Detecting outlier patterns with query-based artificially generated searching conditions
- Yu, Shuo, Xia, Feng, Sun, Yuchen, Tang, Tao, Yan, Xiaoran, Lee, Ivan
- Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
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- Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.
- Authors: Yu, Shuo , Xia, Feng , Sun, Yuchen , Tang, Tao , Yan, Xiaoran , Lee, Ivan
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 8, no. 1 (2021), p. 134-147
- Full Text:
- Reviewed:
- Description: In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures. © 2014 IEEE.
How to optimize an academic team when the outlier member is leaving?
- Yu, Shuo, Liu, Jiaying, Wei, Haoran, Xia, Feng, Tong, Hanghang
- Authors: Yu, Shuo , Liu, Jiaying , Wei, Haoran , Xia, Feng , Tong, Hanghang
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Intelligent Systems Vol. 36, no. 3 (May-Jun 2021), p. 23-30
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- Description: An academic team is a highly cohesive collaboration group of scholars, which has been recognized as an effective way to improve scientific output in terms of both quality and quantity. However, the high staff turnover brings about a series of problems that may have negative influences on team performance. To address this challenge, we first detect the tendency of the member who may potentially leave. Here, the outlierness is defined with respect to familiarity, which is quantified by using collaboration intensity. It is assumed that if a team member has a higher familiarity with scholars outside the team, then this member might probably leave the team. To minimize the influence caused by the leaving of such an outlier member, we propose an optimization solution to find a proper candidate who can replace the outlier member. Based on random walk with graph kernel, our solution involves familiarity matching, skill matching, as well as structure matching. The proposed approach proves to be effective and outperforms existing methods when applied to computer science academic teams.
- Authors: Yu, Shuo , Liu, Jiaying , Wei, Haoran , Xia, Feng , Tong, Hanghang
- Date: 2021
- Type: Text , Journal article
- Relation: IEEE Intelligent Systems Vol. 36, no. 3 (May-Jun 2021), p. 23-30
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- Description: An academic team is a highly cohesive collaboration group of scholars, which has been recognized as an effective way to improve scientific output in terms of both quality and quantity. However, the high staff turnover brings about a series of problems that may have negative influences on team performance. To address this challenge, we first detect the tendency of the member who may potentially leave. Here, the outlierness is defined with respect to familiarity, which is quantified by using collaboration intensity. It is assumed that if a team member has a higher familiarity with scholars outside the team, then this member might probably leave the team. To minimize the influence caused by the leaving of such an outlier member, we propose an optimization solution to find a proper candidate who can replace the outlier member. Based on random walk with graph kernel, our solution involves familiarity matching, skill matching, as well as structure matching. The proposed approach proves to be effective and outperforms existing methods when applied to computer science academic teams.
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:
- Reviewed:
- 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.
The evolution of Turing Award Collaboration Network : bibliometric-level and network-level metrics
- Kong, Xiangjie, Shi, Yajie, Wang, Wei, Ma, Kai, Wan, Liangtian, Xia, Feng
- Authors: Kong, Xiangjie , Shi, Yajie , Wang, Wei , Ma, Kai , Wan, Liangtian , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 6, no. 6 (2019), p. 1318-1328
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- Description: The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades. © 2014 IEEE.
- Authors: Kong, Xiangjie , Shi, Yajie , Wang, Wei , Ma, Kai , Wan, Liangtian , Xia, Feng
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Transactions on Computational Social Systems Vol. 6, no. 6 (2019), p. 1318-1328
- Full Text:
- Reviewed:
- Description: The year of 2017 for the 50th anniversary of the Turing Award, which represents the top-level award in the computer science field, is a milestone. We study the long-term evolution of the Turing Award Collaboration Network, and it can be considered as a microcosm of the computer science field from 1974 to 2016. First, scholars tend to publish articles by themselves at the early stages, and they began to focus on tight collaboration since the late 1980s. Second, compared with the same scale random network, although the Turing Award Collaboration Network has small-world properties, it is not a scale-free network. The reason may be that the number of collaborators per scholar is limited. It is impossible for scholars to connect to others freely (preferential attachment) as the scale-free network. Third, to measure how far a scholar is from the Turing Award, we propose a metric called the Turing Number (TN) and find that the TN decreases gradually over time. Meanwhile, we discover the phenomenon that scholars prefer to gather into groups to do research with the development of computer science. This article presents a new way to explore the evolution of academic collaboration network in the field of computer science by building and analyzing the Turing Award Collaboration Network for decades. © 2014 IEEE.
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