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2nd LoG 2023: Virtual Event
- Soledad Villar, Benjamin Chamberlain:
Learning on Graphs Conference, 27-30 November 2023, Virtual Event. Proceedings of Machine Learning Research 231, PMLR 2023 - Soledad Villar, Benjamin Paul Chamberlain, Yuanqi Du, Hannes Stärk, Chaitanya K. Joshi, Andreea Deac, Iulia Duta, Joshua Robinson, Yanqiao Zhu, Kexin Huang, Michelle M. Li, Sofia Bourhim, Ilia Igashov, Alexandre Duval, Mathieu Alain, Dominique Beaini, Xinyu Yuan:
The Second Learning on Graphs Conference: Preface. i-xix - Josef Hoppe, Michael T. Schaub:
Representing Edge Flows on Graphs via Sparse Cell Complexes. 1 - Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
Meta-Path Learning for Multi-Relational Graph Neural Networks. 2 - Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic:
Asynchronous Algorithmic Alignment With Cocycles. 3 - Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang:
Cycle Invariant Positional Encoding for Graph Representation Learning. 4 - Jonas Jürß, Dulhan Hansaja Jayalath, Petar Velickovic:
Recursive Algorithmic Reasoning. 5 - Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra:
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. 6 - Ama Bembua Bainson, Judith Hermanns, Petros Petsinis, Niklas Aavad, Casper Dam Larsen, Tiarnan Swayne, Amit Boyarski, Davide Mottin, Alex M. Bronstein, Panagiotis Karras:
Spectral Subgraph Localization. 7 - Lukas Faber, Roger Wattenhofer:
GwAC: GNNs With Asynchronous Communication. 8 - Etzion Harari, Naphtali Abudarham, Roee Litman:
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass. 9 - Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic:
Latent Space Representations of Neural Algorithmic Reasoners. 10 - Jiale Yan, Hiroaki Ito, Ángel López García-Arias, Yasuyuki Okoshi, Hikari Otsuka, Kazushi Kawamura, Thiem Van Chu, Masato Motomura:
Multicoated and Folded Graph Neural Networks With Strong Lottery Tickets. 11 - Yixuan He, Xitong Zhang, Junjie Huang, Benedek Rozemberczki, Mihai Cucuringu, Gesine Reinert:
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed Graphs. 12 - Stefan Künzli, Florian Grötschla, Joël Mathys, Roger Wattenhofer:
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics. 13 - Jing Gu, Dongmian Zou:
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. 14 - Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi, Nils Blach, Piotr Nyczyk, Hubert Niewiadomski, Torsten Hoefler:
HOT: Higher-Order Dynamic Graph Representation Learning With Efficient Transformers. 15 - Raffaele Pojer, Andrea Passerini, Manfred Jaeger:
Generalized Reasoning With Graph Neural Networks by Relational Bayesian Network Encodings. 16 - Vincent Peter Grande, Michael T. Schaub:
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. 17 - Mikhail Hayhoe, Hans Riess, Michael M. Zavlanos, Victor M. Preciado, Alejandro Ribeiro:
Transferable Hypergraph Neural Networks via Spectral Similarity. 18 - Lukas Fesser, Melanie Weber:
Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature. 19 - Shiqing Yu, Mathias Drton, Ali Shojaie:
Interaction Models and Generalized Score Matching for Compositional Data. 20 - Xiandong Zou, Xiangyu Zhao, Pietro Lio, Yiren Zhao:
Will More Expressive Graph Neural Networks Do Better on Generative Tasks? 21 - Dhananjay Bhaskar, Daniel Sumner Magruder, Matheo Morales, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, Guy Wolf, Smita Krishnaswamy:
Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations. 22 - Shubhankar Prashant Patankar, Mathieu Ouellet, Juan Cerviño, Alejandro Ribeiro, Kieran A. Murphy, Danielle S. Bassett:
Intrinsically Motivated Graph Exploration Using Network Theories of Human Curiosity. 23 - Yuzhou Chen, Ignacio Segovia-Dominguez, Cuneyt Gurcan Akcora, Zhiwei Zhen, Murat Kantarcioglu, Yulia R. Gel, Baris Coskunuzer:
EMP: Effective Multidimensional Persistence for Graph Representation Learning. 24 - Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. 25 - Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini:
A Simple Latent Variable Model for Graph Learning and Inference. 26 - Vasileios Baltatzis, Luca Costabello:
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation. 27 - Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio:
Neural Algorithmic Reasoning for Combinatorial Optimisation. 28 - Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au, Michael McThrow, Tao Komikado, Koji Maruhashi, Kanji Uchino, Xiaoning Qian, Shuiwang Ji:
A Latent Diffusion Model for Protein Structure Generation. 29 - Zhiwei Zhen, Yuzhou Chen, Murat Kantarcioglu, Kangkook Jee, Yulia R. Gel:
United We Stand, Divided We Fall: Networks to Graph (N2G) Abstraction for Robust Graph Classification Under Graph Label Corruption. 30 - Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic:
Parallel Algorithms Align With Neural Execution. 31 - Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta J. Bedathur:
Generative Modeling of Labeled Graphs Under Data Scarcity. 32 - Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. 33 - Naganand Yadati, Tarun Kumar, Deepak Maurya, Balaraman Ravindran, Partha P. Talukdar:
HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched With Attributes and Layers. 34 - Andreas Roth, Thomas Liebig:
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks. 35 - Cong Fu, Jacob Helwig, Shuiwang Ji:
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. 36 - Xiao Lin, Jian Kang
, Weilin Cong, Hanghang Tong:
BeMap: Balanced Message Passing for Fair Graph Neural Network. 37 - Tuo Xu, Lei Zou:
Rethinking Higher-Order Representation Learning With Graph Neural Networks. 38
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