Signed Network Modeling Based on Structural Balance Theory

Abstract

The modeling of networks, specifically generative models, has been shown to provide a plethora of information about the underlying network structures, as well as many other benefits behind their construction. There has been a considerable increase in interest for the better understanding and modeling of networks, and the vast majority of existing work has been for unsigned networks. However, many networks can have positive and negative links (or signed networks), especially in online social media. It is evident from recent work that signed networks present unique properties and principles from unsigned networks due to the added complexity, which pose tremendous challenges on existing unsigned network models. Hence, in this paper, we investigate the problem of modeling signed networks. In particular, we provide a principled approach to capture important properties and principles of signed networks and propose a novel signed network model guided by Structural Balance Theory. Empirical experiments on three real-world signed networks demonstrate the effectiveness of the proposed model.

The Structure of Negative Social Ties in Rural Village Networks

“…Some of these properties, such as triadic closure and degree assortativity (the tendency of people with similar numbers of connections to be connected), have been theorized to play a role in human evolution, as they may affect the ability of groups to coordinate activities or resist epidemics (Fowler, Dawes, and Christakis 2009 ). Recently, researchers have used such structural insights to motivate new algorithms for predicting negative ties and generating signed networks (Derr, Aggarwal, and Tang 2018 ; Wang et al 2018 ).…”

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“…Our technique shows a clear advantage over methods that ignore the importance of the edge's sign. K-fold cross-validation Trust/distrust datasets are unbalanced due to the lack of negative links ( [9]). We validate our solution with K-Fold cross-validation, because this method is useful when data is unbalanced.…”

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