Abstract
There are many community detection algorithms for discovering communities in networks, but very few deal with networks that change structure. The SCAN (Structural Clustering Algorithm for Networks) algorithm is one of these algorithms that detect communities in static networks. To make SCAN more effective for the dynamic social networks that are continually changing their structure, we propose the algorithm DSCAN (Dynamic SCAN) which improves SCAN to allow it to update a local structure in less time than it would to run SCAN on the entire network. We also improve SCAN by removing the need for parameter tuning. DSCAN, tested on real world dynamic networks, performs faster and comparably to SCAN from one timestamp to another, relative to the size of the change. We also devised an approach to genetic algorithms for detecting communities in dynamic social networks, which performs well in speed and modularity.
Highlights
Social networks, such as Facebook and Twitter, have been rapidly growing in recent years
We have proposed two algorithms to handle community detection in dynamic social networks, DSCAN and GAD
We have shown that DSCAN performs community detection on dynamic networks almost to what SCAN would produce by doing the entire network
Summary
Social networks, such as Facebook and Twitter, have been rapidly growing in recent years Such a network can be represented as a graph, where a node represents a user and an edge represents their affiliation with others. Community detection in dynamic networks involves the process of incorporating the community model of a previous timestamp, or snapshot of a network structure, into the detection of the next to improve the efficiency of detecting the new community structure Another aspect about community structure is that many community detection algorithms discover the best possible community membership for each node in the network, but some nodes are too distant from all other nodes and should be considered outliers.
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