Incremental Community Detection in Social Networks Using Label Propagation Method

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.