A new study of using temporality and weights to improve similarity measures for link prediction of social networks

2018 ◽  
Vol 34 (4) ◽  
pp. 2667-2678
Author(s):  
Farshad Aghabozorgi ◽  
Mohammad Reza Khayyambashi
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
Similarity Measures

Discovering spurious links in multiplex networks based on interlayer relevance

2019 ◽  
Vol 7 (5) ◽  
pp. 641-658 ◽  
Author(s):  
Zeynab Samei ◽  
Mahdi Jalili
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Link Prediction ◽  
Large Scale ◽  
Transportation Networks ◽  
Similarity Index ◽  
Similarity Measures ◽  
Computation Complexity ◽  
Multiplex Networks ◽  
Large Scale Networks

Abstract Many real-world complex systems can be better modelled as multiplex networks, where the same individuals develop connections in multiple layers. Examples include social networks between individuals on multiple social networking platforms, and transportation networks between cities based on air, rail and road networks. Accurately predicting spurious links in multiplex networks is a challenging issue. In this article, we show that one can effectively use interlayer information to build an algorithm for spurious link prediction. We propose a similarity index that combines intralayer similarity with interlayer relevance for the link prediction purpose. The proposed similarity index is used to rank the node pairs, and identify those that are likely to be spurious. Our experimental results show that the proposed metric is much more accurate than intralayer similarity measures in correctly predicting the spurious links. The proposed method is an unsupervised method and has low computation complexity, and thus can be effectively applied for spurious link prediction in large-scale networks.

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

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.

scholarly journals An information theoretic approach to link prediction in multiplex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seyed Hossein Jafari ◽  
Amir Mahdi Abdolhosseini-Qomi ◽  
Masoud Asadpour ◽  
Maseud Rahgozar ◽  
Naser Yazdani
Keyword(s):  
Real World ◽  
Link Prediction ◽  
Large Scale ◽  
Similarity Measures ◽  
Prediction Method ◽  
General Purpose ◽  
Fast Method ◽  
Theoretic Approach ◽  
Multiplex Networks ◽  
Wide Range

AbstractThe entities of real-world networks are connected via different types of connections (i.e., layers). The task of link prediction in multiplex networks is about finding missing connections based on both intra-layer and inter-layer correlations. Our observations confirm that in a wide range of real-world multiplex networks, from social to biological and technological, a positive correlation exists between connection probability in one layer and similarity in other layers. Accordingly, a similarity-based automatic general-purpose multiplex link prediction method—SimBins—is devised that quantifies the amount of connection uncertainty based on observed inter-layer correlations in a multiplex network. Moreover, SimBins enhances the prediction quality in the target layer by incorporating the effect of link overlap across layers. Applying SimBins to various datasets from diverse domains, our findings indicate that SimBins outperforms the compared methods (both baseline and state-of-the-art methods) in most instances when predicting links. Furthermore, it is discussed that SimBins imposes minor computational overhead to the base similarity measures making it a potentially fast method, suitable for large-scale multiplex networks.

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