Title

Link Prediction In Multi-Relational Collaboration Networks

Abstract

Traditional link prediction techniques primarily focus on the effect of potential linkages on the local network neighborhood or the paths between nodes. In this paper, we study the problem of link prediction in networks where instances can simultaneously belong to multiple communities, engendering different types of collaborations. Links in these networks arise from heterogeneous causes, limiting the performance of predictors that treat all links homogeneously. To solve this problem, we introduce a new link prediction framework, Link Prediction using Social Features (LPSF), which weights the network using a similarity function based on features extracted from patterns of prominent interactions across the network. Copyright 2013 ACM.

Publication Date

1-1-2013

Publication Title

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2013

Number of Pages

1445-1447

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2492517.2492584

Socpus ID

84893254273 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893254273

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