Authors:
Andreas Kanavos
;
Georgios Drakopoulos
and
Athanasios Tsakalidis
Affiliation:
University of Patras, Greece
Keyword(s):
CNM Algorithm, Community Discovery, Graph Databases, Graph Mining, Graph Signal Processing, Louvain Algorithm, Newman-Girvan Algorithm, Neo4j, Regularization, Walktrap Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Internet Technology
;
Web Information Systems and Technologies
;
Web Services and Web Engineering
Abstract:
Community discovery is central to social network analysis as it provides a natural way for decomposing a social graph to smaller ones based on the interactions among individuals. Communities do not need to be disjoint and often exhibit recursive structure. The latter has been established as a distinctive characteristic of large social graphs, indicating a modularity in the way humans build societies. This paper presents the implementation of four established community discovery algorithms in the form of Neo4j higher order analytics with the Twitter4j Java API and their application to two real Twitter graphs with diverse structural properties. In order to evaluate the results obtained from each algorithm a regularization-like metric, balancing the global and local graph self-similarity akin to the way it is done in signal processing, is proposed.