Link Prediction in Social Networks Using Computationally Efficient Topological Features

With the rapid development of the Internet, the computational analysis of social networks has grown to be a salient issue. Various research analyses social network topics, and a considerable amount of attention has been devoted to the issue of link prediction. Link prediction aims to predict the interactions that might occur between two entities in the network. To this aim, this study proposed a novel path and node combined approach and constructed a methodology for measuring node similarities. The method was illustrated with five real datasets obtained from different types of social networks. An extensive comparison of the proposed method against existing link prediction algorithms was performed to demonstrate that the path and node combined approach achieved much higher mean average precision (MAP) and area under the curve (AUC) values than those that only consider common nodes (e.g. Common Neighbours and Adamic/Adar) or paths (e.g. Random Walk with Restart and FriendLink). The results imply that two nodes are more likely to establish a link if they have more common neighbours of lower degrees. The weight of the path connecting two nodes is inversely proportional to the product of degrees of nodes on the pathway. The combination of node and topological features can substantially improve the performance of similarity-based link prediction, compared with node-dependent and path-dependent approaches. The experiments also demonstrate that the path-dependent approaches outperform the node-dependent appraoches. This indicates that topological features of networks may contribute more to improving performance than node features.

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A Novel Similarity Measure of Link Prediction in Multi-Layer Social Networks Based on Reliable Paths

10.21203/rs.3.rs-574927/v1 ◽

2021 ◽

Author(s):

Amin Rezaeipanah

Keyword(s):

Social Networks ◽

Similarity Measure ◽

Network Structure ◽

Online Social Networks ◽

Link Prediction ◽

Similarity Measures ◽

Single Layer ◽

Superior Performance ◽

Local Similarity ◽

Topological Features

Abstract Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user’ on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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Computationally efficient link prediction in a variety of social networks

ACM Transactions on Intelligent Systems and Technology ◽

10.1145/2542182.2542192 ◽

2013 ◽

Vol 5 (1) ◽

pp. 1-25 ◽

Cited By ~ 28

Author(s):

Michael Fire ◽

Lena Tenenboim-Chekina ◽

Rami Puzis ◽

Ofrit Lesser ◽

Lior Rokach ◽

...

Keyword(s):

Social Networks ◽

Link Prediction ◽

Computationally Efficient

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Applications of random graphs

10.1093/oso/9780198709893.003.0011 ◽

2017 ◽

Author(s):

A.C.C. Coolen ◽

A. Annibale ◽

E.S. Roberts

Keyword(s):

Social Networks ◽

Random Graphs ◽

Interaction Network ◽

Power Grids ◽

Protein Protein Interaction ◽

Topological Features ◽

First Case ◽

The Stability ◽

Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

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