Poster "graph neural networks" Papers
142 papers found • Page 3 of 3
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, Ismail Ceylan et al.
How Graph Neural Networks Learn: Lessons from Training Dynamics
Chenxiao Yang, Qitian Wu, David Wipf et al.
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi et al.
Learning Divergence Fields for Shift-Robust Graph Representations
Qitian Wu, Fan Nie, Chenxiao Yang et al.
Learning Graph Representation via Graph Entropy Maximization
Ziheng Sun, Xudong Wang, Chris Ding et al.
LLaGA: Large Language and Graph Assistant
Runjin Chen, Tong Zhao, Ajay Jaiswal et al.
MAGNOLIA: Matching Algorithms via GNNs for Online Value-to-go Approximation
Alexandre Hayderi, Amin Saberi, Ellen Vitercik et al.
Mitigating Label Noise on Graphs via Topological Sample Selection
Yuhao Wu, Jiangchao Yao, Xiaobo Xia et al.
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
Modelling Microbial Communities with Graph Neural Networks
Albane Ruaud, Cansu Sancaktar, Marco Bagatella et al.
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Yuchen Zhang, Tianle Zhang, Kai Wang et al.
Networked Inequality: Preferential Attachment Bias in Graph Neural Network Link Prediction
Arjun Subramonian, Levent Sagun, Yizhou Sun
Neural SPH: Improved Neural Modeling of Lagrangian Fluid Dynamics
Artur Toshev, Jonas Erbesdobler, Nikolaus Adams et al.
Neural Tangent Kernels Motivate Cross-Covariance Graphs in Neural Networks
Shervin Khalafi, Saurabh Sihag, Alejandro Ribeiro
On dimensionality of feature vectors in MPNNs
César Bravo, Alexander Kozachinskiy, Cristobal Rojas
On the Expressive Power of Spectral Invariant Graph Neural Networks
Bohang Zhang, Lingxiao Zhao, Haggai Maron
On the Generalization of Equivariant Graph Neural Networks
Rafał Karczewski, Amauri Souza, Vikas Garg
On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective
Jincheng Huang, Jialie SHEN, Xiaoshuang Shi et al.
Open Ad Hoc Teamwork with Cooperative Game Theory
Jianhong Wang, Yang Li, Yuan Zhang et al.
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
Jaejun Lee, Minsung Hwang, Joyce Whang
PANDA: Expanded Width-Aware Message Passing Beyond Rewiring
Jeongwhan Choi, Sumin Parksumin, Hyowon Wi et al.
PGODE: Towards High-quality System Dynamics Modeling
Xiao Luo, Yiyang Gu, Huiyu Jiang et al.
PhAST: Physics-Aware, Scalable, and Task-Specific GNNs for Accelerated Catalyst Design
Alexandre Duval, Victor Schmidt, Santiago Miret et al.
Position: Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym et al.
Predicting and Interpreting Energy Barriers of Metallic Glasses with Graph Neural Networks
Haoyu Li, Shichang Zhang, Longwen Tang et al.
Predicting Lagrangian Multipliers for Mixed Integer Linear Programs
Francesco Demelas, Joseph Roux, Mathieu Lacroix et al.
Quantum Positional Encodings for Graph Neural Networks
Slimane Thabet, Mehdi Djellabi, Igor Sokolov et al.
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Rui Miao, Kaixiong Zhou, Yili Wang et al.
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Langzhang Liang, Sunwoo Kim, Kijung Shin et al.
Structure Your Data: Towards Semantic Graph Counterfactuals
Angeliki Dimitriou, Maria Lymperaiou, Giorgos Filandrianos et al.
Subgraphormer: Unifying Subgraph GNNs and Graph Transformers via Graph Products
Guy Bar Shalom, Beatrice Bevilacqua, Haggai Maron
Subhomogeneous Deep Equilibrium Models
Pietro Sittoni, Francesco Tudisco
The Expressive Power of Path-Based Graph Neural Networks
Caterina Graziani, Tamara Drucks, Fabian Jogl et al.
The Merit of River Network Topology for Neural Flood Forecasting
Nikolas Kirschstein, Yixuan Sun
Towards General Algorithm Discovery for Combinatorial Optimization: Learning Symbolic Branching Policy from Bipartite Graph
Yufei Kuang, Jie Wang, Yuyan Zhou et al.
Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
Dongkwan Kim, Alice Oh
Two Heads Are Better Than One: Boosting Graph Sparse Training via Semantic and Topological Awareness
Guibin Zhang, Yanwei Yue, kun wang et al.
Uncertainty for Active Learning on Graphs
Dominik Fuchsgruber, Tom Wollschläger, Bertrand Charpentier et al.
Understanding Heterophily for Graph Neural Networks
Junfu Wang, Yuanfang Guo, Liang Yang et al.
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
Yuhao Wu, Jiangchao Yao, Bo Han et al.
Verifying message-passing neural networks via topology-based bounds tightening
Christopher Hojny, Shiqiang Zhang, Juan Campos et al.
Weisfeiler Leman for Euclidean Equivariant Machine Learning
Snir Hordan, Tal Amir, Nadav Dym