Semih Cantürk

semihcanturk00@gmail.com

Doctorat - UdeM

Superviseur⋅e principal⋅e

Guy Wolf

Sujets de recherche

Apprentissage automatique médical

Apprentissage de représentations

Apprentissage profond

Apprentissage sur graphes

Biologie computationnelle

Modèles génératifs

Modélisation moléculaire

Réseaux de neurones en graphes

Site web

Google Scholar

GitHub

Publications

Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs

Michael Perlmutter

2025-07-30

Proceedings of the Third Learning on Graphs Conference (publié)

proceedings.mlr.press

openreview.net

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings

Billy Joe Franks

Moshe Eliasof

Semih Cantürk

Guy Wolf

Carola-Bibiane Schönlieb

Sophie Fellenz

Marius Kloft

Recent advances in integrating positional and structural encodings (PSEs) into graph neural networks (GNNs) have significantly enhanced thei… (voir plus)r performance across various graph learning tasks. However, the general applicability of these encodings and their potential to serve as foundational representations for graphs remain uncertain. This paper investigates the fine-tuning efficiency, scalability with sample size, and generalization capability of learnable PSEs across diverse graph datasets. Specifically, we evaluate their potential as universal pre-trained models that can be easily adapted to new tasks with minimal fine-tuning and limited data. Furthermore, we assess the expressivity of the learned representations, particularly, when used to augment downstream GNNs. We demonstrate through extensive benchmarking and empirical analysis that PSEs generally enhance downstream models. However, some datasets may require specific PSE-augmentations to achieve optimal performance. Nevertheless, our findings highlight their significant potential to become integral components of future graph foundation models. We provide new insights into the strengths and limitations of PSEs, contributing to the broader discourse on foundation models in graph learning.

2025-03-07

TMLR (accepté)

openreview.net

Graph Positional and Structural Encoder

Renming Liu

Semih Cantürk

Olivier Lapointe-Gagné

Vincent Létourneau

Guy Wolf

Dominique Beaini

Ladislav Rampášek

Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, as in general graphs lack a canonical node … (voir plus)ordering. This renders PSEs essential tools for empowering modern GNNs, and in particular graph Transformers. However, designing PSEs that work optimally for a variety of graph prediction tasks is a challenging and unsolved problem. Here, we present the graph positional and structural encoder (GPSE), a first-ever attempt to train a graph encoder that captures rich PSE representations for augmenting any GNN. GPSE can effectively learn a common latent representation for multiple PSEs, and is highly transferable. The encoder trained on a particular graph dataset can be used effectively on datasets drawn from significantly different distributions and even modalities. We show that across a wide range of benchmarks, GPSE-enhanced models can significantly improve the performance in certain tasks, while performing on par with those that employ explicitly computed PSEs in other cases. Our results pave the way for the development of large pre-trained models for extracting graph positional and structural information and highlight their potential as a viable alternative to explicitly computed PSEs as well as to existing self-supervised pre-training approaches.

2024-07-08

Proceedings of the 41st International Conference on Machine Learning (publié)

doi.org

openreview.net

Towards a General GNN Framework for Combinatorial Optimization

Frederik Wenkel

Semih Cantürk

Michael Perlmutter

Guy Wolf

2024-05-31

ArXiv (prépublication)

doi.org