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This work introduces TopoBench, an open-source library designed to standardize benchmarking and accelerate research in topological deep learning (TDL).
Topological deep learning: going beyond graph data
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy, Tolga Birdal, Tamal Dey, Soham Mukherjee, Shreyas Samaga, Neal Livesay, Robin Walters, Paul Rosen, and Michael Schaub · 1906
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On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Yu-Te Shen, and Ming Ouhyoung · 2003
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Exploring network structure, dynamics, and function using NetworkX
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
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ZINC: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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Multi-view convolutional neural networks for 3d shape recognition
Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller · 2015
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GUDHI User and Reference Manual
The GUDHI Project · 2015
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3d shapenets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Uci machine learning repository, 2017
Dheeru Dua, Casey Graff, et al · 2017
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My Research Software, 12 2017
Mona Lisa and Hew Bot · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Graph attention networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Social network analysis
David Knoke and Song Yang · 2019
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Scikit-tda: Topological data analysis for python, 2019
Chris Tralie Nathaniel Saul · 2019
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Deep Graph Library: a graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Hydra - a framework for elegantly configuring complex applications
Omry Yadan · 2019
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Hypergcn: A new method for training graph convolutional networks on hypergraphs
Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar · 2019
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Topological signal processing over simplicial complexes
Sergio Barbarossa and Stefania Sardellitti · 2020
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Simplicial 2-complex convolutional neural nets
Eric Bunch, Qian You, Glenn Fung, and Vikas Singh · 2020
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HNHN: hypergraph networks with hyperedge neurons
Yihe Dong, Will Sawin, and Yoshua Bengio · 2020
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Simplicial neural networks
Stefania Ebli, Michaël Defferrard, and Gard Spreemann · 2020
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Cell complex neural networks
Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi · 2020
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Open graph benchmark: datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Residual correlation in graph neural network regression
Junteng Jia and Austion R Benson · 2020
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TUDataset: a collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Karate Club: an API oriented open-source Python framework for unsupervised learning on graphs
Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar · 2020
Topological network traffic compression
Guillermo Bernárdez, Lev Telyatnikov, Eduard Alarcón, Albert Cabellos-Aparicio, Pere Barlet-Ros, and Pietro Liò · 2023
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What are higher-order networks?
Christian Bick, Elizabeth Gross, Heather A Harrington, and Michael T Schaub · 2023
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Topological deep learning: graphs, complexes, sheaves
Cristian Bodnar · 2023
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Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2023
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Cell attention networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa, Paolo Di Lorenzo, Stefania Sardellitti, and Sergio Barbarossa · 2023
Later among the works it cites.
Combinatorial complexes: bridging the gap between cell complexes and hypergraphs
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Aldo Guzman-Saenz, ToIga Birdal, and Michael T Schaub · 2023
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Hypergraph learning with line expansion
Chaoqi Yang, Ruijie Wang, Shuochao Yao, and Tarek Abdelzaher · 2020
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Graph neural networks: A review of methods and applications
Jie Zhou, Guanghui Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2020
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The physics of higher-order interactions in complex systems
Federico Battiston, Enrico Amico, Alain Barrat, Ginestra Bianconi, Guilherme Ferraz de Arruda, Benedetta Franceschiello, Iacopo Iacopini, Sonia Kéfi, Vito Latora, Yamir Moreno, et al · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
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You are allset: a multiset function framework for hypergraph neural networks
Eli Chien, Chao Pan, Jianhao Peng, and Olgica Milenkovic · 2021
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OGB-LSC: a large-scale challenge for machine learning on graphs
Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec · 2021
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XGI: a Python package for higher-order interaction networks
Nicholas W. Landry, Maxime Lucas, Iacopo Iacopini, Giovanni Petri, Alice Schwarze, Alice Patania, and Leo Torres · 2023
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Architectures of topological deep learning: a survey on topological neural networks
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, and Nina Miolane · 2023
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A critical look at the evaluation of GNNs under heterophily: are we really making progress?
Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova · 2023
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Topo-MLP: a simplicial network without message passing
Karthikeyan Natesan Ramamurthy, Aldo Guzmán-Sáenz, and Mustafa Hajij · 2023
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Egg-gae: scalable graph neural networks for tabular data imputation
Lev Telyatnikov and Simone Scardapane · 2023
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Equivariant hypergraph diffusion neural operators
Peihao Wang, Shenghao Yang, Yunyu Liu, Zhangyang Wang, and Pan Li · 2023
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Convolutional learning on simplicial complexes
Maosheng Yang and Elvin Isufi · 2023
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Mantra: The manifold triangulations assemblage
Rubén Ballester, Ernst Röell, Daniel Bin Schmid, Mathieu Alain, Sergio Escalera, Carles Casacuberta, and Bastian Rieck · 2024
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Generalized simplicial attention neural networks
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Icml topological deep learning challenge 2024: Beyond the graph domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol-Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, et al · 2024
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TopoX: a suite of Python packages for machine learning on topological domains
Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, and Nina Miolane · 2024
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Position: Topological deep learning is the new frontier for relational learning
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Amaury Wei and Olga Fink · 2024
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E(n) equivariant topological neural networks
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Ordered topological deep learning: a network modeling case study, 2025
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Hopse: Scalable higher-order positional and structural encoder for combinatorial representations
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Topotune : A framework for generalized combinatorial complex neural networks
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Hypergraph neural networks through the lens of message passing: A common perspective to homophily and architecture design
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