{"list":"other","total":51,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"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":"2005.00687","title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs","created":"2020-05-02","cited_by_count":2163},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1902.07153","title":"Simplifying Graph Convolutional Networks","created":"2019-02-19","cited_by_count":1477},{"id":"2002.02126","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","created":"2020-02-06","cited_by_count":1139},{"id":"1810.05997","title":"Predict then Propagate: Graph Neural Networks meet Personalized PageRank","created":"2018-10-14","cited_by_count":993},{"id":"1603.08861","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","created":"2016-03-29","cited_by_count":976},{"id":"2007.02133","title":"Simple and Deep Graph Convolutional Networks","created":"2020-07-04","cited_by_count":860},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"2006.07988","title":"Adaptive Universal Generalized PageRank Graph Neural Network","created":"2020-06-14","cited_by_count":595},{"id":"1712.00468","title":"Graph Signal Processing: Overview, Challenges and Applications","created":"2017-12-01","cited_by_count":413},{"id":"2110.14446","title":"Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods","created":"2021-10-27","cited_by_count":311},{"id":"2008.08692","title":"Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters","created":"2020-08-19","cited_by_count":250},{"id":"2106.10994","title":"BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation","created":"2021-06-21","cited_by_count":195},{"id":"2205.15508","title":"Rethinking Graph Neural Networks for Anomaly Detection","created":"2022-05-31","cited_by_count":187},{"id":"1901.01343","title":"Graph Neural Networks with convolutional ARMA filters","created":"2019-01-05","cited_by_count":185},{"id":"2205.07308","title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily","created":"2022-05-15","cited_by_count":179},{"id":"2202.07082","title":"Graph Neural Networks for Graphs with Heterophily: A Survey","created":"2022-02-14","cited_by_count":175},{"id":"2108.01938","title":"PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations","created":"2021-08-04","cited_by_count":106},{"id":"2303.01028","title":"Specformer: Spectral Graph Neural Networks Meet Transformers","created":"2023-03-02","cited_by_count":102},{"id":"2202.03580","title":"Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited","created":"2022-02-04","cited_by_count":87},{"id":"2106.05150","title":"Scaling Up Graph Neural Networks Via Graph Coarsening","created":"2021-06-09","cited_by_count":80},{"id":"2007.16061","title":"Graph signal processing for machine learning: A review and new perspectives","created":"2020-07-31","cited_by_count":73},{"id":"2302.01524","title":"Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing","created":"2023-02-03","cited_by_count":67},{"id":"1910.02370","title":"GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding","created":"2019-10-06","cited_by_count":58},{"id":"1802.07510","title":"Spectrally approximating large graphs with smaller graphs","created":"2018-02-21","cited_by_count":56},{"id":"2012.08752","title":"Graph Neural Networks: Taxonomy, Advances and Trends","created":"2020-12-16","cited_by_count":48},{"id":"2305.13084","title":"A Fractional Graph Laplacian Approach to Oversmoothing","created":"2023-05-22","cited_by_count":45},{"id":"1802.04447","title":"Graph Coarsening with Preserved Spectral Properties","created":"2018-02-13","cited_by_count":41},{"id":"2110.03969","title":"Graph Meta Network for Multi-Behavior Recommendation","created":"2021-10-08","cited_by_count":38},{"id":"2302.12432","title":"Graph Neural Networks with Learnable and Optimal Polynomial Bases","created":"2023-02-24","cited_by_count":36},{"id":"2112.15345","title":"Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs","created":"2021-12-31","cited_by_count":33},{"id":"2401.14155","title":"Alleviating Structural Distribution Shift in Graph Anomaly Detection","created":"2024-01-25","cited_by_count":32},{"id":"2402.11139","title":"LiGNN: Graph Neural Networks at LinkedIn","created":"2024-02-17","cited_by_count":31},{"id":"2312.14438","title":"PC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering","created":"2023-12-22","cited_by_count":28},{"id":"2106.11863","title":"Graph coarsening: From scientific computing to machine learning","created":"2021-06-22","cited_by_count":21},{"id":"2305.14000","title":"Node-wise Diffusion for Scalable Graph Learning","created":"2023-05-23","cited_by_count":17},{"id":"2406.03464","title":"Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach","created":"2024-06-05","cited_by_count":17},{"id":"1612.04883","title":"Graph Summarization Methods and Applications: A Survey","created":"2016-12-14","cited_by_count":16},{"id":"2402.06128","title":"Rethinking Node-wise Propagation for Large-scale Graph Learning","created":"2024-02-09","cited_by_count":16},{"id":"2306.08854","title":"A Gromov--Wasserstein Geometric View of Spectrum-Preserving Graph Coarsening","created":"2023-06-15","cited_by_count":15},{"id":"2312.15520","title":"Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training","created":"2023-12-24","cited_by_count":12},{"id":"1901.08286","title":"Neural IR Meets Graph Embedding: A Ranking Model for Product Search","created":"2019-01-24","cited_by_count":11},{"id":"2210.00437","title":"A Unified Framework for Optimization-Based Graph Coarsening","created":"2022-10-02","cited_by_count":9},{"id":"2403.07954","title":"Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach","created":"2024-03-12","cited_by_count":5},{"id":"2412.19106","title":"ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters","created":"2024-12-26","cited_by_count":5},{"id":"2404.04559","title":"Spectral GNN via Two-dimensional (2-D) Graph Convolution","created":"2024-04-06","cited_by_count":3},{"id":"2404.15354","title":"Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach","created":"2024-04-15","cited_by_count":1}],"next_cursor":null}