[edit]
[edit]
Editors: Bastian Rieck, Razvan Pascanu
The First Learning on Graphs Conference: Preface
Bastian Rieck, Razvan Pascanu, Yuanqi Du, Hannes Stärk, Derek Lim, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Gabriele Corso, Leonardo Cotta, Yanqiao Zhu, Kexin Huang, Michelle Li, Sofia Bourhim, Ilia Igashov; Proceedings of the First Learning on Graphs Conference, PMLR 198:i-xxiii
[abs][Download PDF]
A Generalist Neural Algorithmic Learner
Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Joseph Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković; Proceedings of the First Learning on Graphs Conference, PMLR 198:2:1-2:23
GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks
Chenhui Deng, Xiuyu Li, Zhuo Feng, Zhiru Zhang; Proceedings of the First Learning on Graphs Conference, PMLR 198:3:1-3:23
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu; Proceedings of the First Learning on Graphs Conference, PMLR 198:4:1-4:21
Shortest Path Networks for Graph Property Prediction
Ralph Abboud, Radoslav Dimitrov, Ismail Ilkan Ceylan; Proceedings of the First Learning on Graphs Conference, PMLR 198:5:1-5:25
Taxonomy of Benchmarks in Graph Representation Learning
Renming Liu, Semih Cantürk, Frederik Wenkel, Sarah McGuire, Xinyi Wang, Anna Little, Leslie O’ Bray, Michael Perlmutter, Bastian Rieck, Matthew Hirn, Guy Wolf, Ladislav Rampášek; Proceedings of the First Learning on Graphs Conference, PMLR 198:6:1-6:25
Graph Learning Indexer: A Contributor-Friendly and Metadata-Rich Platform for Graph Learning Benchmarks
Jiaqi Ma, Xingjian Zhang, Hezheng Fan, Jin Huang, Tianyue Li, Ting Wei Li, Yiwen Tu, Chenshu Zhu, Qiaozhu Mei; Proceedings of the First Learning on Graphs Conference, PMLR 198:7:1-7:23
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang, Vlado Menkovski, Jiaxu Zhao, Lu Yin, Yulong Pei, Decebal Constantin Mocanu, Zhangyang Wang, Mykola Pechenizkiy, Shiwei Liu; Proceedings of the First Learning on Graphs Conference, PMLR 198:8:1-8:17
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
Influence-Based Mini-Batching for Graph Neural Networks
Johannes Gasteiger, Chendi Qian, Stephan Günnemann; Proceedings of the First Learning on Graphs Conference, PMLR 198:9:1-9:19
Learning Graph Search Heuristics
Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Zhitao Ying, Jure Leskovec, Pietro Lio; Proceedings of the First Learning on Graphs Conference, PMLR 198:10:1-10:13
On the Unreasonable Effectiveness of Feature Propagation in Learning on Graphs With Missing Node Features
Emanuele Rossi, Henry Kenlay, Maria I. Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, Michael M. Bronstein; Proceedings of the First Learning on Graphs Conference, PMLR 198:11:1-11:16
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
Well-Conditioned Spectral Transforms for Dynamic Graph Representation
Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Lio, Yu Guang Wang; Proceedings of the First Learning on Graphs Conference, PMLR 198:12:1-12:19
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
Efficient Representation Learning for Higher-Order Data With Simplicial Complexes
Ruochen Yang, Frederic Sala, Paul Bogdan; Proceedings of the First Learning on Graphs Conference, PMLR 198:13:1-13:21
Flashlight: Scalable Link Prediction With Effective Decoders
Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah; Proceedings of the First Learning on Graphs Conference, PMLR 198:14:1-14:17
DiffWire: Inductive Graph Rewiring via the Lovász Bound
Adrián Arnaiz-Rodrı́guez, Ahmed Begga, Francisco Escolano, Nuria M Oliver; Proceedings of the First Learning on Graphs Conference, PMLR 198:15:1-15:27
Towards Efficient and Expressive GNNs for Graph Classification via Subgraph-Aware Weisfeiler-Lehman
Zhaohui Wang, Qi Cao, Huawei Shen, Xu Bingbing, Muhan Zhang, Xueqi Cheng; Proceedings of the First Learning on Graphs Conference, PMLR 198:17:1-17:18
Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes
Sara Hahner, Felix Kerkhoff, Jochen Garcke; Proceedings of the First Learning on Graphs Conference, PMLR 198:18:1-18:19
Graph Neural Network With Local Frame for Molecular Potential Energy Surface
Xiyuan Wang, Muhan Zhang; Proceedings of the First Learning on Graphs Conference, PMLR 198:19:1-19:30
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
Franka Bause, Nils Morten Kriege; Proceedings of the First Learning on Graphs Conference, PMLR 198:20:1-20:18
DIGRAC: Digraph Clustering Based on Flow Imbalance
Yixuan He, Gesine Reinert, Mihai Cucuringu; Proceedings of the First Learning on Graphs Conference, PMLR 198:21:1-21:43
DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series
Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert; Proceedings of the First Learning on Graphs Conference, PMLR 198:23:1-23:19
Graph-Time Convolutional Autoencoders
Mohammad Sabbaqi, Riccardo Taormina, Alan Hanjalic, Elvin Isufi; Proceedings of the First Learning on Graphs Conference, PMLR 198:24:1-24:20
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang; Proceedings of the First Learning on Graphs Conference, PMLR 198:26:1-26:21
PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations
Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu; Proceedings of the First Learning on Graphs Conference, PMLR 198:27:1-27:25
Towards Training GNNs Using Explanation Directed Message Passing
Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal; Proceedings of the First Learning on Graphs Conference, PMLR 198:28:1-28:18
Jointly Modelling Uncertainty and Diversity for Active Molecular Property Prediction
Kuangqi Zhou, Kaixin Wang, Jian Tang, Jiashi Feng, Bryan Hooi, Peilin Zhao, Tingyang Xu, Xinchao Wang; Proceedings of the First Learning on Graphs Conference, PMLR 198:29:1-29:21
Representation Learning on Biomolecular Structures Using Equivariant Graph Attention
Tuan Le, Frank Noe, Djork-Arné Clevert; Proceedings of the First Learning on Graphs Conference, PMLR 198:30:1-30:17
Neural Graph Databases
Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler; Proceedings of the First Learning on Graphs Conference, PMLR 198:31:1-31:38
AutoGDA: Automated Graph Data Augmentation for Node Classification
Tong Zhao, Xianfeng Tang, Danqing Zhang, Haoming Jiang, Nikhil Rao, Yiwei Song, Pallav Agrawal, Karthik Subbian, Bing Yin, Meng Jiang; Proceedings of the First Learning on Graphs Conference, PMLR 198:32:1-32:17
Metric Based Few-Shot Graph Classification
Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà; Proceedings of the First Learning on Graphs Conference, PMLR 198:33:1-33:22
Sparse and Local Networks for Hypergraph Reasoning
Guangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu, Jiayuan Mao; Proceedings of the First Learning on Graphs Conference, PMLR 198:34:1-34:16
ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning
Zhenwei DAI, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis; Proceedings of the First Learning on Graphs Conference, PMLR 198:35:1-35:15
Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations
Abdulkadir CELIKKANAT, Nikolaos Nakis, Morten Mørup; Proceedings of the First Learning on Graphs Conference, PMLR 198:36:1-36:21
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
TopoImb: Toward Topology-Level Imbalance in Learning From Graphs
Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang; Proceedings of the First Learning on Graphs Conference, PMLR 198:37:1-37:18
Expander Graph Propagation
Andreea Deac, Marc Lackenby, Petar Veličković; Proceedings of the First Learning on Graphs Conference, PMLR 198:38:1-38:18
CEP3: Community Event Prediction With Neural Point Process on Graph
Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Junchi Yan; Proceedings of the First Learning on Graphs Conference, PMLR 198:39:1-39:17
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian
Yixuan He, Michael Perlmutter, Gesine Reinert, Mihai Cucuringu; Proceedings of the First Learning on Graphs Conference, PMLR 198:40:1-40:39
Combining Graph and Recurrent Networks for Efficient and Effective Segment Tagging
David Montero, Javier Yebes; Proceedings of the First Learning on Graphs Conference, PMLR 198:41:1-41:14
A Systematic Evaluation of Node Embedding Robustness
Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie; Proceedings of the First Learning on Graphs Conference, PMLR 198:42:1-42:14
Learnable Commutative Monoids for Graph Neural Networks
Euan Ong, Petar Veličković; Proceedings of the First Learning on Graphs Conference, PMLR 198:43:1-43:22
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks
Kenza Amara, Zhitao Ying, Zitao Zhang, Zhichao Han, Yang Zhao, Yinan Shan, Ulrik Brandes, Sebastian Schemm, Ce Zhang; Proceedings of the First Learning on Graphs Conference, PMLR 198:44:1-44:23
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
Learning Distributed Geometric Koopman Operator for Sparse Networked Dynamical Systems
Sayak Mukherjee, Sai Pushpak Nandanoori, Sheng Guan, Khushbu Agarwal, Subhrajit Sinha, Soumya Kundu, Seemita Pal, Yinghui Wu, Draguna L Vrabie, Sutanay Choudhury; Proceedings of the First Learning on Graphs Conference, PMLR 198:45:1-45:17
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth; Proceedings of the First Learning on Graphs Conference, PMLR 198:46:1-46:26
A Survey on Deep Graph Generation: Methods and Applications
Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu; Proceedings of the First Learning on Graphs Conference, PMLR 198:47:1-47:21
Neural Graph Modelling of Whole Slide Images for Survival Ranking
Callum Christopher Mackenzie, Muhammad Dawood, Simon Graham, Mark Eastwood, Fayyaz ul Amir Afsar Minhas; Proceedings of the First Learning on Graphs Conference, PMLR 198:48:1-48:10
Dynamic Network Reconfiguration for Entropy Maximization Using Deep Reinforcement Learning
Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi; Proceedings of the First Learning on Graphs Conference, PMLR 198:49:1-49:15
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
Reasoning-Modulated Representations
Petar Veličković, Matko Bošnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell; Proceedings of the First Learning on Graphs Conference, PMLR 198:50:1-50:17
De Bruijn Goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs
Lisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes; Proceedings of the First Learning on Graphs Conference, PMLR 198:51:1-51:21
Similarity-Based Link Prediction From Modular Compression of Network Flows
Christopher Blöcker, Jelena Smiljanić, Ingo Scholtes, Martin Rosvall; Proceedings of the First Learning on Graphs Conference, PMLR 198:52:1-52:18
Pruning Edges and Gradients to Learn Hypergraphs From Larger Sets
David W Zhang, Gertjan J. Burghouts, Cees G. M. Snoek; Proceedings of the First Learning on Graphs Conference, PMLR 198:53:1-53:18
Continuous Neural Algorithmic Planners
Yu He, Petar Veličković, Pietro Lio, Andreea Deac; Proceedings of the First Learning on Graphs Conference, PMLR 198:54:1-54:13
Effective Higher-Order Link Prediction and Reconstruction From Simplicial Complex Embeddings
Simone Piaggesi, André Panisson, Giovanni Petri; Proceedings of the First Learning on Graphs Conference, PMLR 198:55:1-55:17
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh Chawla; Proceedings of the First Learning on Graphs Conference, PMLR 198:56:1-56:19
[abs][Download PDF][OpenReview][Software][Supplementary ZIP]
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。