{"list":"other","total":83,"rows":[{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"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":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1703.00564","title":"MoleculeNet: A Benchmark for Molecular Machine Learning","created":"2017-03-02","cited_by_count":1051},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"id":"1806.03536","title":"Representation Learning on Graphs with Jumping Knowledge Networks","created":"2018-06-09","cited_by_count":877},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"2007.08663","title":"TUDataset: A collection of benchmark datasets for learning with graphs","created":"2020-07-16","cited_by_count":728},{"id":"1907.10903","title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification","created":"2019-07-25","cited_by_count":680},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"1905.05178","title":"Graph U-Nets","created":"2019-05-11","cited_by_count":492},{"id":"2004.05718","title":"Principal Neighbourhood Aggregation for Graph Nets","created":"2020-04-12","cited_by_count":478},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"1611.08402","title":"Geometric deep learning on graphs and manifolds using mixture model CNNs","created":"2016-11-25","cited_by_count":468},{"id":"1905.00067","title":"MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing","created":"2019-04-30","cited_by_count":443},{"id":"1910.12933","title":"Hyperbolic Graph Convolutional Neural Networks","created":"2019-10-28","cited_by_count":402},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"2106.08903","title":"GemNet: Universal Directional Graph Neural Networks for Molecules","created":"2021-06-02","cited_by_count":343},{"id":"1605.05273","title":"Learning Convolutional Neural Networks for Graphs","created":"2016-05-17","cited_by_count":314},{"id":"2006.09252","title":"Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting","created":"2020-06-16","cited_by_count":313},{"id":"1704.02901","title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","created":"2017-04-10","cited_by_count":310},{"id":"1906.04015","title":"Cormorant: Covariant Molecular Neural Networks","created":"2019-06-06","cited_by_count":263},{"id":"1511.06391","title":"Order Matters: Sequence to sequence for sets","created":"2015-11-19","cited_by_count":237},{"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":"2106.12575","title":"Weisfeiler and Lehman Go Cellular: CW Networks","created":"2021-06-23","cited_by_count":218},{"id":"1906.04817","title":"Position-aware Graph Neural Networks","created":"2019-06-11","cited_by_count":203},{"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":"1905.12560","title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","created":"2019-05-29","cited_by_count":172},{"id":"2005.03675","title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","created":"2020-05-07","cited_by_count":156},{"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":"1901.09342","title":"On the Universality of Invariant Networks","created":"2019-01-27","cited_by_count":152},{"id":"1903.02541","title":"Relational Pooling for Graph Representations","created":"2019-03-06","cited_by_count":150},{"id":"1903.11835","title":"A Survey on Graph Kernels","created":"2019-03-28","cited_by_count":148},{"id":"2110.13197","title":"Nested Graph Neural Networks","created":"2021-10-25","cited_by_count":137},{"id":"2010.02863","title":"Directional Graph Networks","created":"2020-10-06","cited_by_count":133},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122},{"id":"2111.06283","title":"DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks","created":"2021-11-11","cited_by_count":118},{"id":"1906.01277","title":"Wasserstein Weisfeiler-Lehman Graph Kernels","created":"2019-06-04","cited_by_count":104},{"id":"2106.04319","title":"Breaking the Limits of Message Passing Graph Neural Networks","created":"2021-06-08","cited_by_count":101},{"id":"1811.01287","title":"Towards Sparse Hierarchical Graph Classifiers","created":"2018-11-03","cited_by_count":100},{"id":"2006.15107","title":"Building powerful and equivariant graph neural networks with structural message-passing","created":"2020-06-26","cited_by_count":97},{"id":"2104.14624","title":"The Logic of Graph Neural Networks","created":"2021-04-29","cited_by_count":85},{"id":"2110.00577","title":"Reconstruction for Powerful Graph Representations","created":"2021-10-01","cited_by_count":65},{"id":"1811.04801","title":"On Weisfeiler-Leman Invariance: Subgraph Counts and Related Graph Properties","created":"2018-11-09","cited_by_count":63},{"id":"2011.03854","title":"Graph Kernels: State-of-the-Art and Future Challenges","created":"2020-11-07","cited_by_count":63},{"id":"2107.01959","title":"Universal Approximation of Functions on Sets","created":"2021-07-05","cited_by_count":62},{"id":"1912.06058","title":"Coloring graph neural networks for node disambiguation","created":"2019-12-12","cited_by_count":52},{"id":"2106.06707","title":"Graph Neural Networks with Local Graph Parameters","created":"2021-06-12","cited_by_count":52},{"id":"1606.01141","title":"On Valid Optimal Assignment Kernels and Applications to Graph Classification","created":"2016-06-03","cited_by_count":49},{"id":"2102.08786","title":"Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing","created":"2021-02-17","cited_by_count":47},{"id":"2103.01710","title":"Autobahn: Automorphism-based Graph Neural Nets","created":"2021-03-02","cited_by_count":39},{"id":"2003.12590","title":"word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data","created":"2020-03-27","cited_by_count":38},{"id":"1906.09427","title":"Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models","created":"2019-06-22","cited_by_count":36},{"id":"2007.12035","title":"The expressive power of kth-order invariant graph networks","created":"2020-07-23","cited_by_count":34},{"id":"1603.06186","title":"The Multiscale Laplacian Graph Kernel","created":"2016-03-20","cited_by_count":31},{"id":"1610.00064","title":"Faster Kernels for Graphs with Continuous Attributes via Hashing","created":"2016-10-01","cited_by_count":31},{"id":"1802.08876","title":"Lov\\'asz Meets Weisfeiler and Leman","created":"2018-02-24","cited_by_count":27},{"id":"2005.01214","title":"Graph Homomorphism Convolution","created":"2020-05-03","cited_by_count":27},{"id":"1911.07675","title":"GraLSP: Graph Neural Networks with Local Structural Patterns","created":"2019-11-18","cited_by_count":25},{"id":"2004.02593","title":"Let's Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework","created":"2020-04-06","cited_by_count":24},{"id":"1512.03547","title":"Graph Isomorphism in Quasipolynomial Time","created":"2015-12-11","cited_by_count":19},{"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":"1704.01023","title":"On the Combinatorial Power of the Weisfeiler-Lehman Algorithm","created":"2017-04-04","cited_by_count":18},{"id":"2002.10413","title":"Neural Message Passing on High Order Paths","created":"2020-02-24","cited_by_count":17},{"id":"1509.08251","title":"Tight Lower and Upper Bounds for the Complexity of Canonical Colour Refinement","created":"2015-09-28","cited_by_count":16},{"id":"2105.07264","title":"Neural Trees for Learning on Graphs","created":"2021-05-15","cited_by_count":13},{"id":"2005.10182","title":"The Iteration Number of Colour Refinement","created":"2020-05-20","cited_by_count":12},{"id":"1503.08792","title":"Graphs Identified by Logics with Counting","created":"2015-03-30","cited_by_count":11},{"id":"1708.07354","title":"The Weisfeiler-Leman Dimension of Planar Graphs is at most 3","created":"2017-08-24","cited_by_count":10},{"id":"1605.03480","title":"Upper Bounds on the Quantifier Depth for Graph Differentiation in First-Order Logic","created":"2016-05-11","cited_by_count":7}],"next_cursor":null}