Table of Contents

• The First Learning on Graphs Conference: PrefaceBastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu 0001, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov. [doi]
• Neighborhood-Aware Scalable Temporal Network Representation LearningYuhong Luo, Pan Li. 1 [doi]
• A Generalist Neural Algorithmic LearnerBorja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bosnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Velickovic. 2 [doi]
• GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural NetworksChenhui Deng, Xiuyu Li, Zhuo Feng, Zhiru Zhang. 3 [doi]
• Transductive Linear Probing: A Novel Framework for Few-Shot Node ClassificationZhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu 0001. 4 [doi]
• Shortest Path Networks for Graph Property PredictionRalph Abboud, Radoslav Dimitrov, Ismail Ilkan Ceylan. 5 [doi]
• Taxonomy of Benchmarks in Graph Representation LearningRenming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O'Bray, Michael Perlmutter, Bastian Rieck, Matthew J. Hirn, Guy Wolf, Ladislav Rampásek. 6 [doi]
• Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning BenchmarksJiaqi Ma 0001, Xingjian Zhang, Hezheng Fan, Jin Huang, Tianyue Li, Ting-Wei Li, Yiwen Tu, Chenshu Zhu, Qiaozhu Mei. 7 [doi]
• You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs TicketsTianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin 0006, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu. 8 [doi]
• Influence-Based Mini-Batching for Graph Neural NetworksJohannes Gasteiger, Chendi Qian, Stephan Günnemann. 9 [doi]
• Learning Graph Search HeuristicsMichal Pándy, Weikang Qiu, Gabriele Corso, Petar Velickovic, Zhitao Ying, Jure Leskovec, Pietro Liò. 10 [doi]
• On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node FeaturesEmanuele Rossi, Henry Kenlay, Maria I. Gorinova 0001, Benjamin Paul Chamberlain, Xiaowen Dong 0001, Michael M. Bronstein. 11 [doi]
• Well-Conditioned Spectral Transforms for Dynamic Graph RepresentationBingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Liò, Yu Guang Wang. 12 [doi]
• Efficient Representation Learning for Higher-Order Data With Simplicial ComplexesRuochen Yang, Frederic Sala, Paul Bogdan. 13 [doi]
• Flashlight: Scalable Link Prediction With Effective DecodersYiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao 0003, Zhichun Guo, Neil Shah. 14 [doi]
• DiffWire: Inductive Graph Rewiring via the Lovász BoundAdrián Arnaiz-Rodríguez, Ahmed Begga, Francisco Escolano, Nuria Oliver. 15 [doi]
• GEFL: Extended Filtration Learning for Graph ClassificationSimon Zhang, Soham Mukherjee, Tamal K. Dey. 16 [doi]
• Towards Efficient and Expressive GNNs for Graph Classification via Subgraph-Aware Weisfeiler-LehmanZhaohui Wang, Qi Cao, Huawei Shen, Bingbing Xu, Muhan Zhang, Xueqi Cheng. 17 [doi]
• Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface MeshesSara Hahner, Felix Kerkhoff, Jochen Garcke. 18 [doi]
• Graph Neural Network With Local Frame for Molecular Potential Energy SurfaceXiyuan Wang, Muhan Zhang. 19 [doi]
• Gradual Weisfeiler-Leman: Slow and Steady Wins the RaceFranka Bause, Nils Morten Kriege. 20 [doi]
• DIGRAC: Digraph Clustering Based on Flow ImbalanceYixuan He, Gesine Reinert, Mihai Cucuringu. 21 [doi]
• Distributed Representations of Graphs for Drug Pair ScoringPaul Scherer, Pietro Liò, Mateja Jamnik. 22 [doi]
• DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time SeriesJase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert. 23 [doi]
• Graph-Time Convolutional AutoencodersMohammad Sabbaqi, Riccardo Taormina, Alan Hanjalic, Elvin Isufi. 24 [doi]
• A Simple Way to Learn Metrics Between Attributed GraphsYacouba Kaloga, Pierre Borgnat, Amaury Habrard. 25 [doi]
• Label-Wise Graph Convolutional Network for Heterophilic GraphsEnyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang. 26 [doi]
• PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph RepresentationsHan Gao 0005, Xu Han, Jiaoyang Huang, Jian Xun Wang, Liping Liu 0001. 27 [doi]
• Towards Training GNNs Using Explanation Directed Message PassingValentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal. 28 [doi]
• Jointly Modelling Uncertainty and Diversity for Active Molecular Property PredictionKuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang. 29 [doi]
• Representation Learning on Biomolecular Structures Using Equivariant Graph AttentionTuan Le, Frank Noé, Djork-Arné Clevert. 30 [doi]
• Neural Graph DatabasesMaciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler. 31 [doi]
• AutoGDA: Automated Graph Data Augmentation for Node ClassificationTong Zhao, Xianfeng Tang, Danqing Zhang, Haoming Jiang, Nikhil Rao 0001, Yiwei Song, Pallav Agrawal, Karthik Subbian, Bing Yin, Meng Jiang. 32 [doi]
• Metric Based Few-Shot Graph ClassificationDonato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà. 33 [doi]
• Sparse and Local Networks for Hypergraph ReasoningGuangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu 0001, Jiayuan Mao. 34 [doi]
• ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network LearningZhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis. 35 [doi]
• Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node RepresentationsAbdulkadir Çelikkanat, Nikolaos Nakis, Morten Mørup. 36 [doi]
• TopoImb: Toward Topology-Level Imbalance in Learning From GraphsTianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang. 37 [doi]
• Expander Graph PropagationAndreea Deac, Marc Lackenby, Petar Velickovic. 38 [doi]
• CEP3: Community Event Prediction With Neural Point Process on GraphXuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Junchi Yan. 39 [doi]
• MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed LaplacianYixuan He, Michael Perlmutter, Gesine Reinert, Mihai Cucuringu. 40 [doi]
• Combining Graph and Recurrent Networks for Efficient and Effective Segment TaggingDavid Montero, J. Javier Yebes. 41 [doi]
• A Systematic Evaluation of Node Embedding RobustnessAlexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie. 42 [doi]
• Learnable Commutative Monoids for Graph Neural NetworksEuan Ong, Petar Velickovic. 43 [doi]
• GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural NetworksKenza Amara, Zhitao Ying, Zitao Zhang, Zhichao Han, Yang Zhao, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang 0001. 44 [doi]
• Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical SystemsSayak Mukherjee, Sai Pushpak Nandanoori, Sheng Guan, Khushbu Agarwal, Subhrajit Sinha, Soumya Kundu, Seemita Pal, Yinghui Wu, Draguna L. Vrabie, Sutanay Choudhury. 45 [doi]
• Weisfeiler and Leman Go RelationalPablo Barceló, Mikhail Galkin 0001, Christopher Morris 0001, Miguel A. Romero Orth. 46 [doi]
• A Survey on Deep Graph Generation: Methods and ApplicationsYanqiao Zhu 0001, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu. 47 [doi]
• Neural Graph Modelling of Whole Slide Images for Survival RankingCallum Christopher Mackenzie, Muhammad Dawood, Simon Graham, Mark Eastwood, Fayyaz ul Amir Afsar Minhas. 48 [doi]
• Dynamic Network Reconfiguration for Entropy Maximization Using Deep Reinforcement LearningChristoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi. 49 [doi]
• Reasoning-Modulated RepresentationsPetar Velickovic, Matko Bosnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell. 50 [doi]
• De Bruijn Goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic GraphsLisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes. 51 [doi]
• Similarity-Based Link Prediction From Modular Compression of Network FlowsChristopher Blöcker, Jelena Smiljanic, Ingo Scholtes, Martin Rosvall. 52 [doi]
• Pruning Edges and Gradients to Learn Hypergraphs From Larger SetsDavid W. Zhang, Gertjan J. Burghouts, Cees G. M. Snoek. 53 [doi]
• Continuous Neural Algorithmic PlannersYu He, Petar Velickovic, Pietro Liò, Andreea Deac. 54 [doi]
• Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex EmbeddingsSimone Piaggesi, André Panisson, Giovanni Petri. 55 [doi]
• FakeEdge: Alleviate Dataset Shift in Link PredictionKaiwen Dong, Yijun Tian 0001, Zhichun Guo, Yang Yang 0008, Nitesh V. Chawla. 56 [doi]