mlsanityData to 7 Oct 2026

Higher-order Network Analysis Takes Off, Fueled by Classical Ideas and New Data

arXiv 2103.05031 ↗First posted 8 Mar 2021cs.SI physics.soc-phcited by 9

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Abstract (arXiv)

Higher-order network analysis uses the ideas of hypergraphs, simplicial complexes, multilinear and tensor algebra, and more, to study complex systems. These are by now well established mathematical abstractions. What's new is that the ideas can be tested and refined on the type of large-scale data arising in today's digital world. This research area therefore is making an impact across many applications. Here, we provide a brief history, guide, and survey.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2021.

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 2; 2025: 1.

9 papers.

  1. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  2. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  3. 2021Neural Predicting Higher-order Patterns in Temporal Networkscited by 13
  4. 2022De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphscited by 10
  5. 2022Hypergraph Artificial Benchmark for Community Detection (h-ABCD)cited by 9
  6. 2023MPool: Motif-Based Graph Poolingcited by 3
  7. 2024Modularity Based Community Detection in Hypergraphscited by 3
  8. 2021Hypergraph Dissimilarity Measurescited by 1
  9. 2025Enhancing the Utility of Higher-Order Information in Relational Learningcited by 0