LoG

Keyphrases

Publications

2023

Vincent Peter Grande, Michael T. Schaub
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. LoG (2023)
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. LoG (2023)
Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger
Meta-Path Learning for Multi-Relational Graph Neural Networks. LoG (2023)
Mikhail Hayhoe, Hans Riess, Michael M. Zavlanos, Victor M. Preciado, Alejandro Ribeiro
Transferable Hypergraph Neural Networks via Spectral Similarity. LoG (2023)
Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic
Latent Space Representations of Neural Algorithmic Reasoners. LoG (2023)
Etzion Harari, Naphtali Abudarham, Roee Litman
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass. LoG (2023)
Jonas Jürß, Dulhan Hansaja Jayalath, Petar Velickovic
Recursive Algorithmic Reasoning. LoG (2023)
Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta J. Bedathur
Generative Modeling of Labeled Graphs Under Data Scarcity. LoG (2023)
Vasileios Baltatzis, Luca Costabello
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation. LoG (2023)
Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini
A Simple Latent Variable Model for Graph Learning and Inference. LoG (2023)
Xiandong Zou, Xiangyu Zhao, Pietro Lio, Yiren Zhao
Will More Expressive Graph Neural Networks Do Better on Generative Tasks? LoG (2023)
Naganand Yadati, Tarun Kumar, Deepak Maurya, Balaraman Ravindran, Partha P. Talukdar
HEAL: Unlocking the Potential of Learning on Hypergraphs Enriched With Attributes and Layers. LoG (2023)
Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong
BeMap: Balanced Message Passing for Fair Graph Neural Network. LoG (2023)
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. LoG (2023)
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. LoG (2023)
Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein
Edge Directionality Improves Learning on Heterophilic Graphs. LoG (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. LoG (2023)
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. LoG (2023)
Cong Fu, Jacob Helwig, Shuiwang Ji
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. LoG (2023)
Josef Hoppe, Michael T. Schaub
Representing Edge Flows on Graphs via Sparse Cell Complexes. LoG (2023)
Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang, Chao Chen, Yusu Wang
Cycle Invariant Positional Encoding for Graph Representation Learning. LoG (2023)
Raffaele Pojer, Andrea Passerini, Manfred Jaeger
Generalized Reasoning With Graph Neural Networks by Relational Bayesian Network Encodings. LoG (2023)
Tuo Xu, Lei Zou
Rethinking Higher-Order Representation Learning With Graph Neural Networks. LoG (2023)
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. LoG (2023)
Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio
Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG (2023)
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. LoG (2023)
Donald Loveland, Jiong Zhu, Mark Heimann, Benjamin Fish, Michael T. Schaub, Danai Koutra
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks. LoG (2023)
Jing Gu, Dongmian Zou
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. LoG (2023)
Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic
Asynchronous Algorithmic Alignment With Cocycles. LoG (2023)
Andreas Roth, Thomas Liebig
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks. LoG (2023)
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. LoG (2023)
Lukas Fesser, Melanie Weber
Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature. LoG (2023)
Shiqing Yu, Mathias Drton, Ali Shojaie
Interaction Models and Generalized Score Matching for Compositional Data. LoG (2023)
Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic
Parallel Algorithms Align With Neural Execution. LoG (2023)
Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup
MUDiff: Unified Diffusion for Complete Molecule Generation. LoG (2023)
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. LoG (2023)
Lukas Faber, Roger Wattenhofer
GwAC: GNNs With Asynchronous Communication. LoG (2023)
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. LoG (2023)
Stefan Künzli, Florian Grötschla, Joël Mathys, Roger Wattenhofer
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics. LoG (2023)

volume 231, 2023

Learning on Graphs Conference, 27-30 November 2023, Virtual Event. LoG 231 (2023)

2022

volume 198, 2022

Learning on Graphs Conference, LoG 2022, 9-12 December 2022, Virtual Event. LoG 198 (2022)