{"list":"other","total":51,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1904.00962","title":"Large Batch Optimization for Deep Learning: Training BERT in 76 minutes","created":"2019-04-01","cited_by_count":674},{"id":"2004.05718","title":"Principal Neighbourhood Aggregation for Graph Nets","created":"2020-04-12","cited_by_count":478},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"1812.09902","title":"Invariant and Equivariant Graph Networks","created":"2018-12-24","cited_by_count":346},{"id":"2006.09252","title":"Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting","created":"2020-06-16","cited_by_count":313},{"id":"2002.04025","title":"Can Graph Neural Networks Count Substructures?","created":"2020-02-10","cited_by_count":236},{"id":"2103.03212","title":"Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks","created":"2021-03-04","cited_by_count":221},{"id":"2002.06157","title":"Generalization and Representational Limits of Graph Neural Networks","created":"2020-02-14","cited_by_count":218},{"id":"1907.03199","title":"What graph neural networks cannot learn: depth vs width","created":"2019-07-06","cited_by_count":201},{"id":"2010.01179","title":"The Surprising Power of Graph Neural Networks with Random Node Initialization","created":"2020-10-02","cited_by_count":198},{"id":"2101.10320","title":"Identity-aware Graph Neural Networks","created":"2021-01-25","cited_by_count":198},{"id":"2002.03155","title":"Random Features Strengthen Graph Neural Networks","created":"2020-02-08","cited_by_count":186},{"id":"2202.13013","title":"Sign and Basis Invariant Networks for Spectral Graph Representation Learning","created":"2022-02-25","cited_by_count":179},{"id":"1905.12560","title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","created":"2019-05-29","cited_by_count":172},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"2110.03753","title":"From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness","created":"2021-10-07","cited_by_count":155},{"id":"2110.02910","title":"Equivariant Subgraph Aggregation Networks","created":"2021-10-06","cited_by_count":154},{"id":"1903.02541","title":"Relational Pooling for Graph Representations","created":"2019-03-06","cited_by_count":150},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122},{"id":"2301.09505","title":"Rethinking the Expressive Power of GNNs via Graph Biconnectivity","created":"2023-01-23","cited_by_count":119},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112},{"id":"2106.04319","title":"Breaking the Limits of Message Passing Graph Neural Networks","created":"2021-06-08","cited_by_count":101},{"id":"2006.15646","title":"Expressive Power of Invariant and Equivariant Graph Neural Networks","created":"2020-06-28","cited_by_count":100},{"id":"1905.10261","title":"Approximation Ratios of Graph Neural Networks for Combinatorial Problems","created":"2019-05-24","cited_by_count":69},{"id":"2110.00577","title":"Reconstruction for Powerful Graph Representations","created":"2021-10-01","cited_by_count":65},{"id":"2204.04661","title":"Expressiveness and Approximation Properties of Graph Neural Networks","created":"2022-04-10","cited_by_count":64},{"id":"1811.04801","title":"On Weisfeiler-Leman Invariance: Subgraph Counts and Related Graph Properties","created":"2018-11-09","cited_by_count":63},{"id":"2006.12179","title":"Hierarchical Inter-Message Passing for Learning on Molecular Graphs","created":"2020-06-22","cited_by_count":59},{"id":"2206.11168","title":"Ordered Subgraph Aggregation Networks","created":"2022-06-22","cited_by_count":57},{"id":"2106.06707","title":"Graph Neural Networks with Local Graph Parameters","created":"2021-06-12","cited_by_count":52},{"id":"1910.02421","title":"On Universal Equivariant Set Networks","created":"2019-10-06","cited_by_count":45},{"id":"2103.01710","title":"Autobahn: Automorphism-based Graph Neural Nets","created":"2021-03-02","cited_by_count":39},{"id":"2203.13913","title":"SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks","created":"2022-03-25","cited_by_count":37},{"id":"1906.09427","title":"Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models","created":"2019-06-22","cited_by_count":36},{"id":"2010.15116","title":"On Graph Neural Networks versus Graph-Augmented MLPs","created":"2020-10-28","cited_by_count":30},{"id":"1802.08876","title":"Lov\\'asz Meets Weisfeiler and Leman","created":"2018-02-24","cited_by_count":27},{"id":"2210.09521","title":"A Practical, Progressively-Expressive GNN","created":"2022-10-18","cited_by_count":20},{"id":"1910.03802","title":"A Simple Proof of the Universality of Invariant/Equivariant Graph Neural Networks","created":"2019-10-09","cited_by_count":19},{"id":"2012.03174","title":"Counting Substructures with Higher-Order Graph Neural Networks: Possibility and Impossibility Results","created":"2020-12-06","cited_by_count":19},{"id":"1709.08359","title":"On the expressive power of query languages for matrices","created":"2017-09-25","cited_by_count":14},{"id":"1812.04379","title":"On the expressive power of linear algebra on graphs","created":"2018-12-11","cited_by_count":14},{"id":"1405.7375","title":"Tensor Network Contractions for #SAT","created":"2014-05-28","cited_by_count":2}],"next_cursor":null}