Stacked Community Prediction: A Distributed Stacking-Based Community Extraction Methodology for Large Scale Social Networks

Christos Makris,Georgios Pispirigos

doi:10.3390/bdcc5010014

Abstract

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.

Highlights

In consideration of the exponential proliferation of data along with the substantial demand to conveniently incorporate the internal inference and semantics, information networks have been one of the most prevalent data representations
By presenting each individual data entity as a node and by denoting any kind of association with an interconnection edge, information graphs may formulate any functional system of interacting entities. The usage of this general-purpose abstraction has practically proven beneficial in various scientific sectors such as chemistry, biology, politics, digital marketing, computer science and sociology
Expert identification and mood analysis, to recommendation systems, digital footprint identification and targeted marketing, graph theory lies under numerous social network issues

Summary

Introduction

In consideration of the exponential proliferation of data along with the substantial demand to conveniently incorporate the internal inference and semantics, information networks have been one of the most prevalent data representations. By presenting each individual data entity as a node and by denoting any kind of association with an interconnection edge, information graphs may formulate any functional system of interacting entities. The usage of this general-purpose abstraction has practically proven beneficial in various scientific sectors such as chemistry, biology, politics, digital marketing, computer science and sociology. Information networks can be deemed as the predominant data structure due to their handily compact representation. Expert identification and mood analysis, to recommendation systems, digital footprint identification and targeted marketing, graph theory lies under numerous social network issues. Its inter-cluster density, denoted as dext(C), is calculated as: dext(C)

Methods

Results

Conclusion