# A Distributed Hybrid Community Detection Methodology for Social Networks
## 1 Introduction
It is worth mentioning that due to community detection's extensive application in diverse research fields, such as physics and discrete mathematics, many alternatives influenced by different scientific backgrounds have also been published with the most impressive among them engaging linear algebra techniques [1; 4], Markov chains theory [1] or elementary circuit analysis theory [1]. Due to the great diversity of the proposed approaches, the introduction of a general, well interpretable and unanimously agreed criterion, responsible for evaluating the quality of the generated community structure has been more than fundamental in any case. Hence, the modularity function [8] has primarily been defined as the quality measure for assessing the generated community structure [1] and has secondly served as the stopping criterion in the repetitive process cases. Among the profuse amount of modularity measures defined, the one introduced by [PERSON] and [PERSON] [8] is broadly considered as the state-of-the-art in community detection and unquestionably its performance serves as standard. Along with the network structure oriented approaches, there is plenty of research that enhance the above-mentioned similarity measures with different information modalities, such as the node associated attributes intrinsically included. In particular, based on the homophily concept, which is the natural human tendency for interacting with peers of similar interests and characteristics [9], it is more than obvious that a valuable community should be the group of associated vertices that besides being more densely intra-connected, should also have various distinctive node attributes in common [3]. Thus, plentiful hybrid methods [3; 7; 10; 11; 12; 13; 14; 15; 16; 17] have been published to uncover communities, leveraging not only the vertices associations but also the inherent node attribute information. Nevertheless, as it is already proven in [18], there is no algorithm that can be universally applied and optimally export the subjacent network structure for any possible real-world network, since on one hand, the classic community detection methods and algorithms are not only of high polynomial degree in the size of vertices and edges but also substantially unscalable, while on the other hand, the hybrid approaches obligingly require superlative statistical network analysis. Thereafter, the application on real-world data networks of the existing methods, both classic and hybrid, has practically proven infeasible. Albeit the obvious over-demanding resources requirements in both cases, the outcome of the classic approaches might also be considered comparably inaccurate, since in the case of social media graphs, the network topology-oriented methods deliberately neglect critical modalities of information inherently included, such as the user content information. Therefore, the introduction of a novel, distributed community detection methodology that combines the topological properties with the integral user profile information seems to be assuredly prosperous. Specifically, 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 remainder of this manuscript is organized as follows:  
* Section 2 presents the existing community detection bibliography;
* Section 3 comprehensively analyzes the proposed methodology and its implementation;
* Section 4 describes the experimentation process and assess its outcome; and
* Section 5 outline the conclusions and propose the future goals of this research work.