Presently, due to the extended availability of gigantic information networks and the beneficial application of graph analysis in various scientific fields, the necessity for efficient and highly scalable community detection algorithms has never been more essential. Despite the significant amount of published research, the existing methods-such as the Girvan-Newman, random-walk edge betweenness, vertex centrality, InfoMap, spectral clustering, etc.-have virtually been proven incapable of handling real-life social graphs due to the intrinsic computational restrictions that lead to mediocre performance and poor scalability. The purpose of this article is to introduce a novel, distributed community detection methodology which in accordance with the community prediction concept, leverages the reduced complexity and the decreased variance of the bagging ensemble methods, to unveil the subjacent community hierarchy. The proposed approach has been thoroughly tested, meticulously compared against different classic community detection algorithms, and practically proven exceptionally scalable, eminently efficient, and promisingly accurate in unfolding the underlying community structure.Information 2020, 11, 199 2 of 15 of community detection's field [2,3] is the evaluation of homophily in social networks, expressed as the identification of the underlying community structure.With a wide range of applications-such as recommendation systems, targeted market analysis, viral marketing, social influence analysis, etc.-community detection has been proven significant for revealing the information networks' inner mechanisms and evolution. However, as clearly demonstrated in [3], there is not a universally accepted definition of the community. Nevertheless, by concentrating on social graph's context, a community can intuitively be defined as the group of vertices which are more densely connected with each other (a.k.a., intra-connected) than connected with the rest of the graph (a.k.a., inter-connected). Thus, community detection can alternatively be interpreted as the edges' classification to either intra-connection, which are the edges linking vertices of the same community, or inter-connection that are the edges linking vertices of different communities [3,4].As thoroughly described in [2-10], due to community detection's widespread application, ample research has already been conducted to efficiently unveil the information networks' subjacent community structure. The classic community detection algorithms are originally designed to be generally applied to any information network. These techniques-such as the Girvan-Newman [6] algorithm, edge centrality [7] method, geodesic edge betweenness [2] approach, Kernighan-Lin algorithm [2], Latora-Marchiori [2] algorithm, etc.-are basically recursive methods of high polynomial computational complexity that predominantly leverage an iteratively modified and repetitively calculated set of global network topology metrics, in order to extract the underlying community hierarchy from any possib...