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Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features.
St-unet: A spatio-temporal u-network for graph-structured time series modeling
Bowen Yu, Haonan Yin, and Zhanxing Zhu · 1903
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The truly deep graph convolutional networks for node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 1907
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Robust estimation of a location parameter
Peter J. Huber · 1964
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The reduction of a graph to canonical form and the algebra which appears therein
B. Weisfeiler and A. Leman · 1968
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Recent developments in hodge theory: a discussion of techniques and results
Phillip Griffiths and Wilfried Schmid · 1975
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Data formats for gis: A survey of popular systems, 1992
Daniel Ayers · 1992
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Multiscale statistical models for hierarchical spatial aggregation
Eric D. Kolaczyk and Haiying Huang · 2001
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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Discrete calculus: Applied analysis on graphs for computational science
L. J. Grady and J. R. Polimeni · 2010
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Least squares ranking on graphs, Hodge Laplacians, time optimality, and iterative methods
A. N. Hirani, K. Kalyanaraman, and S. Watts · 2010
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Spectral networks and locally connected networks on graphs
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun · 2014
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The relationship between aristotelian and hasse diagrams
Lorenz Demey and Hans Smessaert · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2014
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Unveiling patterns of international communities in a global city using mobile phone data
Paolo Bajardi, Matteo Delfino, André Panisson, Giovanni Petri, and Michele Tizzoni · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Two’s company, three (or more) is a simplex
C. Giusti, R. Ghrist, and D. S. Bassett · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The topological 'shape' of brexit
Bernadette Stolz, Heather Harrington, and Mason Alexander Porter · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
T. N. Kipf and M. Welling · 2017
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The shape of collaborations
A. Patania, G. Petri, and F. Vaccarino · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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A topological representation of branching neuronal morphologies
L. Kanari, P. Dłotko, M. Scolamiero, R. Levi, J. Shillcock, K. Hess, and H. Markram · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Modeling the spread of the zika virus using topological data analysis
Derek Lo and Briton Park · 2018
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Network geometry and complexity
D. Mulder and G. Bianconi · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William Hamilton, and Jure Leskovec · 2018
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Link prediction based on graph neural networks
M. Zhang and Y. Chen · 2018
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Cormorant: covariant molecular neural networks
Brandon Anderson, Truong-Son Hy, and Risi Kondor · 2019
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A general theory of equivariant cnns on homogeneous spaces
Taco S Cohen, Mario Geiger, and Maurice Weiler · 2019
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An ensemble-based model of pm2. 5 concentration across the contiguous united states with high spatiotemporal resolution
Qian Di, Heresh Amini, Liuhua Shi, Itai Kloog, Rachel Silvern, James Kelly, M Benjamin Sabath, Christine Choirat, Petros Koutrakis, Alexei Lyapustin, et al · 2019
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Towards graph pooling by edge contraction
Tobias Diehl, Thomas Brunner, Marco Tiezzi Le, and Alois Knoll · 2019
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Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
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Toward a spectral theory of cellular sheaves
J. Hansen and R. Ghrist · 2019
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Equivariant flows: sampling configurations for multi-body systems with symmetric energies
Jonas Köhler, Leon Klein, and Frank Noé · 2019
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Self-attention graph pooling
Junhyun Lee, Inyeong Lee, and Jaewoo Kang · 2019
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Deep hierarchical graph convolution for election prediction from geospatial census data
Ming Li, Emmanuel Perrier, and Chengbin Xu · 2019
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How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
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Heterogeneous graph neural network
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla · 2019
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Topological signal processing over simplicial complexes
S. Barbarossa and S. Sardellitti · 2020
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Simplicial neural networks
S. Ebli, M. Defferrard, and G. Spreemann · 2020
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data
Marc Finzi, Samuel Stanton, Pavel Izmailov, and Andrew Gordon Wilson · 2020
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Se (3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2020
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Cell complex neural networks
Mustafa Hajij, Kyle Istvan, and Ghada Zamzmi · 2020
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Efficient representation learning for higher-order data with simplicial complexes
Ruochen Yang, Frederic Sala, and Paul Bogdan · 2022
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Gaussian processes on cellular complexes
Mathieu Alain, So Takao, Brooks Paige, and Marc Peter Deisenroth · 2023
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Tangent bundle filters and neural networks: From manifolds to cellular sheaves and back
C. Battiloro, Z. Wang, H. Riess, P. Di Lorenzo, and A. Ribeiro · 2023
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Topological signal processing over weighted simplicial complexes
Claudio Battiloro, Stefania Sardellitti, Sergio Barbarossa, and Paolo Di Lorenzo · 2023
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Topological Deep Learning: Graphs, Complexes, Sheaves
Cristian Bodnar · 2023
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Sheaf neural networks, 2020
Jakob Hansen and Thomas Gebhart · 2020
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Hodge Laplacians on graphs
L. Lim · 2020
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Multi-view graph neural networks for molecular property prediction, 2020
Hehuan Ma, Yatao Bian, Yu Rong, Wenbing Huang, Tingyang Xu, Weiyang Xie, Geyan Ye, and Junzhou Huang · 2020
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Incompleteness of atomic structure representations
Sergey N Pozdnyakov, Michael J Willatt, Albert P Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti · 2020
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Graph neural networks in particle physics
Jonathan Shlomi, Peter Battaglia, and Jean-Roch Vlimant · 2020
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Learning effective road network representation with hierarchical graph neural networks
Ning Wu, Xiaowen Wu, Jingyuan Wang, and Daojing Pan · 2020
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Junwu Chen and Philippe Schwaller · 2023
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On over-squashing in message passing neural networks: The impact of width, depth, and topology
Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael M Bronstein · 2023
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E ( n ) (n) equivariant message passing simplicial networks
Floor Eijkelboom, Rob Hesselink, and Erik J Bekkers · 2023
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Cell attention networks
Lorenzo Giusti, Claudio Battiloro, Lucia Testa, Paolo Di Lorenzo, Stefania Sardellitti, and Sergio Barbarossa · 2023
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A spatial–temporal hypergraph based method for service recommendation in the mobile internet of things-enabled service platform
Zhixuan Jia, Yushun Fan, Chunyu Wei, and Ruyu Yan · 2023
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Expressivity-preserving GNN simulation
Fabian Jogl, Maximilian Thiessen, and Thomas Gärtner · 2023
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On the expressive power of geometric graph neural networks
Chaitanya K Joshi, Cristian Bodnar, Simon V Mathis, Taco Cohen, and Pietro Lio · 2023
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Architectures of topological deep learning: A survey on topological neural networks, 2023
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, and Nina Miolane · 2023
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Hyperlocal environmental data with a mobile platform in urban environments
An Wang, Simone Mora, Yuki Machida, Priyanka deSouza, Sanjana Paul, Oluwatobi Oyinlola, Fábio Duarte, and Carlo Ratti · 2023
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Long-term mortality burden trends attributed to black carbon and pm2· 5 from wildfire emissions across the continental usa from 2000 to 2020: a deep learning modelling study
Jing Wei, Jun Wang, Zhanqing Li, Shobha Kondragunta, Susan Anenberg, Yi Wang, Huanxin Zhang, David Diner, Jenny Hand, Alexei Lyapustin, et al · 2023
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Equivariant and Coordinate Independent Convolutional Networks
Maurice Weiler, Patrick Forré, Erik Verlinde, and Max Welling · 2023
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Convolutional learning on simplicial complexes, 2023
Maosheng Yang and Elvin Isufi · 2023
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Protein representation learning by geometric structure pretraining
Zuobai Zhang, Minghao Xu, Arian Rokkum Jamasb, Vijil Chenthamarakshan, Aurelie Lozano, Payel Das, and Jian Tang · 2023
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Signal Processing and Learning over Topological Spaces
Claudio Battiloro · 2024
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Osmnx: A python package to work with openstreetmap data
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Geometric algebra transformer
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SaNN: Simple yet powerful simplicial-aware neural networks
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Path complex neural network for molecular property prediction
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Clifford group equivariant simplicial message passing networks
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Primary land use tax lot output (pluto) data
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Average daily traffic (aadt) data
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Position paper: Challenges and opportunities in topological deep learning
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