Link-Prediction Enhanced Consensus Clustering for Complex Networks (Open Access)
University of Michigan Ann Arbor United States
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Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset Facebook friend links will never be complete or the result of sampling one may only have access to a portion of the data. The consequence is that downstream analyses that consume the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7 on artificial data and 17 on ego networks collected from Facebook.
- Information Science
- Statistics and Probability