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Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian

arXiv 1807.05044 ↗First posted 13 Jul 2018cs.SI cs.DM math.ATcited by 84

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Abstract (arXiv)

Focusing on coupling between edges, we generalize the relationship between the normalized graph Laplacian and random walks on graphs by devising an appropriate normalization for the Hodge Laplacian -- the generalization of the graph Laplacian for simplicial complexes -- and relate this to a random walk on edges. Importantly, these random walks are intimately connected to the topology of the simplicial complex, just as random walks on graphs are related to the topology of the graph. This serves as a foundational step towards incorporating Laplacian-based analytics for higher-order interactions. We demonstrate how to use these dynamics for data analytics that extract information about the edge-space of a simplicial complex that complements and extends graph-based analysis. Specifically, we use our normalized Hodge Laplacian to derive spectral embeddings for examining trajectory data of ocean drifters near Madagascar and also develop a generalization of personalized PageRank for the edge-space of simplicial complexes to analyze a book co-purchasing dataset.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2018.

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Citing papers per year: 2021: 15; 2022: 7; 2023: 19; 2024: 14; 2025: 12; 2026: 17.

84 papers.

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  1. 2021Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networkscited by 219
  2. 2021Weisfeiler and Lehman Go Cellular: CW Networkscited by 218
  3. 2021Automatic Symmetry Discovery with Lie Algebra Convolutional Networkcited by 82
  4. 2021Signal Processing on Higher-Order Networks: Livin' on the Edge ... and Beyondcited by 81
  5. 2021Principled Simplicial Neural Networks for Trajectory Predictioncited by 77
  6. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  7. 2023Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatmentcited by 47
  8. 2023Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networkscited by 43
  9. 2022Simplicial Attention Networkscited by 40
  10. 2021Signal Processing on Cell Complexescited by 31
  11. 2023Generalized Simplicial Attention Neural Networkscited by 27
  12. 2023Convolutional Learning on Simplicial Complexescited by 26
38 more
  1. 2024TopoX: A Suite of Python Packages for Machine Learning on Topological Domainscited by 22
  2. 2021BScNets: Block Simplicial Complex Neural Networkscited by 21
  3. 2022Simplicial Attention Neural Networkscited by 21
  4. 2024Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structurescited by 20
  5. 2021A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinationscited by 18
  6. 2022Simplicial Convolutional Filterscited by 18
  7. 2021Simplicial Complex Representation Learningcited by 17
  8. 2023Simplicial Hopfield networkscited by 16
  9. 2023Hodge-Compositional Edge Gaussian Processescited by 16
  10. 2021Finite Impulse Response Filters for Simplicial Complexescited by 15
  11. 2023Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial Complexescited by 15
  12. 2023Facilitating Graph Neural Networks with Random Walk on Simplicial Complexescited by 14
  13. 2022Inference of hyperedges and overlapping communities in hypergraphscited by 13
  14. 2021Signal processing on simplicial complexescited by 12
  15. 2024Attending to Topological Spaces: The Cellular Transformercited by 11
  16. 2023Weisfeiler and Lehman Go Paths: Learning Topological Features via Path Complexescited by 10
  17. 2023Dirac signal processing of higher-order topological signalscited by 9
  18. 2021The decomposition of the higher-order homology embedding constructed from the k-Laplaciancited by 8
  19. 2021Data-Centric AI Requires Rethinking Data Notioncited by 8
  20. 2021Helmholtzian Eigenmap: Topological feature discovery & edge flow learning from point cloud datacited by 7
  21. 2023ICML 2023 Topological Deep Learning Challenge : Design and Resultscited by 7
  22. 2023Topo-MLP : A Simplicial Network Without Message Passingcited by 7
  23. 2022Pooling Strategies for Simplicial Convolutional Networkscited by 5
  24. 2023Topological Point Cloud Clusteringcited by 5
  25. 2023Representing Edge Flows on Graphs via Sparse Cell Complexescited by 5
  26. 2021Outlier Detection for Trajectories via Flow-embeddingscited by 4
  27. 2024Point-Level Topological Representation Learning on Point Cloudscited by 4
  28. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  29. 2025Topological Dictionary Learningcited by 4
  30. 2025Topological Schr"odinger Bridge Matchingcited by 4
  31. 2023Interaction Measures, Partition Lattices and Kernel Tests for High-Order Interactionscited by 3
  32. 2024Binarized Simplicial Convolutional Neural Networkscited by 3
  33. 2024FTF-ER: Feature-Topology Fusion-Based Experience Replay Method for Continual Graph Learningcited by 3
  34. 2025Matched Topological Subspace Detectorcited by 3
  35. 2022Stable and Transferable Hyper-Graph Neural Networkscited by 2
  36. 2025HiPoNet: A Multi-View Simplicial Complex Network for High Dimensional Point-Cloud and Single-Cell Datacited by 2
  37. 2025Continuous Simplicial Neural Networkscited by 2
  38. 2026Topological Neural Operatorscited by 2

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