Journal ArticleDOI
Elimination based algorithm for link prediction on social networks
TLDR
This paper presents a new algorithm for link prediction on social networks that tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future.Abstract:
Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network.read more
Citations
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Journal ArticleDOI
Improving the innovation ability of engineering students: a Science and Technology Innovation Community organisation network analysis
TL;DR: Science and Technology Innovation Communities (STICs) as discussed by the authors are student-led partnerships that bring together businesses, research centres, and university staff, and constitute an effective way of promot...
Journal ArticleDOI
Link Prediction in Social Networks Using Markov Random Field
TL;DR: This research uses Markov random field (MRF) for link prediction problem in social networks and results on Flicker dataset showed the proposed method was better than previous methods in precision and recall.
Proceedings ArticleDOI
Node Degree and Neighbourhood Tightness based Link Prediction in Social Networks
Junchao Guo,Leilei Shi,Lu Liu +2 more
TL;DR: This paper proposes an improved link prediction approach called Common Neighbour Tightness (CNT) based on local information of the nodes and neighbourhood tightness, which considers the common neighbour as a whole and weigh them with local information.
Proceedings ArticleDOI
Performance evaluation of baseline link prediction techniques on simple and complex networks
TL;DR: This article summarized the recent work about link prediction in complex social networks by implementing baseline predictor methods common neighbor, jaccard coefficient, adamic/adar, preferential attachment, LRW and SRW on three data sets.
Book ChapterDOI
A Link Prediction in Social Networks: A Fuzzy Cognitive Map Approach
TL;DR: This paper proposed fuzzy cognitive map approach for link prediction in social networks by analyzing the links/relationships between the nodes and assigning the weight to the links /relationships in the network.
References
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