{"total":243,"rows":[{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167,"area":null},{"id":"2110.07875","title":"Graph Neural Networks with Learnable Structural and Positional Representations","created":"2021-10-15","cited_by_count":333,"area":null},{"id":"2103.16378","title":"End-to-End Constrained Optimization Learning: A Survey","created":"2021-03-30","cited_by_count":157,"area":null},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127,"area":null},{"id":"2107.01188","title":"Combinatorial Optimization with Physics-Inspired Graph Neural Networks","created":"2021-07-02","cited_by_count":108,"area":null},{"id":"2205.15659","title":"The CLRS Algorithmic Reasoning Benchmark","created":"2022-05-31","cited_by_count":106,"area":null},{"id":"2105.02761","title":"Neural Algorithmic Reasoning","created":"2021-05-06","cited_by_count":94,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2301.03313","title":"BQ-NCO: Bisimulation Quotienting for Efficient Neural Combinatorial Optimization","created":"2023-01-09","cited_by_count":82,"area":null},{"id":"2110.05291","title":"Graph Neural Network Guided Local Search for the Traveling Salesperson Problem","created":"2021-10-11","cited_by_count":70,"area":null},{"id":"2201.04122","title":"In Defense of the Unitary Scalarization for Deep Multi-Task Learning","created":"2022-01-11","cited_by_count":67,"area":null},{"id":"2307.05596","title":"Compositional Generalization from First Principles","created":"2023-07-10","cited_by_count":64,"area":null},{"id":"2112.01898","title":"Linear algebra with transformers","created":"2021-12-03","cited_by_count":63,"area":null},{"id":"2308.14104","title":"Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy","created":"2023-08-27","cited_by_count":62,"area":null},{"id":"2204.07697","title":"Theory of Graph Neural Networks: Representation and Learning","created":"2022-04-16","cited_by_count":61,"area":null},{"id":"2209.11142","title":"A Generalist Neural Algorithmic Learner","created":"2022-09-22","cited_by_count":59,"area":null},{"id":"2203.15386","title":"Pareto Set Learning for Neural Multi-objective Combinatorial Optimization","created":"2022-03-29","cited_by_count":56,"area":null},{"id":"2209.15059","title":"Provably expressive temporal graph networks","created":"2022-09-29","cited_by_count":56,"area":null},{"id":"2203.15544","title":"Graph Neural Networks are Dynamic Programmers","created":"2022-03-29","cited_by_count":55,"area":null},{"id":"2502.14546","title":"Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks","created":"2025-02-20","cited_by_count":53,"area":null},{"id":"2205.14210","title":"MIP-GNN: A Data-Driven Framework for Guiding Combinatorial Solvers","created":"2022-05-27","cited_by_count":49,"area":null},{"id":"2104.14516","title":"Constructions in combinatorics via neural networks","created":"2021-04-29","cited_by_count":48,"area":null},{"id":"2406.15079","title":"GOAL: A Generalist Combinatorial Optimization Agent Learner","created":"2024-06-21","cited_by_count":42,"area":null},{"id":"2205.06109","title":"Equivariant quantum circuits for learning on weighted graphs","created":"2022-05-12","cited_by_count":41,"area":null},{"id":"2305.17010","title":"Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets","created":"2023-05-26","cited_by_count":40,"area":null},{"id":"2209.12288","title":"On Representing Linear Programs by Graph Neural Networks","created":"2022-09-25","cited_by_count":39,"area":null},{"id":"2310.06116","title":"OptiMUS: Optimization Modeling Using MIP Solvers and large language models","created":"2023-10-09","cited_by_count":39,"area":null},{"id":"2506.09050","title":"ALE-Bench: A Benchmark for Long-Horizon Objective-Driven Algorithm Engineering","created":"2025-06-10","cited_by_count":39,"area":null},{"id":"2207.07888","title":"SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks","created":"2022-07-16","cited_by_count":37,"area":null},{"id":"2110.10942","title":"Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness","created":"2021-10-21","cited_by_count":35,"area":null},{"id":"2210.15304","title":"Explaining the Explainers in Graph Neural Networks: a Comparative Study","created":"2022-10-27","cited_by_count":35,"area":null},{"id":"2310.13397","title":"Equivariant Deep Weight Space Alignment","created":"2023-10-20","cited_by_count":35,"area":null},{"id":"2406.02997","title":"Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs","created":"2024-06-05","cited_by_count":34,"area":null},{"id":"2206.13211","title":"Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set","created":"2022-06-27","cited_by_count":33,"area":null},{"id":"2402.01107","title":"Simulation of Graph Algorithms with Looped Transformers","created":"2024-02-02","cited_by_count":30,"area":null},{"id":"2202.01606","title":"Graph Coloring with Physics-Inspired Graph Neural Networks","created":"2022-02-03","cited_by_count":29,"area":null},{"id":"2106.07162","title":"Goal-Aware Neural SAT Solver","created":"2021-06-14","cited_by_count":28,"area":null},{"id":"2203.15935","title":"Graph Neural Networks in IoT: A Survey","created":"2022-03-29","cited_by_count":28,"area":null},{"id":"2208.10227","title":"One Model, Any CSP: Graph Neural Networks as Fast Global Search Heuristics for Constraint Satisfaction","created":"2022-08-22","cited_by_count":27,"area":null},{"id":"2202.10962","title":"Adaptive Cut Selection in Mixed-Integer Linear Programming","created":"2022-02-22","cited_by_count":26,"area":null},{"id":"2310.02156","title":"Probabilistically Rewired Message-Passing Neural Networks","created":"2023-10-03","cited_by_count":26,"area":null},{"id":"2206.00787","title":"On the Generalization of Neural Combinatorial Optimization Heuristics","created":"2022-06-01","cited_by_count":24,"area":null},{"id":"2310.10603","title":"Exploring the Power of Graph Neural Networks in Solving Linear Optimization Problems","created":"2023-10-16","cited_by_count":24,"area":null},{"id":"2402.05501","title":"Machine Learning Augmented Branch and Bound for Mixed Integer Linear Programming","created":"2024-02-08","cited_by_count":24,"area":null},{"id":"2401.11849","title":"Self-Labeling the Job Shop Scheduling Problem","created":"2024-01-22","cited_by_count":23,"area":null},{"id":"2406.00415","title":"Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives","created":"2024-06-01","cited_by_count":23,"area":null},{"id":"2107.07076","title":"An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problem","created":"2021-07-15","cited_by_count":22,"area":null},{"id":"2202.08396","title":"Augment with Care: Contrastive Learning for Combinatorial Problems","created":"2022-02-17","cited_by_count":22,"area":null},{"id":"2206.02568","title":"A Deep Reinforcement Learning Framework For Column Generation","created":"2022-06-03","cited_by_count":22,"area":null},{"id":"2110.05442","title":"Neural Algorithmic Reasoners are Implicit Planners","created":"2021-10-11","cited_by_count":21,"area":null},{"id":"2211.03232","title":"Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks","created":"2022-11-06","cited_by_count":21,"area":null},{"id":"2302.11603","title":"Some Might Say All You Need Is Sum","created":"2023-02-22","cited_by_count":21,"area":null},{"id":"2311.14156","title":"Variational Annealing on Graphs for Combinatorial Optimization","created":"2023-11-23","cited_by_count":21,"area":null},{"id":"2205.14345","title":"Reinforcement Learning for Branch-and-Bound Optimisation using Retrospective Trajectories","created":"2022-05-28","cited_by_count":20,"area":null},{"id":"2302.09166","title":"Machine Learning for Cutting Planes in Integer Programming: A Survey","created":"2023-02-17","cited_by_count":20,"area":null},{"id":"2105.05911","title":"The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs","created":"2021-05-12","cited_by_count":19,"area":null},{"id":"2107.10234","title":"Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks","created":"2021-07-21","cited_by_count":19,"area":null},{"id":"2110.15481","title":"Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning","created":"2021-10-29","cited_by_count":19,"area":null},{"id":"2304.14329","title":"Learning to Extrapolate: A Transductive Approach","created":"2023-04-27","cited_by_count":19,"area":null},{"id":"2206.14092","title":"Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks","created":"2022-06-28","cited_by_count":17,"area":null},{"id":"2205.02453","title":"Learning to Solve Vehicle Routing Problems: A Survey","created":"2022-05-05","cited_by_count":16,"area":null},{"id":"2309.16965","title":"Controlling Continuous Relaxation for Combinatorial Optimization","created":"2023-09-29","cited_by_count":16,"area":null},{"id":"2405.17311","title":"Probabilistic Graph Rewiring via Virtual Nodes","created":"2024-05-27","cited_by_count":15,"area":null},{"id":"2406.14096","title":"Graph Neural Networks for Job Shop Scheduling Problems: A Survey","created":"2024-06-20","cited_by_count":15,"area":null},{"id":"2208.04055","title":"Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions","created":"2022-08-08","cited_by_count":14,"area":null},{"id":"2103.01043","title":"Persistent Message Passing","created":"2021-03-01","cited_by_count":13,"area":null},{"id":"2107.08881","title":"Reasoning-Modulated Representations","created":"2021-07-19","cited_by_count":13,"area":null},{"id":"2111.03016","title":"Graph neural network initialisation of quantum approximate optimisation","created":"2021-11-04","cited_by_count":13,"area":null},{"id":"2211.14296","title":"A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation","created":"2022-11-25","cited_by_count":13,"area":null},{"id":"2402.07568","title":"Weisfeiler-Leman at the margin: When more expressivity matters","created":"2024-02-12","cited_by_count":13,"area":null},{"id":"2412.14451","title":"CLDG: Contrastive Learning on Dynamic Graphs","created":"2024-12-19","cited_by_count":13,"area":null},{"id":"2107.07493","title":"Algorithmic Concept-based Explainable Reasoning","created":"2021-07-15","cited_by_count":12,"area":null},{"id":"2305.14814","title":"What functions can Graph Neural Networks compute on random graphs? The role of Positional Encoding","created":"2023-05-24","cited_by_count":12,"area":null},{"id":"2305.19903","title":"Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization","created":"2023-05-31","cited_by_count":12,"area":null},{"id":"2310.00526","title":"Are Graph Neural Networks Optimal Approximation Algorithms?","created":"2023-10-01","cited_by_count":11,"area":null},{"id":"2401.03244","title":"Artificial Intelligence for Operations Research: Revolutionizing the Operations Research Process","created":"2024-01-06","cited_by_count":11,"area":null},{"id":"2503.19173","title":"Graph neural networks extrapolate out-of-distribution for shortest paths","created":"2025-03-24","cited_by_count":11,"area":null},{"id":"2505.16952","title":"FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization","created":"2025-05-22","cited_by_count":11,"area":null},{"id":"2109.10380","title":"Deep Policies for Online Bipartite Matching: A Reinforcement Learning Approach","created":"2021-09-21","cited_by_count":10,"area":null},{"id":"2207.14742","title":"Graph Neural Networks for Channel Decoding","created":"2022-07-29","cited_by_count":10,"area":null},{"id":"2502.00277","title":"Regularized Langevin Dynamics for Combinatorial Optimization","created":"2025-02-01","cited_by_count":10,"area":null},{"id":"2202.00264","title":"Graph-based Neural Acceleration for Nonnegative Matrix Factorization","created":"2022-02-01","cited_by_count":9,"area":null},{"id":"2402.10977","title":"Generative AI and Process Systems Engineering: The Next Frontier","created":"2024-02-15","cited_by_count":9,"area":null},{"id":"2307.04661","title":"On the power of graph neural networks and the role of the activation function","created":"2023-07-10","cited_by_count":8,"area":null},{"id":"2310.03195","title":"Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions","created":"2023-10-04","cited_by_count":8,"area":null},{"id":"2410.11061","title":"Learning to Optimize for Mixed-Integer Non-linear Programming with Feasibility Guarantees","created":"2024-10-14","cited_by_count":8,"area":null},{"id":"2103.00137","title":"Meta-Learning with Graph Neural Networks: Methods and Applications","created":"2021-02-27","cited_by_count":7,"area":null},{"id":"2405.08424","title":"Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More","created":"2024-05-14","cited_by_count":7,"area":null},{"id":"2507.00218","title":"Learning for routing: A guided review of recent developments and future directions","created":"2025-06-30","cited_by_count":7,"area":null},{"id":"2204.10834","title":"Learning for Spatial Branching: An Algorithm Selection Approach","created":"2022-04-22","cited_by_count":6,"area":null},{"id":"2205.00897","title":"Fast Continuous and Integer L-shaped Heuristics Through Supervised Learning","created":"2022-05-02","cited_by_count":6,"area":null},{"id":"2205.01356","title":"Neural Combinatorial Optimization: a New Player in the Field","created":"2022-05-03","cited_by_count":6,"area":null},{"id":"2205.11638","title":"DOGE-Train: Discrete Optimization on GPU with End-to-end Training","created":"2022-05-23","cited_by_count":6,"area":null},{"id":"2402.02287","title":"Future Directions in the Theory of Graph Machine Learning","created":"2024-02-03","cited_by_count":6,"area":null},{"id":"2505.23185","title":"Improving the Effective Receptive Field of Message-Passing Neural Networks","created":"2025-05-29","cited_by_count":6,"area":null},{"id":"2111.07568","title":"Can Graph Neural Networks Learn to Solve MaxSAT Problem?","created":"2021-11-15","cited_by_count":5,"area":null},{"id":"2204.09052","title":"Optimizing Tensor Network Contraction Using Reinforcement Learning","created":"2022-04-18","cited_by_count":5,"area":null},{"id":"2211.01980","title":"Learning to Configure Computer Networks with Neural Algorithmic Reasoning","created":"2022-10-26","cited_by_count":5,"area":null},{"id":"2307.00670","title":"Automatic MILP Solver Configuration By Learning Problem Similarities","created":"2023-07-02","cited_by_count":5,"area":null},{"id":"2310.04140","title":"Routing Arena: A Benchmark Suite for Neural Routing Solvers","created":"2023-10-06","cited_by_count":5,"area":null}],"next_cursor":"100"}