4 papers · 1 filter
Random Probing for Structural Self-Interactions in Graph Neural Networks
Saku Peltonen, H. Çağrı Bilgi, Kubilay Atasu
Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, subgraph centrality, and heat…
Autoregressive Frontier Expansion: Growing Trees with Graph Machine Learning
Umer Gupta, Saku Peltonen, Martin Ritzert
Tree-like branching structures are common in nature, from botanical trees to neurons, blood vessels and respiratory trees. Their branching shape often reflects function, making str…
From Message-Passing to Linearized Graph Sequence Models
Joël Mathys, Basil Rohner, Saku Peltonen +1
Message-passing based approaches form the default backbone of most learning architectures on graph-structured data. However, the rapid progress of modern deep learning architecture…
On the Expressive Power of GNNs for Boolean Satisfiability
Saku Peltonen, Roger Wattenhofer
Machine learning approaches to solving Boolean Satisfiability (SAT) aim to replace handcrafted heuristics with learning-based models. Graph Neural Networks have emerged as the main…