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https://github.com/xiaoxinhe/iclr2024_learning_on_graphs

List of papers on ICLR 2024
https://github.com/xiaoxinhe/iclr2024_learning_on_graphs

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List of papers on ICLR 2024

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# [ICLR'24] Learning on Graphs



| Title    | Authors | OpenReview |

| -------- | ------- | ------- |

|DREAM: Dual Structured Exploration with Mixup for Open-set Graph Domain Adaption|Nan Yin, Mengzhu Wang, Zhenghan Chen, Li Shen, Huan Xiong, Bin Gu, Xiao Luo|[here](https://iclr.cc/virtual/2024/poster/19448)

|Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks|Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, Dongsheng Luo|[here](https://iclr.cc/virtual/2024/poster/17563)

|PROGRAM: PROtotype GRAph Model based Pseudo-Label Learning for Test-Time Adaptation|Haopeng Sun, Lumin Xu, Sheng Jin, Ping Luo, Chen Qian, Wentao Liu|[here](https://iclr.cc/virtual/2024/poster/17470)

|Graphpulse: Topological representations for temporal graph property prediction|Kiarash Shamsi, Farimah Poursafaei, Shenyang(Andy) Huang, Tran Gia Bao Ngo, Baris Coskunuzer, Cuneyt Akcora|[here](https://iclr.cc/virtual/2024/poster/19138)

|GnnX-Bench: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking|Mert Kosan, Samidha Verma, Burouj Armgaan, Khushbu Pahwa, Ambuj K Singh, Sourav Medya, Sayan Ranu|[here](https://iclr.cc/virtual/2024/poster/18507)

|GraphCare: Enhancing Healthcare Predictions with Personalized Knowledge Graphs|Pengcheng Jiang, Cao Xiao, Adam Cross, Jimeng Sun|[here](https://iclr.cc/virtual/2024/poster/17612)

|Improving Generalization in Equivariant Graph Neural Networks with Physical Inductive Biases|Yang Liu, Jiashun Cheng, Haihong Zhao, Tingyang Xu, Peilin Zhao, Fugee Tsung, Jia Li, Yu Rong|[here](https://iclr.cc/virtual/2024/poster/19496)

|BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs|Zifeng Wang, Zichen Wang, Balasubramaniam Srinivasan, Vassilis N. Ioannidis, Huzefa Rangwala, RISHITA ANUBHAI|[here](https://iclr.cc/virtual/2024/poster/18031)

|Scalable and Effective Implicit Graph Neural Networks on Large Graphs|Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Yiwei Wang, Chaosheng Dong, Xiaokui Xiao|[here](https://iclr.cc/virtual/2024/poster/18674)

|A Differentially Private Clustering Algorithm for Well-Clustered Graphs|Weiqiang He, Hendrik Fichtenberger, Pan Peng|[here](https://iclr.cc/virtual/2024/poster/18087)

|Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs|Anson Bastos, Kuldeep Singh, Abhishek Nadgeri, Manish Singh, Toyotaro Suzumura|[here](https://iclr.cc/virtual/2024/poster/17560)

|Contrastive Learning is Spectral Clustering on Similarity Graph|Yifan Zhang, Zhiquan Tan, Jingqin Yang, Yang Yuan|[here](https://iclr.cc/virtual/2024/poster/18101)

|Talk like a Graph: Encoding Graphs for Large Language Models|Bahare Fatemi, Jonathan Halcrow, Bryan Perozzi|[here](https://iclr.cc/virtual/2024/poster/18954)

|Uncertainty-aware Graph-based Hyperspectral Image Classification|Linlin Yu, Yifei Lou, Feng Chen|[here](https://iclr.cc/virtual/2024/poster/19322)

|Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks|Tianyu Fan, Lirong Wu, Yufei Huang, Haitao Lin, Cheng Tan, Zhangyang Gao, Stan Z Li|[here](https://iclr.cc/virtual/2024/poster/18307)

|On the Stability of Expressive Positional Encodings for Graph Neural Networks|Yinan Huang, William Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, Pan Li|[here](https://iclr.cc/virtual/2024/poster/17465)

|$\mathbb{D}^2$ Pruning: Message Passing for Balancing Diversity & Difficulty in Data Pruning|Adyasha Maharana, Prateek Yadav, Mohit Bansal|[here](https://iclr.cc/virtual/2024/poster/17608)

|NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks|Wenxi Wang, Yang Hu, Mohit Tiwari, Sarfraz Khurshid, Kenneth McMillan, Risto Miikkulainen|[here](https://iclr.cc/virtual/2024/poster/17641)

|Neural Common Neighbor with Completion for Link Prediction|Xiyuan Wang, Haotong Yang, Muhan Zhang|[here](https://iclr.cc/virtual/2024/poster/17650)

|From Graphs to Hypergraphs: Hypergraph Projection and its Remediation|Yanbang Wang, Jon Kleinberg|[here](https://iclr.cc/virtual/2024/poster/17710)

|Counting Graph Substructures with Graph Neural Networks|Charilaos Kanatsoulis, Alejandro Ribeiro|[here](https://iclr.cc/virtual/2024/poster/17728)

|Adversarial Attacks on Fairness of Graph Neural Networks|Binchi Zhang, Yushun Dong, Chen Chen, Yada Zhu, Minnan Luo, Jundong Li|[here](https://iclr.cc/virtual/2024/poster/17753)

|Graph Transformers on EHRs: Better Representation Improves Downstream Performance|Raphael Poulain, Rahmatollah Beheshti|[here](https://iclr.cc/virtual/2024/poster/17772)

|Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning|Ahmed Abdulaal, Adamos Hadjivasiliou, Nina Montaña-Brown, Tiantian He, Ayodeji Ijishakin, Ivana Drobnjak, Daniel Castro, Daniel Alexander|[here](https://iclr.cc/virtual/2024/poster/17791)

|GRAPH-CONSTRAINED DIFFUSION FOR END-TO-END PATH PLANNING|DINGYUAN SHI, Yongxin Tong, Zimu Zhou, Ke Xu, Zheng Wang, Jieping Ye|[here](https://iclr.cc/virtual/2024/poster/17513)

|CoRe-GD: A Hierarchical Framework for Scalable Graph Visualization with GNNs|Florian Grötschla, Joël Mathys, Róbert Veres, Roger Wattenhofer|[here](https://iclr.cc/virtual/2024/poster/17514)

|Universal Graph Random Features|Isaac Reid, Krzysztof Choromanski, Eli Berger, Adrian Weller|[here](https://iclr.cc/virtual/2024/poster/17523)

|A Simple and Scalable Representation for Graph Generation|Yunhui Jang, Seul Lee, Sungsoo Ahn|[here](https://iclr.cc/virtual/2024/poster/17859)

|Towards Foundation Models for Knowledge Graph Reasoning|Mikhail Galkin, Xinyu Yuan, Hesham Mostafa, Jian Tang, Zhaocheng Zhu|[here](https://iclr.cc/virtual/2024/poster/18020)

|Training Graph Transformers via Curriculum-Enhanced Attention Distillation|Yisong Huang, Jin Li, Xinlong Chen, Yang-Geng Fu|[here](https://iclr.cc/virtual/2024/poster/18040)

|Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors|Hang Yin, Zihao Wang, Yangqiu Song|[here](https://iclr.cc/virtual/2024/poster/19598)

|Graph Metanetworks for Processing Diverse Neural Architectures|Derek Lim, Haggai Maron, Marc T Law, Jonathan Lorraine, James Lucas|[here](https://iclr.cc/virtual/2024/poster/18054)

|Deceptive Fairness Attacks on Graphs via Meta Learning|Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong|[here](https://iclr.cc/virtual/2024/poster/18065)

|Graph Parsing Networks|Yunchong Song, Siyuan Huang, Xinbing Wang, Chenghu Zhou, Zhouhan Lin|[here](https://iclr.cc/virtual/2024/poster/18080)

|Conformal Inductive Graph Neural Networks|Soroush H. Zargarbashi, Aleksandar Bojchevski|[here](https://iclr.cc/virtual/2024/poster/18084)

|Polynormer: Polynomial-Expressive Graph Transformer in Linear Time|Chenhui Deng, Zichao Yue, Zhiru Zhang|[here](https://iclr.cc/virtual/2024/poster/18086)

|Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks|Federico Errica, Mathias Niepert|[here](https://iclr.cc/virtual/2024/poster/18112)

|Efficient Subgraph GNNs by Learning Effective Selection Policies|Beatrice Bevilacqua, Moshe Eliasof, Eli Meirom, Bruno Ribeiro, Haggai Maron|[here](https://iclr.cc/virtual/2024/poster/18129)

|DyVal: Graph-informed Dynamic Evaluation of Large Language Models|Kaijie Zhu, Jiaao Chen, Jindong Wang, Neil Gong, Diyi Yang, Xing Xie|[here](https://iclr.cc/virtual/2024/poster/18134)

|PRES: Toward Scalable Memory-Based Dynamic Graph Neural Networks|Junwei Su, Difan Zou, Chuan Wu|[here](https://iclr.cc/virtual/2024/poster/18136)

|One For All: Towards Training One Graph Model For All Classification Tasks|Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, Muhan Zhang|[here](https://iclr.cc/virtual/2024/poster/19474)

|GNNBoundary: Towards Explaining Graph Neural Networks through the Lens of Decision Boundaries|Xiaoqi Wang, Han Wei Shen|[here](https://iclr.cc/virtual/2024/poster/18484)

|Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries|Haitz Sáez de Ocáriz Borde, Anastasis Kratsios|[here](https://iclr.cc/virtual/2024/poster/18249)

|iGraphMix: Input Graph Mixup Method for Node Classification|Jongwon Jeong, Hoyeop Lee, Hyui Geon Yoon, Beomyoung Lee, Junhee Heo, Geonsoo Kim, Kim Jin Seon|[here](https://iclr.cc/virtual/2024/poster/18379)

|A Stochastic Centering Framework for Improving Calibration in Graph Neural Networks|Puja Trivedi, Mark Heimann, Rushil Anirudh, Danai Koutra, Jayaraman J. Thiagarajan|[here](https://iclr.cc/virtual/2024/poster/18402)

|Deep Temporal Graph Clustering|Meng Liu, Yue Liu, KE LIANG, Wenxuan Tu, Siwei Wang, sihang zhou, Xinwang Liu|[here](https://iclr.cc/virtual/2024/poster/18498)

|Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approach|Aoqi Zuo, yiqing li, Susan Wei, Mingming Gong|[here](https://iclr.cc/virtual/2024/poster/18609)

|A Generative Pre-Training Framework for Spatio-Temporal Graph Transfer Learning|Yuan Yuan, Chenyang Shao, Jingtao Ding, Depeng Jin, Yong Li|[here](https://iclr.cc/virtual/2024/poster/18659)

|Learning Multi-Agent Communication from Graph Modeling Perspective|Shengchao Hu, Li Shen, Ya Zhang, Dacheng Tao|[here](https://iclr.cc/virtual/2024/poster/18666)

|Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs|Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han|[here](https://iclr.cc/virtual/2024/poster/18681)

|From Matching to Mixing: A Graph Interpolation Approach for SAT Instance Generation|Xinyan Chen, Yang Li, Runzhong Wang, Junchi Yan|[here](https://iclr.cc/virtual/2024/poster/18702)

|From Latent Graph to Latent Topology Inference: Differentiable Cell Complex Module|Claudio Battiloro, Indro Spinelli, Lev Telyatinkov, Michael Bronstein, Simone Scardapane, Paolo Di Lorenzo|[here](https://iclr.cc/virtual/2024/poster/19619)

|Biased Temporal Convolution Graph Network for Time Series Forecasting with Missing Values.|Xiaodan Chen, Xiucheng Li, Bo Liu, Zhijun Li|[here](https://iclr.cc/virtual/2024/poster/18754)

|InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior|Chenguo Lin, Yadong MU|[here](https://iclr.cc/virtual/2024/poster/18845)

|A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks|Jintang Li, Huizhe Zhang, Ruofan Wu, Zulun Zhu, Baokun Wang, Changhua Meng, Zibin Zheng, Liang Chen|[here](https://iclr.cc/virtual/2024/poster/18850)

|HiGen: Hierarchical Graph Generative Networks|Mahdi Karami|[here](https://iclr.cc/virtual/2024/poster/18909)

|Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-Image Generation|Jaemin Cho, Yushi Hu, Jason Baldridge, Roopal Garg, Peter Anderson, Ranjay Krishna, Mohit Bansal, Jordi Pont-Tuset, Su Wang|[here](https://iclr.cc/virtual/2024/poster/18963)

|GraphGuard: Provably Robust Graph Classification against Adversarial Attacks|Zaishuo Xia, Han Yang, Binghui Wang, Jinyuan Jia|[here](https://iclr.cc/virtual/2024/poster/18970)

|Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness|Bohang Zhang, Jingchu Gai, Yiheng Du, Qiwei Ye, Di He, Liwei Wang|[here](https://iclr.cc/virtual/2024/poster/18994)

|HoloNets: Spectral Convolutions do extend to Directed Graphs|Christian Koke, Daniel Cremers|[here](https://iclr.cc/virtual/2024/poster/19097)

|Rethinking Label Poisoning for GNNs: Pitfalls and Attacks|Vijay Chandra Lingam, Mohammad Sadegh Akhondzadeh, Aleksandar Bojchevski|[here](https://iclr.cc/virtual/2024/poster/18946)

|Long-range Neural Atom Learning for Molecular Graphs|Xuan Li, Zhanke Zhou, Jiangchao Yao, Yu Rong, Lu Zhang, Bo Han|[here](https://iclr.cc/virtual/2024/poster/19172)

|Graph-based Virtual Sensing from Sparse and Partial Multivariate Observations|Giovanni De Felice, Andrea Cini, Daniele Zambon, Vladimir Gusev, Cesare Alippi|[here](https://iclr.cc/virtual/2024/poster/19185)

|Complete and Efficient Graph Transformers for Crystal Material Property Prediction|Keqiang Yan, Cong Fu, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji|[here](https://iclr.cc/virtual/2024/poster/19203)

|Forward Learning of Graph Neural Networks|Namyong Park, Xing Wang, Antoine Simoulin, Shuai Yang, Grey Yang, Ryan Rossi, Puja Trivedi, Nesreen Ahmed|[here](https://iclr.cc/virtual/2024/poster/19252)

|Causality-Inspired Spatial-Temporal Explanations for Dynamic Graph Neural Networks|Kesen Zhao, Liang Zhang|[here](https://iclr.cc/virtual/2024/poster/19267)

|Local Graph Clustering with Noisy Labels|Artur Back de Luca, Kimon Fountoulakis, Shenghao Yang|[here](https://iclr.cc/virtual/2024/poster/19337)

|Rethinking and Extending the Probabilistic Inference Capacity of GNNs|Tuo Xu, Lei Zou|[here](https://iclr.cc/virtual/2024/poster/19345)

|Robust Angular Synchronization via Directed Graph Neural Networks|Yixuan He, Gesine Reinert, David Wipf, Mihai Cucuringu|[here](https://iclr.cc/virtual/2024/poster/19411)

|TEDDY: Trimming Edges with Degree-based Graph Diffusion Strategy|Hyunjin Seo, Jihun Yun, Eunho Yang|[here](https://iclr.cc/virtual/2024/poster/19426)

|Locality-Aware Graph Rewiring in GNNs|Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, Francesco Di Giovanni|[here](https://iclr.cc/virtual/2024/poster/19465)

|Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection|Xiangyu Dong, Xingyi Zhang, Sibo WANG|[here](https://iclr.cc/virtual/2024/poster/19466)

|BroGNet: Momentum-Conserving Graph Neural Stochastic Differential Equation for Learning Brownian Dynamics|Suresh Bishnoi, Jayadeva Jayadeva, Sayan Ranu, N. M. Anoop Krishnan|[here](https://iclr.cc/virtual/2024/poster/19540)

|Efficient and Scalable Graph Generation through Iterative Local Expansion|Andreas Bergmeister, Karolis Martinkus, Nathanaël Perraudin, Roger Wattenhofer|[here](https://iclr.cc/virtual/2024/poster/19545)

|GOAt: Explaining Graph Neural Networks via Graph Output Attribution|Shengyao Lu, Keith G Mills, Jiao He, Bang Liu, Di Niu|[here](https://iclr.cc/virtual/2024/poster/19551)

|Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials|Ivan Grega, Ilyes Batatia, Gábor Csányi, Sri Karlapati, Vikram Deshpande|[here](https://iclr.cc/virtual/2024/poster/17634)

|A Poincaré Inequality and Consistency Results for Signal Sampling on Large Graphs|Thien Le, Luana Ruiz, Stefanie Jegelka|[here](https://iclr.cc/virtual/2024/poster/17949)

|Transformers vs. Message Passing GNNs: Distinguished in Uniform|Jan Tönshoff, Eran Rosenbluth, Martin Ritzert, Berke Kisin, Martin Grohe|[here](https://iclr.cc/virtual/2024/poster/19249)

|Consistency Training with Learnable Data Augmentation for Graph Anomaly Detection with Limited Supervision|Nan Chen, Zemin Liu, Bryan Hooi, Bingsheng He, Rizal Fathony, Jun Hu, Jia Chen|[here](https://iclr.cc/virtual/2024/poster/18209)

|LightHGNN: Distilling Hypergraph Neural Networks into MLPs for 100x Faster Inference|Yifan Feng, Yihe Luo, Shihui Ying, Yue Gao|[here](https://iclr.cc/virtual/2024/poster/17940)

|Hypergraph Dynamic System|Jielong Yan, Yifan Feng, Shihui Ying, Yue Gao|[here](https://iclr.cc/virtual/2024/poster/18791)

|Self-Supervised Heterogeneous Graph Learning:  a Homophily and Heterogeneity View|YUJIE MO, Feiping Nie, Ping Hu, Heng Tao Shen, Zheng Zhang, Xinchao Wang, Xiaofeng Zhu|[here](https://iclr.cc/virtual/2024/poster/19523)

|Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs|Milan Papez, Martin Rektoris, Tomáš Pevný, Vaclav Smidl|[here](https://iclr.cc/virtual/2024/poster/17901)

|Label-free Node Classification on Graphs with Large Language Models (LLMs)|Zhikai Chen, Haitao Mao, Hongzhi Wen, Haoyu Han, Wei Jin, Haiyang Zhang, Hui Liu, Jiliang Tang|[here](https://iclr.cc/virtual/2024/poster/18104)

|Mayfly: a Neural Data Structure for Graph Stream Summarization|yuan feng, Yukun Cao, Hairu Wang, Xike Xie, S Kevin Zhou|[here](https://iclr.cc/virtual/2024/poster/17871)

|GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks|Peter Müller, Lukas Faber, Karolis Martinkus, Roger Wattenhofer|[here](https://iclr.cc/virtual/2024/poster/18957)

|Adaptive Self-training Framework for Fine-grained Scene Graph Generation|Kibum Kim, Kanghoon Yoon, Yeonjun In, Jinyoung Moon, Donghyun Kim, Chanyoung Park|[here](https://iclr.cc/virtual/2024/poster/18465)

|Structural Fairness-aware Active Learning for Graph Neural Networks|Haoyu Han, Xiaorui Liu, Li Ma, MohamadAli Torkamani, Hui Liu, Jiliang Tang, Makoto Yamada|[here](https://iclr.cc/virtual/2024/poster/18317)

|Learning Adaptive Multiresolution Transforms via Meta-Framelet-based Graph Convolutional Network|Tianze Luo, Zhanfeng Mo, Sinno Pan|[here](https://iclr.cc/virtual/2024/poster/19425)

|VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition|Chenyu Liu, XINLIANG ZHOU, Zhengri Zhu, Liming Zhai, Ziyu Jia, Yang Liu|[here](https://iclr.cc/virtual/2024/poster/19115)

|Online GNN Evaluation Under Test-time Graph Distribution Shifts|Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du, Shirui Pan|[here](https://iclr.cc/virtual/2024/poster/18895)

|Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability|Zehao Dong, Muhan Zhang, Philip Payne, Michael Province, Carlos Cruchaga, Tianyu Zhao, Fuhai Li, Yixin Chen|[here](https://iclr.cc/virtual/2024/poster/17857)

|Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach|Christian Fabian, Kai Cui, Heinz Koeppl|[here](https://iclr.cc/virtual/2024/poster/17367)

|VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs|Ling Yang, Ye Tian, Minkai Xu, Zhongyi Liu, Shenda Hong, Wei Qu, Wentao Zhang, Bin CUI, Muhan Zhang, Jure Leskovec|[here](https://iclr.cc/virtual/2024/poster/18114)

|Latent 3D Graph Diffusion|Yuning You, Ruida Zhou, Jiwoong Park, Haotian Xu, Chao Tian, Zhangyang Wang, Yang Shen|[here](https://iclr.cc/virtual/2024/poster/18292)

|Graph Neural Networks for Learning Equivariant Representations of Neural Networks|Miltiadis (Miltos) Kofinas, Boris Knyazev, Yan Zhang, Yunlu Chen, Gertjan J Burghouts, Efstratios Gavves, Cees G Snoek, David Zhang|[here](https://iclr.cc/virtual/2024/poster/17821)

|Graph Generation with  $K^2$-trees|Yunhui Jang, Dongwoo Kim, Sungsoo Ahn|[here](https://iclr.cc/virtual/2024/poster/18652)

|M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering|Jiaxin Lu, Zetian Jiang, Tianzhe Wang, Junchi Yan|[here](https://iclr.cc/virtual/2024/poster/19259)

|NP-GL: Extending Power of Nature from Binary Problems to Real-World Graph Learning|Chunshu Wu, Ruibing Song, Chuan Liu, Yunan Yang, Ang Li, Michael Huang, Tong Geng|[here](https://iclr.cc/virtual/2024/poster/17731)

|Clifford Group Equivariant Simplicial Message Passing Networks|Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forré|[here](https://iclr.cc/virtual/2024/poster/18381)

|FreeDyG: Frequency Enhanced Continuous-Time Dynamic Graph Model for Link Prediction|Yuxing Tian, Yiyan Qi, Fan Guo|[here](https://iclr.cc/virtual/2024/poster/19341)

|MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy|Yan Sun, Jicong Fan|[here](https://iclr.cc/virtual/2024/poster/19024)

|Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoning|Linhao Luo, Yuan-Fang Li, Reza Haffari, Shirui Pan|[here](https://iclr.cc/virtual/2024/poster/18404)

|Revisiting Link Prediction: a data perspective|Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang|[here](https://iclr.cc/virtual/2024/poster/19325)

|Mixture of Weak and Strong Experts on Graphs|Hanqing Zeng, Hanjia Lyu, Diyi Hu, Yinglong Xia, Jiebo Luo|[here](https://iclr.cc/virtual/2024/poster/17490)

|VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections|Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long|[here](https://iclr.cc/virtual/2024/poster/18601)

|Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature Attacks|Yassine ABBAHADDOU, Sofiane ENNADIR, Johannes Lutzeyer, Michalis Vazirgiannis, Henrik Boström|[here](https://iclr.cc/virtual/2024/poster/19134)

|Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning|Xiaoxin He, Xavier Bresson, Thomas Laurent, Adam Perold, Yann LeCun, Bryan Hooi|[here](https://iclr.cc/virtual/2024/poster/18640)

|Mitigating Severe Robustness Degradation on Graphs|Xiangchi Yuan, Chunhui Zhang, Yijun Tian, Yanfang Ye, Chuxu Zhang|[here](https://iclr.cc/virtual/2024/poster/18886)

|Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph|Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel Ni, Heung-Yeung Shum, Jian Guo|[here](https://iclr.cc/virtual/2024/poster/17844)

|Efficient Sharpness-Aware Minimization for Molecular Graph Transformer Models|Yili Wang, Kaixiong Zhou, Ninghao Liu, Ying Wang, Xin Wang|[here](https://iclr.cc/virtual/2024/poster/18737)

|Orbit-Equivariant Graph Neural Networks|Matthew Morris, Bernardo Grau, Ian Horrocks|[here](https://iclr.cc/virtual/2024/poster/19019)

|Encoding Unitig-level Assembly Graphs with Heterophilous Constraints for Metagenomic Contigs Binning|Hansheng Xue, Vijini Mallawaarachchi, Lexing Xie, Vaibhav Rajan|[here](https://iclr.cc/virtual/2024/poster/17546)

|Boosting Graph Anomaly Detection with Adaptive Message Passing|Jingyan Chen, Guanghui Zhu, Chunfeng Yuan, Yihua Huang|[here](https://iclr.cc/virtual/2024/poster/19168)

|Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FROND|Qiyu Kang, Kai Zhao, Qinxu Ding, Feng Ji, Xuhao Li, Wenfei Liang, Yang Song, Wee Peng Tay|[here](https://iclr.cc/virtual/2024/poster/17486)

|Temporal Generalization Estimation in Evolving Graphs|Bin Lu, Tingyan Ma, Xiaoying Gan, Xinbing Wang, Yunqiang Zhu, Chenghu Zhou, Shiyu Liang|[here](https://iclr.cc/virtual/2024/poster/19001)

|Rethinking the Benefits of Steerable Features in 3D Equivariant Graph Neural Networks|Shih-Hsin Wang, Yung-Chang Hsu, Justin Baker, Andrea Bertozzi, Jack Xin, Bao Wang|[here](https://iclr.cc/virtual/2024/poster/17900)

|InterpGNN: Understand and Improve Generalization Ability of Transdutive GNNs through the Lens of Interplay between Train and Test Nodes|Jiawei Sun, Kailai Li, Ruoxin Chen, Jie LI, Chentao Wu, Yue Ding, Junchi Yan|[here](https://iclr.cc/virtual/2024/poster/17760)

|Graph Lottery Ticket Automated|Guibin Zhang, Kun Wang, Wei Huang, Yanwei Yue, Yang Wang, Roger Zimmermann, Aojun Zhou, Dawei Cheng, Jin Zeng, Yuxuan Liang|[here](https://iclr.cc/virtual/2024/poster/17845)

|Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learning|Mirco Mutti, Riccardo De Santi, Marcello Restelli, Alexander Marx, Giorgia Ramponi|[here](https://iclr.cc/virtual/2024/poster/18841)

|Partitioning Message Passing for Graph Fraud Detection|Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen|[here](https://iclr.cc/virtual/2024/poster/17621)

|Variance-enlarged Poisson Learning for Graph-based Semi-Supervised Learning with Extremely Sparse Labeled Data|Xiong Zhou, Xianming Liu, Hao Yu, Jialiang Wang, Zeke Xie, Junjun Jiang, Xiangyang Ji|[here](https://iclr.cc/virtual/2024/poster/17410)

|On the Power of the Weisfeiler-Leman Test for Graph Motif Parameters|Matthias Lanzinger, Pablo Barcelo|[here](https://iclr.cc/virtual/2024/poster/19057)

|Boosting the Adversarial Robustness of Graph Neural Networks: An OOD Perspective|Kuan Li, YiWen Chen, Yang Liu, Jin Wang, QING HE, Minhao Cheng, Xiang Ao|[here](https://iclr.cc/virtual/2024/poster/19149)

|A Topological Perspective on Demystifying GNN-Based Link Prediction Performance|Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr|[here](https://iclr.cc/virtual/2024/poster/18354)

|Hybrid Directional Graph Neural Network for Molecules|Junyi An, Chao Qu, Zhipeng Zhou, Fenglei Cao, Xu Yinghui, Yuan Qi, Furao Shen|[here](https://iclr.cc/virtual/2024/poster/19228)

|Mirage: Model-agnostic Graph Distillation for Graph Classification|Mridul Gupta, Sahil Manchanda, HARIPRASAD KODAMANA, Sayan Ranu|[here](https://iclr.cc/virtual/2024/poster/19373)

|PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters|Jingyu Chen, Runlin Lei, Zhewei Wei|[here](https://iclr.cc/virtual/2024/poster/17428)

|StructComp: Substituting propagation with Structural Compression in Training Graph Contrastive Learning|Shengzhong Zhang, Wenjie Yang, Xinyuan Cao, Hongwei Zhang, Zengfeng Huang|[here](https://iclr.cc/virtual/2024/poster/18378)

|GTMGC: Using Graph Transformer to Predict Molecule’s Ground-State Conformation|Guikun Xu, Yongquan Jiang, PengChuan Lei, Yan Yang, Jim Chen|[here](https://iclr.cc/virtual/2024/poster/19080)