Recipe for a General, Powerful, Scalable Graph Transformer

25 May 2022

Ladislav Rampášek

Mikhail Galkin

Vijay Prakash Dwivedi

A. Luu

Guy Wolf

Dominique Beaini

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Papers citing "Recipe for a General, Powerful, Scalable Graph Transformer"

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Title
Exploring Sparsity in Graph Transformers Chuang Liu Yibing Zhan Xueqi Ma Liang Ding Dapeng Tao Jia Wu Wenbin Hu Bo Du 34 6 0 09 Dec 2023
Graph Metanetworks for Processing Diverse Neural Architectures Derek Lim Haggai Maron Marc T. Law Jonathan Lorraine James Lucas AI4CE 34 31 0 07 Dec 2023
Graph Convolutions Enrich the Self-Attention in Transformers! Jeongwhan Choi Hyowon Wi Jayoung Kim Yehjin Shin Kookjin Lee Nathaniel Trask Noseong Park 32 4 0 07 Dec 2023
Large Language Models on Graphs: A Comprehensive Survey Bowen Jin Gang Liu Chi Han Meng Jiang Heng Ji Jiawei Han AI4CE 37 138 0 05 Dec 2023
Expressive Sign Equivariant Networks for Spectral Geometric Learning Derek Lim Joshua Robinson Stefanie Jegelka Haggai Maron 76 16 0 04 Dec 2023
Recurrent Distance Filtering for Graph Representation Learning Yuhui Ding Antonio Orvieto Bobby He Thomas Hofmann GNN 36 6 0 03 Dec 2023
Effective Structural Encodings via Local Curvature Profiles Lukas Fesser Melanie Weber 33 3 0 24 Nov 2023
Learning to Optimise Wind Farms with Graph Transformers Siyi Li Arnaud Robert A. A. Faisal M. Piggott 29 5 0 21 Nov 2023
Exposition on over-squashing problem on GNNs: Current Methods, Benchmarks and Challenges Dai Shi Andi Han Lequan Lin Yi Guo Junbin Gao 47 12 0 13 Nov 2023
Hybrid Focal and Full-Range Attention Based Graph Transformers Minhong Zhu Zhenhao Zhao Weiran Cai 34 0 0 08 Nov 2023
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel Xuan Li Zhanke Zhou Jiangchao Yao Yu Rong Lu Zhang Bo Han 42 3 0 02 Nov 2023
$Generating QM1B with PySCF$_{\text{IPU}}$$ Generating QM1B with PySCF $_{\text{IPU}}$ Alexander Mathiasen Hatem Helal Kerstin Klaser Paul Balanca Josef Dean Carlo Luschi Dominique Beaini Andrew Fitzgibbon Dominic Masters 25 1 0 02 Nov 2023
Facilitating Graph Neural Networks with Random Walk on Simplicial Complexes Cai Zhou Xiyuan Wang Muhan Zhang 41 15 0 30 Oct 2023
Improving Compositional Generalization Using Iterated Learning and Simplicial Embeddings Yi Ren Samuel Lavoie Mikhail Galkin Danica J. Sutherland Aaron Courville 41 15 0 28 Oct 2023
Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding Jiangyan Ma Yifei Wang Yisen Wang 36 12 0 28 Oct 2023
Learning to Search Feasible and Infeasible Regions of Routing Problems with Flexible Neural k-Opt Yining Ma Zhiguang Cao Yeow Meng Chee 29 37 0 27 Oct 2023
BLIS-Net: Classifying and Analyzing Signals on Graphs Charles Xu Laney Goldman Valentina Guo Benjamin Hollander-Bodie Maedee Trank-Greene Ian M. Adelstein E. Brouwer Rex Ying Smita Krishnaswamy Michael Perlmutter 24 1 0 26 Oct 2023
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt Mouxiang Chen Zemin Liu Chenghao Liu Jundong Li Qiheng Mao Jianling Sun AAML 27 12 0 23 Oct 2023
Can strong structural encoding reduce the importance of Message Passing? Floor Eijkelboom Erik J. Bekkers Michael M. Bronstein Francesco Di Giovanni University of Amsterdam 27 2 0 22 Oct 2023
Graph AI in Medicine Ruth Johnson Michelle M. Li Ayush Noori Owen Queen Marinka Zitnik 29 3 0 20 Oct 2023
Multi-omics Sampling-based Graph Transformer for Synthetic Lethality Prediction Xusheng Zhao Hao Liu Qiong Dai Hao Peng Xu Bai Huailiang Peng 17 2 0 17 Oct 2023
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning Jinsong Chen Gaichao Li J. Hopcroft Kun He SLR 13 5 0 17 Oct 2023
LPFormer: An Adaptive Graph Transformer for Link Prediction Harry Shomer Yao Ma Haitao Mao Juanhui Li Bo Wu Jiliang Tang 38 7 0 17 Oct 2023
Mirage: Model-Agnostic Graph Distillation for Graph Classification Mridul Gupta S. Manchanda H. Kodamana Sayan Ranu DD 26 15 0 14 Oct 2023
Multimodal Graph Learning for Generative Tasks Minji Yoon Jing Yu Koh Bryan Hooi Ruslan Salakhutdinov 33 7 0 11 Oct 2023
Non-backtracking Graph Neural Networks Seonghyun Park Narae Ryu Ga-Rin Kim Dongyeop Woo Se-Young Yun Sungsoo Ahn 35 4 0 11 Oct 2023
Atom-Motif Contrastive Transformer for Molecular Property Prediction Wentao Yu Shuo Chen Chen Gong Gang Niu Masashi Sugiyama ViT 45 2 0 11 Oct 2023
Flood and Echo Net: Algorithmically Aligned GNNs that Generalize Joël Mathys Florian Grötschla K. Nadimpalli Roger Wattenhofer FedML 44 0 0 10 Oct 2023
Supercharging Graph Transformers with Advective Diffusion Qitian Wu Chenxiao Yang Kaipeng Zeng Fan Nie AI4CE 53 6 0 10 Oct 2023
Tailoring Self-Attention for Graph via Rooted Subtrees Siyuan Huang Yunchong Song Jiayue Zhou Zhouhan Lin 29 8 0 08 Oct 2023
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets Dominique Beaini Shenyang Huang Joao Alex Cunha Zhiyi Li Gabriela Moisescu-Pareja ... Thérence Bois Andrew Fitzgibbon Bla.zej Banaszewski Chad Martin Dominic Masters AI4CE 30 19 0 06 Oct 2023
On the Stability of Expressive Positional Encodings for Graphs Yinan Huang William Lu Joshua Robinson Yu Yang Muhan Zhang Stefanie Jegelka Pan Li 40 9 0 04 Oct 2023
Probabilistically Rewired Message-Passing Neural Networks Chendi Qian Andrei Manolache Kareem Ahmed Zhe Zeng Mathias Niepert Mathias Niepert Christopher Morris 39 12 0 03 Oct 2023
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking Mert Kosan S. Verma Burouj Armgaan Khushbu Pahwa Ambuj K. Singh Sourav Medya Sayan Ranu 32 13 0 03 Oct 2023
Transformers are efficient hierarchical chemical graph learners Zihan Pengmei Zimu Li Chih-chan Tien Risi Kondor Aaron R Dinner GNN 23 1 0 02 Oct 2023
Locality-Aware Graph-Rewiring in GNNs Federico Barbero A. Velingker Amin Saberi Michael M. Bronstein Francesco Di Giovanni 40 27 0 02 Oct 2023
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks Yanqiao Zhu Jeehyun Hwang Keir Adams Zhen Liu B. Nan ... Olaf Wiest Olexandr Isayev Connor W. Coley Yizhou Sun Wei Wang 27 6 0 29 Sep 2023
MuSe-GNN: Learning Unified Gene Representation From Multimodal Biological Graph Data Tianyu Liu Yuge Wang Rex Ying Hongyu Zhao 32 12 0 29 Sep 2023
A Model-Agnostic Graph Neural Network for Integrating Local and Global Information Wenzhuo Zhou Annie Qu Keiland W Cooper Norbert Fortin Babak Shahbaba 42 1 0 23 Sep 2023
Deep Prompt Tuning for Graph Transformers Reza Shirkavand Heng-Chiao Huang 23 7 0 18 Sep 2023
Masked Diffusion with Task-awareness for Procedure Planning in Instructional Videos Fen Fang Yun Liu Ali Koksal Qianli Xu Joo-Hwee Lim VGen DiffM 26 5 0 14 Sep 2023
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation Siwei Zhang Yun Xiong Yao Zhang Xixi Wu Yiheng Sun Jiawei Zhang AI4TS 17 4 0 05 Sep 2023
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark Jan Tonshoff Martin Ritzert Eran Rosenbluth Martin Grohe 24 47 0 01 Sep 2023
Structural Node Embeddings with Homomorphism Counts Hinrikus Wolf Luca Oeljeklaus Pascal Kuhner Martin Grohe 30 3 0 29 Aug 2023
TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs P. Phothilimthana Sami Abu-El-Haija Kaidi Cao Bahare Fatemi Mike Burrows Charith Mendis Bryan Perozzi GNN AI4TS 33 17 0 25 Aug 2023
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding Yuan Luo Hongkang Li Lei Shi Xiao-Ming Wu 31 7 0 22 Aug 2023
UGSL: A Unified Framework for Benchmarking Graph Structure Learning Bahare Fatemi Sami Abu-El-Haija Anton Tsitsulin Seyed Mehran Kazemi Dustin Zelle Neslihan Bulut Jonathan J. Halcrow Bryan Perozzi 54 10 0 21 Aug 2023
Transitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity Van Thuy Hoang O-Joun Lee 27 11 0 18 Aug 2023
Graph Positional and Structural Encoder Semih Cantürk Renming Liu Olivier Lapointe-Gagné Vincent Létourneau Guy Wolf Dominique Beaini Ladislav Rampášek 41 15 0 14 Jul 2023
ChiENN: Embracing Molecular Chirality with Graph Neural Networks Piotr Gaiñski Michał Koziarski Jacek Tabor Marek Śmieja GNN 34 3 0 05 Jul 2023