mlsanityData to 7 Oct 2026

Networks beyond pairwise interactions: structure and dynamics

arXiv 2006.01764 ↗First posted 2 Jun 2020physics.soc-ph cond-mat.dis-nn cs.SIcited by 127

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Abstract (arXiv)

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a great variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, in face-to-face human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes and cannot be simply described just in terms of simple dyads. Until recently, little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can greatly enhance our modeling capacities and help us to understand and predict their emerging dynamical behaviors. Here, we present a complete overview of the emerging field of networks beyond pairwise interactions. We first discuss the methods to represent higher-order interactions and give a unified presentation of the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed in the literature to generate synthetic structures, such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We introduce and discuss the rapidly growing research on higher-order dynamical systems and on dynamical topology. We focus on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond pairwise interactions. We elucidate the relations between higher-order topology and dynamical properties, and conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers.

Key facts. Cited by 127 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2020.

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Citing papers per year: 2021: 20; 2022: 10; 2023: 26; 2024: 19; 2025: 22; 2026: 30.

127 papers.

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  1. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  2. 2024Position: Graph Foundation Models are Already Herecited by 84
  3. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  4. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  5. 2021Generative hypergraph clustering: from blockmodels to modularitycited by 48
  6. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  7. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  8. 2021Signal Processing on Cell Complexescited by 31
  9. 2022High-Order Pooling for Graph Neural Networks with Tensor Decompositioncited by 24
  10. 2022Heterogeneous manifolds for curvature-aware graph embeddingcited by 23
  11. 2024A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guidecited by 22
  12. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 18
38 more
  1. 2023Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexescited by 15
  2. 2023Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphscited by 15
  3. 2023Curvature-based Clustering on Graphscited by 14
  4. 2022Inference of hyperedges and overlapping communities in hypergraphscited by 13
  5. 2021Signal processing on simplicial complexescited by 12
  6. 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
  7. 2021A nonlinear diffusion method for semi-supervised learning on hypergraphscited by 11
  8. 2024Masked Graph Autoencoder with Non-discrete Bandwidthscited by 11
  9. 2024Attending to Topological Spaces: The Cellular Transformercited by 11
  10. 2025Copresheaf Topological Neural Networks: A Generalized Deep Learning Frameworkcited by 11
  11. 2023HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformerscited by 10
  12. 2023Learning High-Order Relationships of Brain Regionscited by 10
  13. 2024AI-enhanced Collective Intelligencecited by 10
  14. 2022Hypergraph Artificial Benchmark for Community Detection (h-ABCD)cited by 9
  15. 2023Dirac signal processing of higher-order topological signalscited by 9
  16. 2021Recommender systems based on graph embedding techniques: A comprehensive reviewcited by 8
  17. 2023Learning Harmonic Molecular Representations on Riemannian Manifoldcited by 8
  18. 2021Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Modelcited by 7
  19. 2023Structure and inference in hypergraphs with node attributescited by 7
  20. 2021Disentangling homophily, community structure and triadic closure in networkscited by 6
  21. 2025HOG-Diff: Higher-Order Guided Diffusion for Graph Generationcited by 6
  22. 2021Nonparametric Modeling of Higher-Order Interactions via Hypergraphonscited by 4
  23. 2021Gaussian Processes on Hypergraphscited by 4
  24. 2022Nonbacktracking spectral clustering of nonuniform hypergraphscited by 4
  25. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  26. 2021Randomized Algorithms for Scientific Computing (RASC)cited by 3
  27. 2022Hypergraphon Mean Field Gamescited by 3
  28. 2022Deep Ensembles for Graphs with Higher-order Dependenciescited by 3
  29. 2023Kernel-based Joint Independence Tests for Multivariate Stationary and Non-stationary Time Seriescited by 3
  30. 2023Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactionscited by 3
  31. 2023Learning the effective order of a hypergraph dynamical systemcited by 3
  32. 2024Modularity Based Community Detection in Hypergraphscited by 3
  33. 2024IsUMap: Manifold Learning and Data Visualization leveraging Vietoris-Rips filtrationscited by 3
  34. 2024Information-Theoretic Measures on Lattices for High-Order Interactionscited by 3
  35. 2024Imputation of Time-varying Edge Flows in Graphs by Multilinear Kernel Regression and Manifold Learningcited by 3
  36. 2025Non-adaptive Learning of Random Hypergraphs with Queriescited by 3
  37. 2025Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networkscited by 3
  38. 2026Modeling Higher-Order Brain Interactions via a Multi-View Information Bottleneck Framework for fMRI-based Psychiatric Diagnosiscited by 3

50 most cited of 127 All 127 (JSON)