Ensemble Clustering for Graphs
arXiv:1809.05578 · doi:10.1007/978-3-030-05411-3_19
Abstract
We propose an ensemble clustering algorithm for graphs (ECG), which is based on the Louvain algorithm and the concept of consensus clustering. We validate our approach by replicating a recently published study comparing graph clustering algorithms over artificial networks, showing that ECG outperforms the leading algorithms from that study. We also illustrate how the ensemble obtained with ECG can be used to quantify the presence of community structure in the graph.
arXiv comment9 pages, 5 figures
References in corpus (20)
- Scikit-learn: Machine Learning in Python
- Fast unfolding of communities in large networks
- The structure and function of complex networks
- Community structure in social and biological networks
- Finding community structure in very large networks
- Finding community structure in networks using the eigenvectors of matrices
- 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
- Community detection algorithms: a comparative analysis
- Statistical Mechanics of Community Detection
- Community detection in networks: A user guide
- Consensus clustering in complex networks
- A Comparative Analysis of Community Detection Algorithms on Artificial Networks
- Tolerating the Community Detection Resolution Limit with Edge Weighting
- Adjusting for Chance Clustering Comparison Measures
- Comparative Evaluation of Community Detection Algorithms: A Topological Approach
- Ensemble Clustering for Graphs
- Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures