Abstract
Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.
Highlights
In recent years, due to internet’s universal spread and social media’s extensive use, the amount of disposable information continuously grows in an exponential rate
In the proposed approach the network structure, expressed by the loose similarity and the local edge betweenness that is a properly modified, in terms of scalability and efficiency, Girvan–Newman edge betweenness measure, along with the user profile information in the form of node attribute vectors are ably combined to unveil the subjacent community structure. This is the actual purpose of this work, for which the experimentation with various real-world social networks workably verified that the proposed methodology is profoundly promising, extremely scalable and adequately efficient
The major drawbacks of those approaches are on one hand, the heavy computational demands that require the repetitive recalculation of a global similarity measure, which can implicitly be interpreted as the repetitive traversing over the entire network for each vertex calculation, and on the other hand the inaccurate generated hierarchy structure due to the lack of social information context consideration, in the case of network topology oriented approaches
Summary
Due to internet’s universal spread and social media’s extensive use, the amount of disposable information continuously grows in an exponential rate. In the proposed approach the network structure, expressed by the loose similarity and the local edge betweenness that is a properly modified, in terms of scalability and efficiency, Girvan–Newman edge betweenness measure, along with the user profile information in the form of node attribute vectors are ably combined to unveil the subjacent community structure. This is the actual purpose of this work, for which the experimentation with various real-world social networks workably verified that the proposed methodology is profoundly promising, extremely scalable and adequately efficient.
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