Abstract:
Data analysis involves community detection. Algorithms to solve this problem have been proposed for decades. Most community detection research ignores statistical significance. Although statistically significant communities have been mined, deriving an analytical solution of p-value for one community under the configuration model is still a difficult task.
The configuration model is a popular random graph model for community detection that preserves each node’s degree. To partially fill this void, we present a tight upper bound on the p-value of a single community under the configuration model, which can be used to quantify the statistical significance of each community analytically.
We present an iterative local search method to find statistically significant communities. Experimental results show that our method detects statistically significant communities similarly to competitors.
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