4 citations · 10 across the 17 of their papers we have counts for
7 papers · 1 filter
Homomorphism Counts as Structural Encodings for Graph Learning
Linus Bao, Emily Jin, Michael Bronstein +2
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on gr…
Learning on Large Graphs using Intersecting Communities
Ben Finkelshtein, İsmail İlkan Ceylan, Michael Bronstein +1
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from t…
Fully-inductive Node Classification on Arbitrary Graphs
Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin +3
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new struct…
Fisher Flow Matching for Generative Modeling over Discrete Data
Oscar Davis, Samuel Kessler, Mircea Petrache +3
Generative modeling over discrete data has recently seen numerous success stories, with applications spanning language modeling, biological sequence design, and graph-structured mo…
Homomorphism Counts for Graph Neural Networks: All About That Basis
Emily Jin, Michael Bronstein, İsmail İlkan Ceylan +1
A large body of work has investigated the properties of graph neural networks and identified several limitations, particularly pertaining to their expressive power. Their inability…
Link Prediction with Relational Hypergraphs
Xingyue Huang, Miguel Romero Orth, Pablo Barceló +2
Link prediction with knowledge graphs has been thoroughly studied in graph machine learning, leading to a rich landscape of graph neural network architectures with successful appli…
Future Directions in the Theory of Graph Machine Learning
Christopher Morris, Fabrizio Frasca, Nadav Dym +7
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of discip…