{"list":"other","total":56,"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":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2004.11362","title":"Supervised Contrastive Learning","created":"2020-04-23","cited_by_count":2829},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"2002.02126","title":"LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation","created":"2020-02-06","cited_by_count":1139},{"id":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"id":"1708.05031","title":"Neural Collaborative Filtering","created":"2017-08-16","cited_by_count":1111},{"id":"1905.08108","title":"Neural Graph Collaborative Filtering","created":"2019-05-20","cited_by_count":661},{"id":"1503.03578","title":"LINE: Large-scale Information Network Embedding","created":"2015-03-12","cited_by_count":582},{"id":"1909.03211","title":"Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological View","created":"2019-09-07","cited_by_count":545},{"id":"1602.01585","title":"Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering","created":"2016-02-04","cited_by_count":520},{"id":"2011.02260","title":"Graph Neural Networks in Recommender Systems: A Survey","created":"2020-11-04","cited_by_count":494},{"id":"1905.07854","title":"KGAT: Knowledge Graph Attention Network for Recommendation","created":"2019-05-20","cited_by_count":343},{"id":"1802.05814","title":"Variational Autoencoders for Collaborative Filtering","created":"2018-02-16","cited_by_count":310},{"id":"2010.10783","title":"Self-supervised Graph Learning for Recommendation","created":"2020-10-21","cited_by_count":305},{"id":"1907.06902","title":"Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches","created":"2019-07-16","cited_by_count":185},{"id":"2112.08679","title":"Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation","created":"2021-12-16","cited_by_count":173},{"id":"2001.10167","title":"Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach","created":"2020-01-28","cited_by_count":120},{"id":"2007.01764","title":"Disentangled Graph Collaborative Filtering","created":"2020-07-03","cited_by_count":118},{"id":"1910.14238","title":"Learning Disentangled Representations for Recommendation","created":"2019-10-31","cited_by_count":110},{"id":"2101.06448","title":"Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation","created":"2021-01-16","cited_by_count":101},{"id":"1905.03375","title":"Embarrassingly Shallow Autoencoders for Sparse Data","created":"2019-05-08","cited_by_count":94},{"id":"2110.15114","title":"UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation","created":"2021-10-28","cited_by_count":87},{"id":"2202.06200","title":"Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning","created":"2022-02-13","cited_by_count":86},{"id":"2205.00976","title":"Knowledge Graph Contrastive Learning for Recommendation","created":"2022-05-02","cited_by_count":70},{"id":"1510.07025","title":"Modeling User Exposure in Recommendation","created":"2015-10-23","cited_by_count":69},{"id":"1911.07698","title":"A Troubling Analysis of Reproducibility and Progress in Recommender Systems Research","created":"2019-11-18","cited_by_count":65},{"id":"2109.12613","title":"SimpleX: A Simple and Strong Baseline for Collaborative Filtering","created":"2021-09-26","cited_by_count":65},{"id":"2204.12200","title":"Hypergraph Contrastive Collaborative Filtering","created":"2022-04-26","cited_by_count":63},{"id":"2205.09626","title":"BARS: Towards Open Benchmarking for Recommender Systems","created":"2022-05-19","cited_by_count":54},{"id":"2108.05552","title":"Graph Trend Filtering Networks for Recommendations","created":"2021-08-12","cited_by_count":53},{"id":"2108.07567","title":"How Powerful is Graph Convolution for Recommendation?","created":"2021-08-17","cited_by_count":50},{"id":"2102.10044","title":"Interest-aware Message-Passing GCN for Recommendation","created":"2021-02-19","cited_by_count":39},{"id":"2202.08523","title":"Contrastive Meta Learning with Behavior Multiplicity for Recommendation","created":"2022-02-17","cited_by_count":37},{"id":"1808.10523","title":"Spectral Collaborative Filtering","created":"2018-08-30","cited_by_count":34},{"id":"2204.08176","title":"HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization","created":"2022-04-18","cited_by_count":34},{"id":"2006.15516","title":"Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters","created":"2020-06-28","cited_by_count":32},{"id":"1804.10862","title":"Collaborative Memory Network for Recommendation Systems","created":"2018-04-29","cited_by_count":30},{"id":"2012.05442","title":"Bipartite Graph Embedding via Mutual Information Maximization","created":"2020-12-10","cited_by_count":29},{"id":"2302.02113","title":"Personalized Graph Signal Processing for Collaborative Filtering","created":"2023-02-04","cited_by_count":29},{"id":"2103.02590","title":"Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation","created":"2021-03-03","cited_by_count":28},{"id":"2204.06832","title":"Self-Guided Learning to Denoise for Robust Recommendation","created":"2022-04-14","cited_by_count":24},{"id":"2304.01910","title":"On the Variance of Neural Network Training with respect to Test Sets and Distributions","created":"2023-04-04","cited_by_count":22},{"id":"2007.07204","title":"Sampler Design for Implicit Feedback Data by Noisy-label Robust Learning","created":"2020-06-28","cited_by_count":18},{"id":"2208.12689","title":"SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation","created":"2022-08-26","cited_by_count":16},{"id":"2112.13705","title":"Graph Collaborative Reasoning","created":"2021-12-27","cited_by_count":14},{"id":"2204.12326","title":"Investigating Accuracy-Novelty Performance for Graph-based Collaborative Filtering","created":"2022-04-26","cited_by_count":13},{"id":"2202.02576","title":"Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation","created":"2022-02-05","cited_by_count":12},{"id":"2203.01155","title":"Top-N Recommendation Algorithms: A Quest for the State-of-the-Art","created":"2022-03-02","cited_by_count":12},{"id":"2007.12865","title":"Self-supervised Learning for Large-scale Item Recommendations","created":"2020-07-25","cited_by_count":11},{"id":"2303.08537","title":"Graph-less Collaborative Filtering","created":"2023-03-15","cited_by_count":11},{"id":"2204.11346","title":"Less is More: Reweighting Important Spectral Graph Features for Recommendation","created":"2022-04-24","cited_by_count":10},{"id":"2102.00482","title":"Improving Accountability in Recommender Systems Research Through Reproducibility","created":"2021-01-31","cited_by_count":5}],"next_cursor":null}