Title: Community detection in social networks using logic-based probabilistic programming

Authors: Ahmed Ibrahem Hafez; Eiman Tamah Al-Shammari; Aboul Ella Hassanien; Aly A. Fahmy

Addresses: CS Department, Faculty of Computer and Information, Minia University, Main Road, Shalaby Land, Menia, Minia, Postal Code 61519, Egypt ' Faculty of Computing Science and Engineering, Kuwait University, Al-Adailiya, Library Building, 1st Floor, P.O. Box: 5969, Safat 13060, Kuwait ' Faculty of Computers and Information, Cairo University, 5 Ahmed Zewal St., Orman, Giza, Postal Code 12613, Egypt ' Faculty of Computers and Information, Cairo University, 5 Ahmed Zewal St., Orman, Giza, Postal Code 12613, Egypt

Abstract: Community detection in complex networks has attracted a lot of attention in recent years. Communities play special roles in the structure-function relationship; therefore, detecting communities can be a way to identify substructures that could correspond to important functions. Social networks can be formalised by a generative process in which interactions between actors are generated based on some assumptions, i.e., a model with some parameters. Based on that idea, a probabilistic inference technique can be used to infer the community structure of the network. We propose a generative model to describe how network interactions are generated and show the use of a logic-based probabilistic modelling technique such as PRISM, to solve the community detection problem. The proposed model works well with directed and undirected networks, and with weighted and un-weighted networks. We use the deterministic annealing expectation maximisation algorithm in the learning process, which prove to yield a very promising result when is applied to the community detection problem.

Keywords: social networks; probabilistic modelling; community detection; network communities; community structure inference; logic.

DOI: 10.1504/IJSNM.2015.072303

International Journal of Social Network Mining, 2015 Vol.2 No.2, pp.158 - 172

Accepted: 18 Jun 2015
Published online: 08 Oct 2015 *

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