- Description: Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution. © 2014 IEEE.
- Authors: Yu, Shuo , Xia, Feng , Zhang, Chen , Wei, Haoran , Keogh, Kathleen , Chen, Honglong
- Date: 2022
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 9, no. 5 (2022), p. 1432-1445
- Full Text:
- Reviewed:
- Description: Collaborative teamwork is key to major scientific discoveries. However, the prevalence of collaboration among researchers makes team recognition increasingly challenging. Previous studies have demonstrated that people are more likely to collaborate with individuals they are familiar with. In this work, we employ the definition of familiarity and then propose faMiliarity-based cOllaborative Team recOgnition (MOTO) algorithm to recognize collaborative teams. MOTO calculates the shortest distance matrix within the global collaboration network and the local density of each node. Central team members are initially recognized based on local density. Then, MOTO recognizes the remaining team members by using the familiarity metric and shortest distance matrix. Extensive experiments have been conducted upon a large-scale dataset. The experimental results show that compared with baseline methods, MOTO can recognize the largest number of teams. The teams recognized by the MOTO possess more cohesive team structures and lower team communication costs compared with other methods. MOTO utilizes familiarity in team recognition to identify cohesive academic teams. The recognized teams are in line with real-world collaborative teamwork patterns. Based on team recognition using MOTO, the research team structure and performance are further analyzed for given time periods. The number of teams that consist of members from different institutions increases gradually. Such teams are found to perform better in comparison with those whose members are from the same institution. © 2014 IEEE.
Motifs in big networks : methods and applications
- Yu, Shuo, Xu, Jin, Zhang, Chen, Xia, Feng, Almakhadmeh, Zafer, Tolba, Amr
- Authors: Yu, Shuo , Xu, Jin , Zhang, Chen , Xia, Feng , Almakhadmeh, Zafer , Tolba, Amr
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 183322-183338
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- Description: Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed. © 2013 IEEE.
- Authors: Yu, Shuo , Xu, Jin , Zhang, Chen , Xia, Feng , Almakhadmeh, Zafer , Tolba, Amr
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Access Vol. 7, no. (2019), p. 183322-183338
- Full Text:
- Reviewed:
- Description: Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed. © 2013 IEEE.
OFFER: A Motif Dimensional Framework for Network Representation Learning
- Yu, Shuo, Xia, Feng, Xu, Jin, Chen, Zhikui, Lee, Ivan
- Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
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- Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
- Authors: Yu, Shuo , Xia, Feng , Xu, Jin , Chen, Zhikui , Lee, Ivan
- Date: 2020
- Type: Text , Conference proceedings
- Relation: 29th ACM International Conference on Information and Knowledge Management, CIKM 2020 p. 3349-3352
- Full Text:
- Reviewed:
- Description: Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency. © 2020 ACM.
Subgraph adaptive structure-aware graph contrastive learning
- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
- Full Text:
- Reviewed:
- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
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