Fetching the paper…
Reading the bibliography…
We propose CRaWl, a novel neural network architecture for graph learning.
Random walks, universal traversal sequences, and the complexity of maze problem
R. Aleliunas, R.M. Karp, R.J. Lipton, L. Lovász, and C. Rackoff · 1979
Earlier work this paper cites.
An optimal lower bound on the number of variables for graph identification
J. Cai, M. Fürer, and N. Immerman · 1992
Earlier work this paper cites.
Network motifs: theory and experimental approaches
Uri Alon · 2007
Earlier work this paper cites.
Weisfeiler-Lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
Earlier work this paper cites.
Deep graph kernels
Pinar Yanardag and SVN Vishwanathan · 2015
Earlier work this paper cites.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Earlier work this paper cites.
Reconstructing markov processes from independent and anonymous experiments
Silvio Micali and Zeyuan Allen Zhu · 2016
Earlier work this paper cites.
Learning convolutional neural networks for graphs
M. Niepert, M. Ahmed, and K. Kutzkov · 2016
Earlier work this paper cites.
Xception: Deep learning with depthwise separable convolutions
Francois Chollet · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Learning graph-level representation for drug discovery
Junying Li, Deng Cai, and Xiaofei He · 2017
Earlier work this paper cites.
Motif-based convolutional neural network on graphs
Aravind Sankar, Xinyang Zhang, and Kevin Chen-Chuan Chang · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Anonymous walk embeddings
Sergey Ivanov and Evgeny Burnaev · 2018
Earlier work this paper cites.
Graph attention networks
Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
MoleculeNet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Earlier work this paper cites.
An end-to-end deep learning architecture for graph classification
Muhan Zhang, Zhicheng Cui, Marion Neumann, and Yixin Chen · 2018
Earlier work this paper cites.
Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu · 2019
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Earlier work this paper cites.
Katsuhiko Ishiguro, Shin-ichi Maeda, and Masanori Koyama · 2019
Earlier work this paper cites.
Graph convolutional networks with motif-based attention
John Boaz Lee, Ryan A Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, and Anup Rao · 2019
Cited alongside, same era.
Weisfeiler and Leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Relational pooling for graph representations
Ryan Murphy, Balasubramaniam Srinivasan, Vinayak Rao, and Bruno Ribeiro · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Random features strengthen graph neural networks
Ryoma Sato, Makoto Yamada, and Hisashi Kashima · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
Later among the works it cites.
The surprising power of graph neural networks with random node initialization
Ralph Abboud, İsmail İlkan Ceylan, Martin Grohe, and Thomas Lukasiewicz · 2021
Closest in time.
Graph neural networks with local graph parameters
Pablo Barceló, Floris Geerts, Juan L. Reutter, and Maksimilian Ryschkov · 2021
Closest in time.
Directional graph networks
Dominique Beaini, Saro Passaro, Vincent Létourneau, William L. Hamilton, Gabriele Corso, and Pietro Lió · 2021
Closest in time.
Weisfeiler and lehman go cellular: Cw networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein · 2020
Cited alongside, same era.
Graph convolutions that can finally model local structure
Rémy Brossard, Oriel Frigo, and David Dehaene · 2020
Cited alongside, same era.
Can graph neural networks count substructures?
Zhengdao Chen, Lei Chen, Soledad Villar, and Joan Bruna · 2020
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Velickovic · 2020
Cited alongside, same era.
A generalization of transformer networks to graphs
Vijay Prakash Dwivedi and Xavier Bresson · 2020
Cited alongside, same era.
Benchmarking graph neural networks
Vijay Prakash Dwivedi, Chaitanya K Joshi, Thomas Laurent, Yoshua Bengio, and Xavier Bresson · 2020
Cited alongside, same era.
Hierarchical inter-message passing for learning on molecular graphs
Matthias Fey, Jan-Gin Yuen, and Frank Weichert · 2020
Cited alongside, same era.
Cristian Bodnar, Fabrizio Frasca, Nina Otter, Yuguang Wang, Pietro Liò, Guido F Montufar, and Michael Bronstein · 2021
Closest in time.
Reconstruction for powerful graph representations
Leonardo Cotta, Christopher Morris, and Bruno Ribeiro · 2021
Closest in time.
The logic of graph neural networks
Martin Grohe · 2021
Closest in time.
Wasserstein embedding for graph learning
Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, and Heiko Hoffmann · 2021
Closest in time.
Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
Closest in time.
Inductive representation learning in temporal networks via causal anonymous walks
Yanbang Wang, Yen-Yu Chang, Yunyu Liu, Jure Leskovec, and Pan Li · 2021
Closest in time.
Node2seq: Towards trainable convolutions in graph neural networks
Hao Yuan and Shuiwang Ji · 2021
Closest in time.
Nested graph neural networks
Muhan Zhang and Pan Li · 2021
Closest in time.
Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, Gopinath Balamurugan, Michael M. Bronstein, and Haggai Maron · 2022
Closest in time.
Structure-aware transformer for graph representation learning
Dexiong Chen, Leslie O’Bray, and Karsten Borgwardt · 2022
Closest in time.
Long range graph benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini · 2022
Closest in time.
pathgcn: Learning general graph spatial operators from paths
Moshe Eliasof, Eldad Haber, and Eran Treister · 2022
Closest in time.
Understanding and extending subgraph GNNs by rethinking their symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael M. Bronstein, and Haggai Maron · 2022
Closest in time.
A sparse-motif ensemble graph convolutional network against over-smoothing
Xuan Jiang, Zhiyong Yang, Peisong Wen, Li Su, and Qingming Huang · 2022
Closest in time.
Raw-gnn: Random walk aggregation based graph neural network
Di Jin, Rui Wang, Meng Ge, Dongxiao He, Xiang Li, Wei Lin, and Weixiong Zhang · 2022
Closest in time.
Algorithm and system co-design for efficient subgraph-based graph representation learning
Haoteng Yin, Muhan Zhang, Yanbang Wang, Jianguo Wang, and Pan Li · 2022
Closest in time.
From stars to subgraphs: Uplifting any GNN with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2022
Closest in time.