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Informatik 5
Information Systems
Prof. Dr. M. Jarke
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Prof. Dr. M. Jarke
RWTH Aachen
Informatik 5
Ahornstr. 55
D-52056 Aachen
Tel +49/241/8021501
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The Signi ficant Effect of Overlapping Community structures Book in Signed Social Networks

Year 2017
ISBN-10 978-3-319-51049-1

Social networks are non-detachable part of modern life. It is improbable that someone is not familiar with Facebook or Twitter. Nowadays people join these platforms and communicate with other members. In social networks, some people are more similar to each other and they form densely connected components named communities. Detection of these tightly connected components have received much attention recently. These components are overlapping in nature. In other words, a member belongs to more than one community. Not only overlapping community structure but also temporality is an intrinsic property of online social networks. Regarding the dynamism, people initiate new connections among each other. When people’s communications is mapped to signed connections then we obtain a network with both positive and negative links. To reliably predict how these interactions evolve, we consider the sign prediction problem. Sign prediction and overlapping community detection have been explored to a certain extent, however, answers to some of research questions are still unknown. For instance, how much is the significance of overlapping members in signed networks? To answer this question, we need a fast and precise overlapping community detection algorithm (OCD) working based on simple network dynamics such as disassortative degree mixing and information diffusion. In this paper, we propose a two-phase approach to discover overlapping communities in signed networks. In the first phase, the algorithm identifies most influential nodes (leaders) in the network. In the second phase, we identify the membership of each node to the leaders using network coordination game. We apply several features to investigate the significance of overlapping members. These features include extra, overlapping and intra. To compute these feature types, we not only apply simple degree ranking but also extend original HITS and PageRank ranking algorithms. We employ the features to the sign prediction problem. Results indicate that overlapping nodes competitively predict signs in comparison to intra and extra nodes.


Prediction and Inference from Social Networks and Social Media.


Published in

Prediction and Inference from Social Networks and Social Media , p. 51-84 ; Springer, Cham .

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