Affiliations: School of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
Correspondence:
[*]
Corresponding author: Mohammad Fathian, School of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran. Tel.: +98 2173225048; Fax: +98 2173225098; E-mail:fathian@iust.ac.ir
Abstract: Online Social Network (OSN) users generate massive amounts of information by
their online interactions, by publishing profiles and posting content.
Detection and analysis of the dense sub-structures of networks, called
communities could facilitate a comprehensive understanding of OSNs. This
presents the challenge of formulating appropriate means to evaluate and
validate each detected community. Most researchers have tackled this issue
by comparing results obtained from community detection algorithms with
information on available social grouping as a ground-truth. However, social
grouping does not guarantee formation or existence of an experienced sense
of community, based on the community-oriented behavior patterns of its
users. This study presents a new scoring function that targets the behavior
of nodes in order to validate detected communities. Indeed, we employ this
function as a Cluster Validity Index (CVI) for evaluating detected
communities. Then, performance of the proposed CVI was compared with other
known functions by ranking in terms of several goodness metrics, on a
variety of homogeneous networks. This study also presents an enhanced
version of the CVI to evaluate communities efficiently in heterogeneous
networks. A number of experiments have been provided to demonstrate the
effectiveness and reliability of the proposed CVI for heterogeneous
networks.
Keywords: Community detection, community-oriented behavior, online social networks, scoring function, validity index, heterogeneous networks
