LoG 2023

38 papers

A Latent Diffusion Model for Protein Structure Generation Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael Curtis McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji

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A Simple Latent Variable Model for Graph Learning and Inference Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini

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Asynchronous Algorithmic Alignment with Cocycles Andrew Joseph Dudzik, Tamara Glehn, Razvan Pascanu, Petar Veličković

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BeMap: Balanced Message Passing for Fair Graph Neural Network Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong

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Cycle Invariant Positional Encoding for Graph Representation Learning Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang

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Edge Directionality Improves Learning on Heterophilic Graphs Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein

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EMP: Effective Multidimensional Persistence for Graph Representation Learning Yuzhou Chen, Ignacio Segovia-Dominguez, Cuneyt Gurcan Akcora, Zhiwei Zhen, Murat Kantarcioglu, Yulia Gel, Baris Coskunuzer

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Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings Raffaele Pojer, Andrea Passerini, Manfred Jaeger

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Generative Modeling of Labeled Graphs Under Data Scarcity Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta J. Bedathur

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GSCAN: Graph Stability Clustering for Applications with Noise Using Edge-Aware Excess-of-Mass Etzion Harari, Naphtali Abudarham, Roee Litman

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GwAC: GNNs with Asynchronous Communication Lukas Faber, Roger Wattenhofer

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HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched with Attributes and Layers Naganand Yadati, Tarun Kumar, Deepak Maurya, Balaraman Ravindran, Partha Talukdar

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HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi, Nils Blach, Piotr Nyczyk, Hubert Niewiadomski, Torsten Hoefler

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Inferring Dynamic Regulatory Interaction Graphs from Time Series Data with Perturbations Dhananjay Bhaskar, Daniel Sumner Magruder, Matheo Morales, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, James Noonan, Guy Wolf, Natalia Ivanova, Smita Krishnaswamy

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Interaction Models and Generalized Score Matching for Compositional Data Shiqing Yu, Mathias Drton, Ali Shojaie

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Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity Shubhankar Prashant Patankar, Mathieu Ouellet, Juan Cervino, Alejandro Ribeiro, Kieran A. Murphy, Danielle Bassett

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KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation Vasileios Baltatzis, Luca Costabello

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Latent Space Representations of Neural Algorithmic Reasoners Vladimir V Mirjanic, Razvan Pascanu, Petar Veličković

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Meta-Path Learning for Multi-Relational Graph Neural Networks Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger

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Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature Lukas Fesser, Melanie Weber

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MUDiff: Unified Diffusion for Complete Molecule Generation Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup

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Multicoated and Folded Graph Neural Networks with Strong Lottery Tickets Jiale Yan, Hiroaki Ito, Ángel López García-Arias, Yasuyuki Okoshi, Hikari Otsuka, Kazushi Kawamura, Thiem Van Chu, Masato Motomura

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Neural Algorithmic Reasoning for Combinatorial Optimisation Dobrik Georgiev Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio

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Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH Vincent Peter Grande, Michael T Schaub

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On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T Schaub, Danai Koutra

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Parallel Algorithms Align with Neural Execution Valerie Engelmayer, Dobrik Georgiev Georgiev, Petar Veličković

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PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs Yixuan He, Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert

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Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks Andreas Roth, Thomas Liebig

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Recursive Algorithmic Reasoning Jonas Jürß, Dulhan Hansaja Jayalath, Petar Veličković

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Representing Edge Flows on Graphs via Sparse Cell Complexes Josef Hoppe, Michael T Schaub

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Rethinking Higher-Order Representation Learning with Graph Neural Networks Tuo Xu, Lei Zou

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Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction Cong Fu, Jacob Helwig, Shuiwang Ji

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Spectral Subgraph Localization Ama Bembua Bainson, Judith Hermanns, Petros Petsinis, Niklas Aavad, Casper Dam Larsen, Tiarnan Swayne, Amit Boyarski, Davide Mottin, Alex M. Bronstein, Panagiotis Karras

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SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics Stefan Künzli, Florian Grötschla, Joël Mathys, Roger Wattenhofer

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Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks Jing Gu, Dongmian Zou

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Transferable Hypergraph Neural Networks via Spectral Similarity Mikhail Hayhoe, Hans Matthew Riess, Michael M. Zavlanos, Victor Preciado, Alejandro Ribeiro

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United We Stand, Divided We Fall: Networks to Graph (N2G) Abstraction for Robust Graph Classification Under Graph Label Corruption Zhiwei Zhen, Yuzhou Chen, Murat Kantarcioglu, Kangkook Jee, Yulia Gel

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Will More Expressive Graph Neural Networks Do Better on Generative Tasks? Xiandong Zou, Xiangyu Zhao, Pietro Lio, Yiren Zhao

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