mlsanityData to 7 Oct 2026

The Multivariate Community Hawkes Model for Dependent Relational Events in Continuous-time Networks

arXiv 2205.00639 ↗First posted 2 May 2022stat.ME cs.LG cs.SIcited by 4

Not on the map. Why?

Abstract (arXiv)

The stochastic block model (SBM) is one of the most widely used generative models for network data. Many continuous-time dynamic network models are built upon the same assumption as the SBM: edges or events between all pairs of nodes are conditionally independent given the block or community memberships, which prevents them from reproducing higher-order motifs such as triangles that are commonly observed in real networks. We propose the multivariate community Hawkes (MULCH) model, an extremely flexible community-based model for continuous-time networks that introduces dependence between node pairs using structured multivariate Hawkes processes. We fit the model using a spectral clustering and likelihood-based local refinement procedure. We find that our proposed MULCH model is far more accurate than existing models both for predictive and generative tasks.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 18 of its 59 references that are in the map. First posted on arXiv 2 May 2022.

Builds on

18 of 59 references.

References · 18

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  2. 2016Higher-order organization of complex networkscited by 109
  3. 2016Motifs in Temporal Networkscited by 101
  4. 2013Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodelcited by 45
  5. 2018Lecture Notes: Temporal Point Processes and the Conditional Intensity Functioncited by 40
  6. 2015COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolutioncited by 37
  7. 2016Community Detection in Degree-Corrected Block Modelscited by 36
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  10. 2015Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recoverycited by 15
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  12. 2015A semiparametric extension of the stochastic block model for longitudinal networkscited by 10
6 more
  1. 2017The Block Point Process Model for Continuous-Time Event-Based Dynamic Networkscited by 8
  2. 2021Disentangling homophily, community structure and triadic closure in networkscited by 6
  3. 2018Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction datacited by 5
  4. 2018Higher-Order Spectral Clustering under Superimposed Stochastic Block Modelcited by 2
  5. 2021Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernelscited by 2
  6. 2020Modeling Human Dynamics and Lifestyle Using Digital Tracescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2022A Mutually Exciting Latent Space Hawkes Process Model for Continuous-time Networkscited by 10
  2. 2026Opal: Private Memory for Personal AIcited by 1
  3. 2022Efficient Estimation for Longitudinal Networks via Adaptive Mergingcited by 0
  4. 2025Spectral clustering for dependent community Hawkes process models of temporal networkscited by 0