Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery

Papaoikonomou, Athanasios; Kardara, Magdalini; Tserpes, Konstantinos; Varvarigou, Theodora

doi:10.1109/mic.2014.82

Cited by 26 publications

(11 citation statements)

References 10 publications

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“…erefore, algorithms in this family provide different approaches to construct features. With this intuition, frequent sub-networks from the ego-networks are used as features in Papaoikonomou et al [111]. Since trust/distrust relations are usually very sparse and most users have few indegrees or outdegrees, many users could have no triangle-based features; besides triangle-based features may not be robust [21].…”

Section: Supervised Methodsmentioning

confidence: 99%

Trust in Social Media

Tang

Huan

2015

Synthesis Lectures on Information Security, Privacy, and Trust

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Section: Supervised Methodsmentioning

confidence: 99%

Trust in Social Media

Tang

Huan

2015

Synthesis Lectures on Information Security, Privacy, and Trust

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“…Sign prediction in signed networks has been previously studied [7,19,27,35,49]. However, in the signed bipartite setting, many of these methods are no longer applicable, since there are no triangles.…”

Section: Sign Prediction For Undirected Signed Bipartite Networkmentioning

confidence: 99%

“…There have been numerous works that focus on sign prediction under the unipartite setting [7,13,19,27,35,49]. In [7] a supervised classifier was presented exploiting balance theory through cycles of length 3 and greater to predict signs.…”

Section: Related Workmentioning

confidence: 99%

Balance in Signed Bipartite Networks

Derr

Johnson

Chang

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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A large portion of today's big data can be represented as networks. However, not all networks are the same, and in fact, for many that have additional complexities to their structure, traditional general network analysis methods are no longer applicable. For example, signed networks contain both positive and negative links, and thus dedicated theories and algorithms have been developed. However, previous work mainly focuses on the unipartite setting where signed links connect any pair of nodes. Signed bipartite networks on the one hand, are commonly found, but have primarily been overlooked. Their complexities of having two node types where signed links can only form across the two sets introduce challenges that prevent most existing literature on unipartite signed and unsigned bipartite networks from being applied. On the other hand, balance theory, a key signed social theory, has been generally defined for cycles of any length and is being used in the form of triangles for numerous unipartite signed network tasks. However, in bipartite networks there are no triangles and furthermore there exist two types of nodes. Therefore, in this work, we conduct the first comprehensive analysis and validation of balance theory using the smallest cycle in signed bipartite networks -signed butterflies (i.e., cycles of length 4 containing the two node types). Then, to investigate the applicability of balance theory aiding signed bipartite network tasks, we develop multiple sign prediction methods that utilize balance theory in the form of signed butterflies. Our sign prediction experiment on three real-world signed bipartite networks demonstrates the effectiveness of using these signed butterflies for not only sign prediction, but paves the way for improvements in other signed bipartite network analysis tasks.

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“…These networks can be modeled as signed graphs whose edges have either positive or negative signs. Great research efforts have been spent on the unipartite signed graphs [4,18,22,25,26,37]. However, as a common form of signed graphs, the signed bipartite graphs have been overlooked by the research community.…”

Section: Introductionmentioning

confidence: 99%

Polarity-based graph neural network for sign prediction in signed bipartite graphs

Zhang

Wang

et al. 2022

World Wide Web

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As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.

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Predicting Edge Signs in Social Networks Using Frequent Subgraph Discovery

Cited by 26 publications

References 10 publications

Trust in Social Media

Trust in Social Media

Balance in Signed Bipartite Networks

Polarity-based graph neural network for sign prediction in signed bipartite graphs

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