Integrating Binary Similarity Measures in the Link Prediction Task

2018 ◽  
Author(s):  
Alfredo Milani ◽  
Valentina Franzoni ◽  
Giulio Biondi ◽  
Yuanxi Li
Keyword(s):  
Link Prediction ◽  
Similarity Measures ◽  
Prediction Task ◽  
Binary Similarity 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.

Multivariate Time Series Link Prediction for Evolving Heterogeneous Network

2019 ◽  
Vol 18 (01) ◽  
pp. 241-286 ◽  
Author(s):  
Alper Ozcan ◽  
Sule Gunduz Oguducu
Keyword(s):  
Time Series ◽  
Real World ◽  
Link Prediction ◽  
Multivariate Time Series ◽  
Similarity Measures ◽  
Dynamic Structure ◽  
Social Bookmarking ◽  
Node Connectivity ◽  
Worldwide Web ◽  
Heterogeneous Social Networks

Link prediction is considered as one of the key tasks in various data mining applications for recommendation systems, bioinformatics, security and worldwide web. The majority of previous works in link prediction mainly focus on the homogeneous networks which only consider one type of node and link. However, real-world networks have heterogeneous interactions and complicated dynamic structure, which make link prediction a more challenging task. In this paper, we have studied the problem of link prediction in the dynamic, undirected, weighted/unweighted, heterogeneous social networks which are composed of multiple types of nodes and links that change over time. We propose a novel method, called Multivariate Time Series Link Prediction for evolving heterogeneous networks that incorporate (1) temporal evolution of the network; (2) correlations between link evolution and multi-typed relationships; (3) local and global similarity measures; and (4) node connectivity information. Our proposed method and the previously proposed time series methods are evaluated experimentally on a real-world bibliographic network (DBLP) and a social bookmarking network (Delicious). Experimental results show that the proposed method outperforms the previous methods in terms of AUC measures in different test cases.

scholarly journals Improved binary similarity measures for software modularization

2017 ◽  
Vol 18 (8) ◽  
pp. 1082-1107 ◽  
Author(s):  
Rashid Naseem ◽  
Mustafa Bin Mat Deris ◽  
Onaiza Maqbool ◽  
Jing-peng Li ◽  
Sara Shahzad ◽  
...  
Keyword(s):  
Similarity Measures ◽  
Software Modularization ◽  
Binary 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.

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