Abstract
Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, LabelRankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping communities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network.
Highlights
In the current world we live, interactions between individuals have become far easier than before due to online media
LabelRank, uses the idea of label propagation for its community detection, but instead of propagating labels at random, LabelRank uses the probability that a node will receive a label from a neighbor, which removes the randomness that is seen in Label Propagation Algorithm (LPA) and Speaker-Listener Label Propagation Algorithm (SLPA)
To test SLPA Dynamic (SLPAD), we ran it on the aforementioned networks along with SLPA, LabelRankT, and Dynamic Structural Clustering Algorithm for Networks Overlapping (DSCAN-O)
Summary
In the current world we live, interactions between individuals have become far easier than before due to online media Massive social networks, such as Facebook, Twitter, and LinkedIn, have allowed individuals to connect with one another on a vast level. Communities can take on several different characteristics, ranging from small, such as a close-knit group of friends on Facebook, to large, such as millions of individuals following an international star on Twitter, as well as being able to include several different types of people within the community. Another important aspect of these communities is their ability to overlap.
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