Research map / Graph learning / Graph algorithms and clustering
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use dynamical Bethe-Hessian instead of dynamic adjacency averaging | Algorithm 1 · synthetic D-DCSBM, low label persistence | overlap | Helped |
| use Algorithm 1 instead of belief propagation | community detection · synthetic D-DCSBM, n=5000, T=4 | overlap | Hurt |
| use dynamical Bethe-Hessian instead of static Bethe-Hessian per time step | Algorithm 1 · synthetic D-DCSBM | overlap | Mixed |
| use dynamical Bethe-Hessian instead of dynamical non-backtracking | Algorithm 1 · synthetic D-DCSBM | overlap | Helped |
| use xi = lambda (oracle) instead of xi = lambda_d (ablation) | dynamical Bethe-Hessian · synthetic D-DCSBM | overlap | Helped |
| use Algorithm 1 instead of the non-dynamic method on Primary school | community detection · SocioPatterns Primary school | modularity | Helped |
| use fast approximate implementation (ablation) | Algorithm 1 · synthetic D-DCSBM large T | runtime: 40 minutes -> less than 4 minutes | Helped |
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