{"list":"other","total":55,"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":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1308.3432","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","created":"2013-08-15","cited_by_count":2079},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":971},{"id":"2002.05287","title":"Geom-GCN: Geometric Graph Convolutional Networks","created":"2020-02-13","cited_by_count":826},{"id":"1803.01422","title":"DAGs with NO TEARS: Continuous Optimization for Structure Learning","created":"2018-03-04","cited_by_count":752},{"id":"2205.12454","title":"Recipe for a General, Powerful, Scalable Graph Transformer","created":"2022-05-25","cited_by_count":713},{"id":"1704.01665","title":"Learning Combinatorial Optimization Algorithms over Graphs","created":"2017-04-05","cited_by_count":561},{"id":"2209.14734","title":"DiGress: Discrete Denoising diffusion for graph generation","created":"2022-09-29","cited_by_count":520},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"1802.04687","title":"Neural Relational Inference for Interacting Systems","created":"2018-02-13","cited_by_count":339},{"id":"2005.06001","title":"Deep Learning Techniques for Inverse Problems in Imaging","created":"2020-05-12","cited_by_count":291},{"id":"1906.01227","title":"An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem","created":"2019-06-04","cited_by_count":269},{"id":"2102.09544","title":"Combinatorial optimization and reasoning with graph neural networks","created":"2021-02-18","cited_by_count":268},{"id":"2206.08164","title":"Long Range Graph Benchmark","created":"2022-06-16","cited_by_count":263},{"id":"2302.08224","title":"DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization","created":"2023-02-16","cited_by_count":221},{"id":"2006.10201","title":"On the Role of Sparsity and DAG Constraints for Learning Linear DAGs","created":"2020-06-17","cited_by_count":191},{"id":"2410.00083","title":"A Survey on Diffusion Models for Inverse Problems","created":"2024-09-30","cited_by_count":180},{"id":"1704.04058","title":"Solving ill-posed inverse problems using iterative deep neural networks","created":"2017-04-13","cited_by_count":145},{"id":"2210.04123","title":"DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems","created":"2022-10-08","cited_by_count":129},{"id":"2006.10643","title":"Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs","created":"2020-06-18","cited_by_count":115},{"id":"1810.13066","title":"Connecting the Dots: Identifying Network Structure via Graph Signal Processing","created":"2018-10-31","cited_by_count":111},{"id":"2210.11790","title":"FoSR: First-order spectral rewiring for addressing oversquashing in GNNs","created":"2022-10-21","cited_by_count":105},{"id":"2106.01798","title":"Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions","created":"2021-06-03","cited_by_count":98},{"id":"2302.04181","title":"Attending to Graph Transformers","created":"2023-02-08","cited_by_count":83},{"id":"2107.10483","title":"Efficient Neural Causal Discovery without Acyclicity Constraints","created":"2021-07-22","cited_by_count":82},{"id":"1901.10517","title":"Reparameterizable Subset Sampling via Continuous Relaxations","created":"2019-01-29","cited_by_count":70},{"id":"2503.11043","title":"InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences","created":"2025-03-14","cited_by_count":68},{"id":"2206.14928","title":"Manifold Interpolating Optimal-Transport Flows for Trajectory Inference","created":"2022-06-29","cited_by_count":64},{"id":"2406.01661","title":"A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization","created":"2024-06-03","cited_by_count":59},{"id":"2310.01668","title":"Locality-Aware Graph-Rewiring in GNNs","created":"2023-10-02","cited_by_count":56},{"id":"1909.08387","title":"Graph Neural Networks for Maximum Constraint Satisfaction","created":"2019-09-18","cited_by_count":46},{"id":"2210.01941","title":"SIMPLE: A Gradient Estimator for $k$-Subset Sampling","created":"2022-10-04","cited_by_count":34},{"id":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26},{"id":"2206.01506","title":"Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem?","created":"2022-06-03","cited_by_count":25},{"id":"2007.13290","title":"Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms","created":"2020-07-27","cited_by_count":16},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15},{"id":"2209.04862","title":"Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models","created":"2022-09-11","cited_by_count":12},{"id":"2111.05300","title":"Double Control Variates for Gradient Estimation in Discrete Latent Variable Models","created":"2021-11-09","cited_by_count":9},{"id":"2306.07803","title":"Inferring dynamic regulatory interaction graphs from time series data with perturbations","created":"2023-06-13","cited_by_count":9},{"id":"2308.10737","title":"UGSL: A Unified Framework for Benchmarking Graph Structure Learning","created":"2023-08-21","cited_by_count":9},{"id":"2405.20543","title":"Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs","created":"2024-05-31","cited_by_count":9},{"id":"2512.04475","title":"GraphBench: Next-generation graph learning benchmarking","created":"2025-12-04","cited_by_count":8},{"id":"2405.01015","title":"Network reconstruction via the minimum description length principle","created":"2024-05-02","cited_by_count":5},{"id":"2210.15353","title":"Learning Discrete Directed Acyclic Graphs via Backpropagation","created":"2022-10-27","cited_by_count":3},{"id":"2404.14668","title":"Source Localization for Cross Network Information Diffusion","created":"2024-04-23","cited_by_count":3},{"id":"2408.10436","title":"Learning Regularization for Graph Inverse Problems","created":"2024-08-19","cited_by_count":3},{"id":"2503.07736","title":"Uncertainty quantification and posterior sampling for network reconstruction","created":"2025-03-10","cited_by_count":1}],"next_cursor":null}