A Survey of Signed Network Mining in Social Media
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TLDR
A review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers can be found in this article, where the authors classify and review tasks of signed network mining with representative algorithms.Abstract:
Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines. With the increasing prevalence of social media networks, signed network analysis has evolved from developing and measuring theories to mining tasks. In this article, we present a review of mining signed networks in the context of social media and discuss some promising research directions and new frontiers. We begin by giving basic concepts and unique properties and principles of signed networks. Then we classify and review tasks of signed network mining with representative algorithms. We also delineate some tasks that have not been extensively studied with formal definitions and also propose research directions to expand the field of signed network mining.read more
Citations
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Proceedings ArticleDOI
Edge Weight Prediction in Weighted Signed Networks
TL;DR: This paper proposes two novel measures of node behavior: the goodness of a node intuitively captures how much this node is liked/trusted by other nodes, while the fairness of a nodes captures how fair the node is in rating other nodes' likeability or trust level.
Proceedings ArticleDOI
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction
TL;DR: This paper establishes a labeled heterogeneous sentiment dataset which consists of users» sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method, and proposes a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users» latent representations from heterogeneous networks and predict the sign of unobserved sentiment links.
Book ChapterDOI
Signed network embedding in social media
TL;DR: Experimental results on two realworld datasets of social media demonstrate the effectiveness of the proposed deep learning framework SiNE for signed network embedding that optimizes an objective function guided by social theories that provide a fundamental understanding of signed social networks.
Proceedings ArticleDOI
Signed Graph Convolutional Networks
Tyler Derr,Yao Ma,Jiliang Tang +2 more
TL;DR: A dedicated and principled effort that utilizes balance theory to correctly aggregate and propagate the information across layers of a signed GCN model is proposed and empirical experiments comparing the proposed signed GCNs against state-of-the-art baselines for learning node representations in signed networks are performed.
Journal ArticleDOI
Academic social networks: Modeling, analysis, mining and applications
TL;DR: This study investigates the background, the current status, and trends of academic social networks, and systematically review representative research tasks in this domain from three levels: actor, relationship, and network.
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