A model for generating temporal networks with dynamic community structure guided by mutual information
2026-07-20T08:52:14Z•3e7edf1207a7580fec602706426bb2cbfdeaae80fcf76c21e393b9530c8581af
ECB MMSRbenchmarkingdegree distributiondynamic community detectionedge-dependenceegonet embeddingsexpectation-maximizationfinancial networksgenerative modelgenetic algorithmgraph neural networkshomophilyhypergraphsmaximum likelihoodmodel generalizationmutual informationnetwork measuresnode churnoscillator networksrole-based clusteringsimulated annealingstability analysissystemic risktemporal networks
What happened
Collection of four recent network-science papers: (1) A generative model for temporal networks that jointly controls evolving community structure and dynamic node sets, using a genetic search guided by a mutual-information similarity to produce controlled splits/merges and node churn and sampling intra-/inter-community edge probabilities for benchmarking dynamic community-detection algorithms. (2) A mechanistic growing-hypergraph model where edge formation depends on prior edges and binary node labels (homophily), yielding tunable assortativity, a power-law degree law, and a likelihood-driven,
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_si
- Record identifier
- 3e7edf1207a7580fec602706426bb2cbfdeaae80fcf76c21e393b9530c8581af
- Enrichment time
- 2026-07-20T08:52:14Z
- AI-assisted enrichment
- Yes
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