LoG 2022

56 papers

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

A Simple Way to Learn Metrics Between Attributed Graphs Yacouba Kaloga, Pierre Borgnat, Amaury Habrard

A Survey on Deep Graph Generation: Methods and Applications Yanqiao Zhu, Yuanqi Du, Yinkai Wang, Yichen Xu, Jieyu Zhang, Qiang Liu, Shu Wu

A Systematic Evaluation of Node Embedding Robustness Alexandru Cristian Mara, Jefrey Lijffijt, Stephan Günnemann, Tijl De Bie

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

CEP3: Community Event Prediction with Neural Point Process on Graph Xuhong Wang, Sirui Chen, Yixuan He, Minjie Wang, Quan Gan, Junchi Yan

Combining Graph and Recurrent Networks for Efficient and Effective Segment Tagging David Montero, Javier Yebes

Continuous Neural Algorithmic Planners Yu He, Petar Veličković, Pietro Lio, Andreea Deac

DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series Jase Clarkson, Mihai Cucuringu, Andrew Elliott, Gesine Reinert

De Bruijn Goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs Lisi Qarkaxhija, Vincenzo Perri, Ingo Scholtes

DiffWire: Inductive Graph Rewiring via the Lovász Bound Adrián Arnaiz-Rodrı́guez, Ahmed Begga, Francisco Escolano, Nuria M Oliver

DIGRAC: Digraph Clustering Based on Flow Imbalance Yixuan He, Gesine Reinert, Mihai Cucuringu

Distributed Representations of Graphs for Drug Pair Scoring Paul Scherer, Pietro Lio, Mateja Jamnik

Dynamic Network Reconfiguration for Entropy Maximization Using Deep Reinforcement Learning Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

Effective Higher-Order Link Prediction and Reconstruction from Simplicial Complex Embeddings Simone Piaggesi, André Panisson, Giovanni Petri

Efficient Representation Learning for Higher-Order Data with Simplicial Complexes Ruochen Yang, Frederic Sala, Paul Bogdan

Expander Graph Propagation Andreea Deac, Marc Lackenby, Petar Veličković

FakeEdge: Alleviate Dataset Shift in Link Prediction Kaiwen Dong, Yijun Tian, Zhichun Guo, Yang Yang, Nitesh Chawla

Flashlight: Scalable Link Prediction with Effective Decoders Yiwei Wang, Bryan Hooi, Yozen Liu, Tong Zhao, Zhichun Guo, Neil Shah

GARNET: Reduced-Rank Topology Learning for Robust and Scalable Graph Neural Networks Chenhui Deng, Xiuyu Li, Zhuo Feng, Zhiru Zhang

GEFL: Extended Filtration Learning for Graph Classification Simon Zhang, Soham Mukherjee, Tamal K. Dey

Gradual Weisfeiler-Leman: Slow and Steady Wins the Race Franka Bause, Nils Morten Kriege

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

Graph Neural Network with Local Frame for Molecular Potential Energy Surface Xiyuan Wang, Muhan Zhang

Graph-Time Convolutional Autoencoders Mohammad Sabbaqi, Riccardo Taormina, Alan Hanjalic, Elvin Isufi

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

Influence-Based Mini-Batching for Graph Neural Networks Johannes Gasteiger, Chendi Qian, Stephan Günnemann

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

Label-Wise Graph Convolutional Network for Heterophilic Graphs Enyan Dai, Shijie Zhou, Zhimeng Guo, Suhang Wang

Learnable Commutative Monoids for Graph Neural Networks Euan Ong, Petar Veličković

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

Learning Graph Search Heuristics Michal Pándy, Weikang Qiu, Gabriele Corso, Petar Veličković, Zhitao Ying, Jure Leskovec, Pietro Lio

Metric Based Few-Shot Graph Classification Donato Crisostomi, Simone Antonelli, Valentino Maiorca, Luca Moschella, Riccardo Marin, Emanuele Rodolà

MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian Yixuan He, Michael Perlmutter, Gesine Reinert, Mihai Cucuringu

Neighborhood-Aware Scalable Temporal Network Representation Learning Yuhong Luo, Pan Li

Neural Graph Databases Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler

Neural Graph Modelling of Whole Slide Images for Survival Ranking Callum Christopher Mackenzie, Muhammad Dawood, Simon Graham, Mark Eastwood, Fayyaz Amir Afsar Minhas

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

PatchGT: Transformer over Non-Trainable Clusters for Learning Graph Representations Han Gao, Xu Han, Jiaoyang Huang, Jian-Xun Wang, Liping Liu

Piecewise-Velocity Model for Learning Continuous-Time Dynamic Node Representations Abdulkadir Celikkanat, Nikolaos Nakis, Morten Mørup

Pruning Edges and Gradients to Learn Hypergraphs from Larger Sets David W Zhang, Gertjan J. Burghouts, Cees G. M. Snoek

Reasoning-Modulated Representations Petar Veličković, Matko Bošnjak, Thomas Kipf, Alexander Lerchner, Raia Hadsell, Razvan Pascanu, Charles Blundell

Representation Learning on Biomolecular Structures Using Equivariant Graph Attention Tuan Le, Frank Noe, Djork-Arné Clevert

ScatterSample: Diversified Label Sampling for Data Efficient Graph Neural Network Learning Zhenwei Dai, Vasileios Ioannidis, Soji Adeshina, Zak Jost, Christos Faloutsos, George Karypis

Shortest Path Networks for Graph Property Prediction Ralph Abboud, Radoslav Dimitrov, Ismail Ilkan Ceylan

Similarity-Based Link Prediction from Modular Compression of Network Flows Christopher Blöcker, Jelena Smiljanić, Ingo Scholtes, Martin Rosvall

Sparse and Local Networks for Hypergraph Reasoning Guangxuan Xiao, Leslie Pack Kaelbling, Jiajun Wu, Jiayuan Mao

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

TopoImb: Toward Topology-Level Imbalance in Learning from Graphs Tianxiang Zhao, Dongsheng Luo, Xiang Zhang, Suhang Wang

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

Towards Training GNNs Using Explanation Directed Message Passing Valentina Giunchiglia, Chirag Varun Shukla, Guadalupe Gonzalez, Chirag Agarwal

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

Transfer Learning Using Spectral Convolutional Autoencoders on Semi-Regular Surface Meshes Sara Hahner, Felix Kerkhoff, Jochen Garcke

Weisfeiler and Leman Go Relational Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth

Well-Conditioned Spectral Transforms for Dynamic Graph Representation Bingxin Zhou, Xinliang Liu, Yuehua Liu, Yunying Huang, Pietro Lio, Yu Guang Wang

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