Research map / Graph learning / Graph algorithms and clustering
Community Detection on Evolving Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Query clusters with probability proportional to square root of cluster size (vs. proportional to size / random node probing) | evolving stochastic block model clustering algorithm · synthetic SBM evolving graph (10000 nodes) | clustering error | Helped |
| Add queue-based mixture of cluster and node queries (optimal queue algorithm) instead of cluster-only sampling (ablation) | evolving SBM clustering algorithm · synthetic SBM evolving graph (10000 nodes), power-law community sizes c=0,1,2,3 | clustering error | Helped |
| Node-level probing of random nodes vs. the optimal queue strategy as baseline; gap shrinks as community size distribution becomes less skewed (smaller c) | baseline probing strategies · synthetic SBM evolving graph, c=0 to c=3 | clustering error | No clear effect |
| Clustering quality worsens over time during execution of the algorithm | data structure of the queue algorithm · synthetic SBM evolving graph | clustering error | Hurt |
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