Link-Prediction Enhanced Consensus Clustering for Complex Networks
arXiv:1506.01461 · doi:10.1371/journal.pone.0153384
Abstract
Many real networks that are inferred or collected 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 algorithm. The framework then merges their multiple outputs 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.
References in corpus (13)
- Fast unfolding of communities in large networks
- Cooperative Game Theory Approaches for Network Partitioning
- Maps of random walks on complex networks reveal community structure
- Near linear time algorithm to detect community structures in large-scale networks
- Benchmark graphs for testing community detection algorithms
- Resolution limit in community detection
- Detecting the overlapping and hierarchical community structure of complex networks
- Consensus clustering in complex networks
- Effective and Efficient Similarity Index for Link Prediction of Complex Networks
- Analysis of the structure of complex networks at different resolution levels
- Robustness of community structure in networks
- Detecting communities using asymptotical Surprise
- Resampling effects on significance analysis of network clustering and ranking