- 2023
- Cong Fu, Jacob Helwig, Shuiwang Ji:
Semi-Supervised Learning for High-Fidelity Fluid Flow Reconstruction. LoG 2023: 36 - 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: 29 - 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: 7 - Vasileios Baltatzis, Luca Costabello:
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation. LoG 2023: 27 - 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: 15 - 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: 22 - 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: 24 - Andrew Joseph Dudzik, Tamara von Glehn, Razvan Pascanu, Petar Velickovic:
Asynchronous Algorithmic Alignment With Cocycles. LoG 2023: 3 - Valerie Engelmayer, Dobrik Georgiev, Petar Velickovic:
Parallel Algorithms Align With Neural Execution. LoG 2023: 31 - Lukas Faber, Roger Wattenhofer:
GwAC: GNNs With Asynchronous Communication. LoG 2023: 8 - Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger:
Meta-Path Learning for Multi-Relational Graph Neural Networks. LoG 2023: 2 - Lukas Fesser, Melanie Weber:
Mitigating Over-Smoothing and Over-Squashing Using Augmentations of Forman-Ricci Curvature. LoG 2023: 19 - Dobrik Georgiev, Danilo Numeroso, Davide Bacciu, Pietro Lio:
Neural Algorithmic Reasoning for Combinatorial Optimisation. LoG 2023: 28 - Vincent Peter Grande, Michael T. Schaub:
Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH. LoG 2023: 17 - Jing Gu, Dongmian Zou:
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural Networks. LoG 2023: 14 - Etzion Harari, Naphtali Abudarham, Roee Litman:
GSCAN: Graph Stability Clustering for Applications With Noise Using Edge-Aware Excess-of-Mass. LoG 2023: 9 - Mikhail Hayhoe, Hans Riess, Michael M. Zavlanos, Victor M. Preciado, Alejandro Ribeiro:
Transferable Hypergraph Neural Networks via Spectral Similarity. LoG 2023: 18 - 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: 12 - Josef Hoppe, Michael T. Schaub:
Representing Edge Flows on Graphs via Sparse Cell Complexes. LoG 2023: 1 - Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, Doina Precup:
MUDiff: Unified Diffusion for Complete Molecule Generation. LoG 2023: 33 - Manfred Jaeger, Antonio Longa, Steve Azzolin, Oliver Schulte, Andrea Passerini:
A Simple Latent Variable Model for Graph Learning and Inference. LoG 2023: 26 - Jonas Jürß, Dulhan Hansaja Jayalath, Petar Velickovic:
Recursive Algorithmic Reasoning. LoG 2023: 5 - Stefan Künzli, Florian Grötschla, Joël Mathys, Roger Wattenhofer:
SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics. LoG 2023: 13 - Xiao Lin, Jian Kang
, Weilin Cong, Hanghang Tong:
BeMap: Balanced Message Passing for Fair Graph Neural Network. LoG 2023: 37 - 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: 6 - Sahil Manchanda, Shubham Gupta, Sayan Ranu, Srikanta J. Bedathur:
Generative Modeling of Labeled Graphs Under Data Scarcity. LoG 2023: 32 - Vladimir V. Mirjanic, Razvan Pascanu, Petar Velickovic:
Latent Space Representations of Neural Algorithmic Reasoners. LoG 2023: 10 - 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: 23 - Raffaele Pojer, Andrea Passerini, Manfred Jaeger:
Generalized Reasoning With Graph Neural Networks by Relational Bayesian Network Encodings. LoG 2023: 16 - Emanuele Rossi, Bertrand Charpentier, Francesco Di Giovanni, Fabrizio Frasca, Stephan Günnemann, Michael M. Bronstein:
Edge Directionality Improves Learning on Heterophilic Graphs. LoG 2023: 25
