Maximum Independent Set
Table of Contents
Maximum Independent Set #
The Maximum Independent Set problem is about finding the largest subset of vertices in a graph with no edges between them. It’s an NP-hard problem with important applications.
Recent Literature #
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search. NeurIPS, 2018. paper
Li, Zhuwen and Chen, Qifeng and Koltun, Vladlen.
Learning What to Defer for Maximum Independent Sets ICML, 2020. paper
Ahn, Sungsoo and Seo, Younggyo and Shin, Jinwoo
Distributed Scheduling Using Graph Neural Networks ICASSP, 2021. paper
Zhao, Zhongyuan and Verma, Gunjan and Rao, Chirag and Swami, Ananthram and Segarra, Santiago
Solving Graph-based Public Good Games with Tree Search and Imitation Learning NeurIPS, 2021. paper
Darvariu, Victor-Alexandru and Hailes, Stephen and Musolesi, Mirco
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs NeurIPS, 2021. paper
McCarty, Evan and Zhao, Qi and Sidiropoulos, Anastasios and Wang, Yusu
What’s Wrong with Deep Learning in Tree Search for Combinatorial Optimization ICLR, 2022. paper, code
Bother, Maximilian and Kissig, Otto and Taraz, Martin and Cohen, Sarel and Seidel, Karen and Friedrich, Tobias
Optimistic tree search strategies for black-box combinatorial optimization NeurIPS, 2022. paper
Malherbe, Cedric and Grosnit, Antoine and Tutunov, Rasul and Ammar, Haitham Bou and Wang, Jun
⭐ROCO: A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs ICLR, 2023. paper, code
Lu, Han and Li, Zenan and Wang, Runzhong and Ren, Qibing and Li, Xijun and Yuan, Mingxuan and Zeng, Jia and Yang, Xiaokang and Yan, Junchi
Revisiting Sampling for Combinatorial Optimization ICML, 2023. paper
Sun, Haoran, Goshvadi Katayoon,Nova Azade,Schuurmans Dale and Dai Hanjun.
DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization NeurIPS, 2023. paper, code
Zhiqing Sun, Yiming Yang
⭐T2T: From Distribution Learning in Training to Gradient Search in Testing for Combinatorial Optimization NeurIPS, 2023. paper, code
Yang Li, Jinpei Guo, Runzhong Wang, Junchi Yan
Unsupervised Learning for Combinatorial Optimization Needs Meta Learning ICLR, 2023. paper, code
Wang, Haoyu and Li, Pan
Graph-based Deterministic Policy Gradient for Repetitive Combinatorial Optimization Problems ICLR, 2023. paper, code
Zhao, Zhongyuan and Swami, Ananthram and Segarra, Santiago
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets NeurIPS, 2023. paper, code
Dinghuai Zhang, Hanjun Dai, Nikolay Malkin, Aaron Courville, Yoshua Bengio, Ling Pan
Variational Annealing on Graphs for Combinatorial Optimization NeurIPS, 2023. paper, code
Sanokowski, Sebastian and Berghammer, Wilhelm Franz and Hochreiter, Sepp and Lehner, Sebastian
Maximum Independent Set: Self-Training through Dynamic Programming NeurIPS, 2023. paper, code
Brusca, Lorenzo and Quaedvlieg, Lars CPM and Skoulakis, Stratis and Chrysos, Grigorios G and Cevher, Volkan
DISCS: A Benchmark for Discrete Sampling NeurIPS, 2023. paper, code
Katayoon Goshvadi, Haoran Sun, Xingchao Liu, Azade Nova, Ruqi Zhang, Will Sussman Grathwohl, Dale Schuurmans, Hanjun Dai
MARCO: A Memory-Augmented Reinforcement Framework for Combinatorial Optimization IJCAI, 2024. paper, code
Andoni I. Garmendia, Quentin Cappart, Josu Ceberio, Alexander Mendiburu
⭐Fast T2T: Optimization Consistency Speeds Up Diffusion-Based Training-to-Testing Solving for Combinatorial Optimization NeurIPS, 2024. paper, code
Yang Li, Jinpei Guo, Runzhong Wang, Hongyuan Zha, Junchi Yan
Controlling Continuous Relaxation for Combinatorial Optimization NeurIPS, 2024. paper
Yuma Ichikawa
Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks Nature Machine Intelligence, 2024. paper, code
Nasimeh Heydaribeni, Xinrui Zhan, Ruisi Zhang, Tina Eliassi-Rad, Farinaz Koushanfar
Efficient Combinatorial Optimization via Heat Diffusion NeurIPS, 2024. paper
Hengyuan Ma, Wenlian Lu, Jianfeng Feng
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization ICML, 2024. paper, code
Sanokowski, Sebastian and Hochreiter, Sepp and Lehner, Sebastian
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics ICLR, 2025. paper
Sebastian Sanokowski, Wilhelm Franz Berghammer, Haoyu Peter Wang, Martin Ennemoser, Sepp Hochreiter, Sebastian Lehner
⭐COExpander: Adaptive Solution Expansion for Combinatorial Optimization ICML, 2025. paper, code
Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan
⭐ML4CO-Bench-101: Benchmark Machine Learning for Classic Combinatorial Problems on Graphs NeurIPS, 2025. paper, code
Jiale Ma and Wenzheng Pan and Yang Li and Junchi Yan