13 citations · 13 across the 7 of their papers we have counts for
8 papers
End-to-End Subgraph Detection with GraphDETR
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph. This problem is fundamental across scientific domains and is closely…
Protein Fold Classification at Scale: Benchmarking and Pretraining
Dexiong Chen, Andrei Manolache, Mathias Niepert +1
Classifying protein topology is essential for deciphering biological function, but progress is held back by the lack of large-scale benchmarks that avoid duplicates and by models t…
PolyGraph Discrepancy: a classifier-based metric for graph generation
Markus Krimmel, Philip Hartout, Karsten Borgwardt +1
Existing methods for evaluating graph generative models primarily rely on Maximum Mean Discrepancy (MMD) metrics based on graph descriptors. While these metrics can rank generative…
A Comprehensive Benchmark for RNA 3D Structure-Function Modeling
Luis Wyss, Vincent Mallet, Wissam Karroucha +2
The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure mode…
Fast Graph Generation via Autoregressive Noisy Filtration Modeling
Markus Krimmel, Jenna Wiens, Karsten Borgwardt +1
Existing graph generative models often face a critical trade-off between sample quality and generation speed. We introduce Autoregressive Noisy Filtration Modeling (ANFM), a flexib…
Flatten Graphs as Sequences: Transformers are Scalable Graph Generators
Dexiong Chen, Markus Krimmel, Karsten Borgwardt
We introduce AutoGraph, a scalable autoregressive model for attributed graph generation using decoder-only transformers. By flattening graphs into random sequences of tokens throug…
Learning Long Range Dependencies on Graphs via Random Walks
Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enabl…
Endowing Protein Language Models with Structural Knowledge
Dexiong Chen, Philip Hartout, Paolo Pellizzoni +2
Understanding the relationships between protein sequence, structure and function is a long-standing biological challenge with manifold implications from drug design to our understa…