Incremental Community Detection in Social Networks Using Label Propagation Method

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Mohammad Asadi Foad Ghaderi
Abstract	The structure of online social networks such as Facebook is continuously changing. Phenomena such as birth, growth, contraction, split, dissolution, and merging with other communities are issues that occur in the communities of online social networks over time. However, characteristics of the consecutive time slots of these networks depend on each other, and independent investigation of each time slot is not efficient for detecting communities in terms of execution time due to the big size of data in each time slot. In order to detect the changes in communities over time, there is a need for algorithms that can detect communities incrementally with proper precision. In this paper, we propose an unsupervised machine learning algorithm for incremental detection of communities using the label propagation method, called Incremental Speaker-Listener Propagation Algorithm (ISLPA). ISLPA can detect both overlapping and non-overlapping communities incrementally after removing or adding a batch of nodes and edges over time. Execution time and modularity comparison on a subset of Facebook dataset confirm that despite the reduced computational costs, the proposed algorithm has promising performance.
Related Links	https://fruct.org/publications/fruct23/files/Asa.pdf
ISSN	23057254
e-ISSN	23430737
Journal	Proceedings of the XXth Conference of Open Innovations Association FRUCT
Issue Number	23
Volume Number	602
Language	English
Publisher	FRUCT
Publisher Date	2018-01-01
Publisher Place	Finland
Access Restriction	Open
Subject Keyword	Telecommunication Online Social Networks Incremental Community Detection Social Media Analysis Machine Learning Label Propagation
Content Type	Text
Resource Type	Article