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This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM).
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Topological signal processing over simplicial complexes
Barbarossa, S. and Sardellitti, S · 2020
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Networkx: Network analysis with python
Hagberg, A. and Conway, D · 2020
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Random walks on simplicial complexes and the normalized hodge 1-laplacian
Schaub, M. T., Benson, A. R., Horn, P., Lippner, G., and Jadbabaie, A · 2020
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The physics of higher-order interactions in complex systems
Battiston, F., Amico, E., Barrat, A., Bianconi, G., Ferraz de Arruda, G., Franceschiello, B., Iacopini, I., Kéfi, S., Latora, V., Moreno, Y., et al · 2021
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Topological deep learning: Classification neural networks
Hajij, M. and Istvan, K · 2021
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Groupnet: Multiscale hypergraph neural networks for trajectory prediction with relational reasoning
Xu, C., Li, M., Ni, Z., Zhang, Y., and Chen, S · 2022
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Topological signal processing over weighted simplicial complexes
Battiloro, C., Sardellitti, S., Barbarossa, S., and Di Lorenzo, P · 2023
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Topological network traffic compression
Bernárdez, G., Telyatnikov, L., Alarcón, E., Cabellos-Aparicio, A., Barlet-Ros, P., and Liò, P · 2023
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What are higher-order networks?
Bick, C., Gross, E., Harrington, H. A., and Schaub, M. T · 2023
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Topological deep learning: graphs, complexes, sheaves
Bodnar, C · 2023
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Combinatorial complexes: bridging the gap between cell complexes and hypergraphs
Hajij, M., Zamzmi, G., Papamarkou, T., Guzman-Saenz, A., Birdal, T., and Schaub, M. T · 2023
Icml 2023 topological deep learning challenge: design and results
Papillon, M., Hajij, M., Myers, A., Frantzen, F., Zamzmi, G., Jenne, H., Mathe, J., Hoppe, J., Schaub, M., Papamarkou, T., et al · 2023
Later among the works it cites.
A comprehensive survey on delaunay triangulation: Applications, algorithms, and implementations over cpus, gpus, and fpgas
Elshakhs, Y. S., Deliparaschos, K. M., Charalambous, T., Oliva, G., and Zolotas, A · 2024
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Topox: a suite of python packages for machine learning on topological domains
Hajij, M., Papillon, M., Frantzen, F., Agerberg, J., AlJabea, I., Ballester, R., Battiloro, C., Bernárdez, G., Birdal, T., Brent, A., et al · 2024
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Representing edge flows on graphs via sparse cell complexes
Hoppe, J. and Schaub, M. T · 2024
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Position: Topological deep learning is the new frontier for relational learning
Papamarkou, T., Birdal, T., Bronstein, M. M., Carlsson, G. E., Curry, J., Gao, Y., Hajij, M., Kwitt, R., Lio, P., Di Lorenzo, P., et al · 2024
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From latent graph to latent topology inference: differentiable cell complex module
Battiloro, C., Spinelli, I., Telyatnikov, L., Bronstein, M., Scardapane, S., and Di Lorenzo, P
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Weisfeiler and lehman go cellular: Cw networks
Bodnar, C., Frasca, F., Otter, N., Wang, Y., Lio, P., Montufar, G. F., and Bronstein, M
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Weisfeiler and lehman go cellular: Cw networks
Bodnar, C., Frasca, F., Otter, N., Wang, Y., Lio, P., Montufar, G. F., and Bronstein, M
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Weisfeiler and lehman go topological: Message passing simplicial networks
Bodnar, C., Frasca, F., Wang, Y., Otter, N., Montufar, G. F., Lio, P., and Bronstein, M
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Higher-order attention networks
Hajij, M., Zamzmi, G., Papamarkou, T., Miolane, N., Guzmán-Sáenz, A., and Ramamurthy, K. N
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Topological deep learning: Going beyond graph data
Hajij, M., Zamzmi, G., Papamarkou, T., Miolane, N., Guzmán-Sáenz, A., Ramamurthy, K. N., Birdal, T., Dey, T. K., Mukherjee, S., Samaga, S. N., et al
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Topobenchmarkx: A framework for benchmarking topological deep learning
Telyatnikov, L., Bernardez, G., Montagna, M., Vasylenko, P., Zamzmi, G., Hajij, M., Schaub, M. T., Miolane, N., Scardapane, S., and Papamarkou, T · 2024
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