Edge-Dual Graph Preserving Sign Prediction for Signed Social Networks

Yuan, Weiwei; He, Kangya; Guan, Donghai; Han, Guangjie

doi:10.1109/access.2017.2746258

Cited by 18 publications

(11 citation statements)

References 30 publications

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“…Comparing the tendency rates for two cases, the sign of the edge is determined by the scenario that has the greater tendency rate. Another work [97] proposes a new method to address the negative link sparsity challenge in signed link prediction problem. It first converts the original graph into the edge-dual graph.…”

Section: Related Workmentioning

confidence: 99%

Social Science–guided Feature Engineering

Beigi

Tang

Liu

2020

ACM Trans. Intell. Syst. Technol.

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Many real-world relations can be represented by signed networks with positive links (e.g., friendships and trust) and negative links (e.g., foes and distrust). Link prediction helps advance tasks in social network analysis such as recommendation systems. Most existing work on link analysis focuses on unsigned social networks. The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks and mandates dedicated efforts on link analysis for signed social networks. Recent findings suggest that properties of signed networks substantially differ from those of unsigned networks and negative links can be of significant help in signed link analysis in complementary ways. In this article, we center our discussion on a challenging problem of signed link analysis . Signed link analysis faces the problem of data sparsity, i.e., only a small percentage of signed links are given. This problem can even get worse when negative links are much sparser than positive ones as users are inclined more toward positive disposition rather than negative. We investigate how we can take advantage of other sources of information for signed link analysis. This research is mainly guided by three social science theories, Emotional Information , Diffusion of Innovations , and Individual Personality . Guided by these, we extract three categories of related features and leverage them for signed link analysis. Experiments show the significance of the features gleaned from social theories for signed link prediction and addressing the data sparsity challenge.

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Section: Related Workmentioning

confidence: 99%

Social Science–guided Feature Engineering

Beigi

Tang

Liu

2020

ACM Trans. Intell. Syst. Technol.

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“…A positive sign denotes friendship or trust and a negative sign denotes the unfriendliness or distrust between the nodes considered. Jaccard coefficient is the popular metric utilized to describe the node similarity of signed social networks [16]. The interactions among the social entities extend beyond normal connections.…”

Section: Existing Work On Link Predictionmentioning

confidence: 99%

“…For predicting the similarity of drug-drug interaction on biological data, chemical data and phenotypic data, metrics like JC, CN, AA, and RA are utilized [29]. In online social network such as YouTube, the Jaccard measure is widely used for measuring the interest similarity [16]. In user-user feature dataset, JC is utilized to pre-process the data for user product prediction in social networks such as Epinions and Flixter [17].…”

Section: Saltonmentioning

confidence: 99%

A review of similarity measures and link prediction models in social networks

al.¹

2020

IJCDS

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Social network is a web-based platform which enables people to share information, make new connections and explore various events that occur in society. In social networks, link prediction techniques are widely used to discover new indirect relationships that may occur in the future. These techniques are also utilized to effectively detect missing links in any monitored network. This study presents a concise review of the similarity measures, techniques employed in predicting future links and application of link prediction with emphasis on dynamic networks. An analysis of available models for link prediction and their suitability for heterogeneous, large, static or dynamic networks is also presented.

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“…Graph is an important complex network model to describe the relationship among various entities in real applications, including knowledge graph, RDF graph, linked data, social network, biological network, and traffic network [1][2][3][4]. Shortest path query is a basic problem on graph model.…”

Section: Introductionmentioning

confidence: 99%

Finding the Shortest Path with Vertex Constraint over Large Graphs

Yang

Wang

et al. 2019

Complexity

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Graph is an important complex network model to describe the relationship among various entities in real applications, including knowledge graph, social network, and traffic network. Shortest path query is an important problem over graphs and has been well studied. This paper studies a special case of the shortest path problem to find the shortest path passing through a set of vertices specified by user, which is NP-hard. Most existing methods calculate all permutations for given vertices and then find the shortest one from these permutations. However, the computational cost is extremely expensive when the size of graph or given set of vertices is large. In this paper, we first propose a novel exact heuristic algorithm in best-first search way and then give two optimizing techniques to improve efficiency. Moreover, we propose an approximate heuristic algorithm in polynomial time for this problem over large graphs. We prove the ratio bound is 3 for our approximate algorithm. We confirm the efficiency of our algorithms by extensive experiments on real-life datasets. The experimental results validate that our algorithms always outperform the existing methods even though the size of graph or given set of vertices is large.

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Edge-Dual Graph Preserving Sign Prediction for Signed Social Networks

Cited by 18 publications

References 30 publications

Social Science–guided Feature Engineering

Social Science–guided Feature Engineering

A review of similarity measures and link prediction models in social networks

Finding the Shortest Path with Vertex Constraint over Large Graphs

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