Improving Community Detection by Mining Social Interactions

Autor:	Leão, Jeancarlo Campos, Brandão, Michele Amaral, de Melo, Pedro O. S. Vaz, Laender, Alberto H. F.
Rok vydání:	2018
Předmět:	Computer Science - Social and Information Networks Physics - Physics and Society
Druh dokumentu:	Working Paper
Popis:	Social relationships can be divided into different classes based on the regularity with which they occur and the similarity among them. Thus, rare and somewhat similar relationships are random and cause noise in a social network, thus hiding the actual structure of the network and preventing an accurate analysis of it. In this context, in this paper we propose a process to handle social network data that exploits temporal features to improve the detection of communities by existing algorithms. By removing random interactions, we observe that social networks converge to a topology with more purely social relationships and more modular communities.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1810.02002 Zobrazit plný text záznamu View this record from Arxiv