Node Classification in Social Network by Distributed Learning Automata
Journal Title: Journal of Information Systems and Telecommunication - Year 2017, Vol 5, Issue 2
Abstract
The aim of this article is improving the accuracy of node classification in social network using Distributed Learning Automata (DLA). In the proposed algorithm using a local similarity measure, new relations between nodes are created, then the supposed graph is partitioned according to the labeled nodes and a network of Distributed Learning Automata is corresponded on each partition. In each partition the maximal spanning tree is determined using DLA. Finally nodes are labeled according to the rewards of DLA. We have tested this algorithm on three real social network datasets, and results show that the expected accuracy of presented algorithm is achieved.
Authors and Affiliations
Ahmad Rahnamazadeh, Mohammad Reza Meybodi, Masoud Taheri Kadkhoda
Keywords
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