arXiv AI June 26, 2026 By Xiang Li, Shanshan Wang, Chenglong Xiao

Learning to Select Maximum Clique Algorithms: From Traditional Machine Learning to a Dual-Channel Hybrid Neural Architecture

arXiv:2508. 08005v4 Announce Type: replace-cross Abstract: The Maximum Clique Problem (MCP) is an NP-hard problem with wide-ranging applications in fields such as bioinformatics, network science, and social computing, yet no single algorithm consistently outperforms all others across diverse graph instances.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 2

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.

By Louisa Cornelis, Johan Mathe, Louis Van Langendonck, Guillermo Bern\'ardez, Nina Miolane

benchmarks

arXiv AI

Jul 14

Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm

arXiv:2509. 24256v2 Announce Type: replace-cross Abstract: The pretrain-transfer paradigm, which underpins the success of large language models (LLMs), has demonstrated the immense power of creating foundation models that learn generalizable representations from vast datasets.

By Yunhao Liang, Pujun Zhang, Yuan Qu, Jingyuan Yang, Shaochong Lin, Zuo-jun Max Shen

llms

arXiv AI

Jun 9

EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks

arXiv:2606. 07700v1 Announce Type: cross Abstract: Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-lab experiments.

By Sahar Mansouri-Rad, Zahra Narimani, Parvin Razzaghi, Nazanin Hosseinkhan

rag

arXiv Machine Learning

Jun 25

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

arXiv:2602. 14772v2 Announce Type: replace Abstract: The Winner Determination Problem (WDP) in combinatorial auctions is NP-hard, and no existing method reliably predicts which instances will defeat fast greedy heuristics.

By Sungwoo Kang

benchmarks safety

arXiv Machine Learning

Jun 19

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

arXiv:2602. 20573v3 Announce Type: replace Abstract: Molecules are often represented as SMILES strings, which can be readily converted to hand-crafted descriptors or fingerprints (FP) for molecular property prediction.

By Rajan, Ishaan Gupta

fine-tuning benchmarks

arXiv Machine Learning

Jun 5

End-to-End Subgraph Detection with GraphDETR

arXiv:2606. 06364v1 Announce Type: new Abstract: Subgraph detection seeks to identify whether and where instances of query patterns occur within a larger graph.

By Dexiong Chen, Till Hendrik Schulz, Karsten Borgwardt

llms computer-vision

Related stories

OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data

Graph Optimization Foundation Model: Tokenizing Graph via A Language-Model Paradigm

EssentialGIN: a new approach for gene essentiality prediction based on graph isomorphism neural networks

Learning Structural Hardness for Combinatorial Auctions: Instance-Dependent Algorithm Selection via Graph Neural Networks

MolGraphBench: A Benchmark of GNN Architectures for Molecular Regression Tasks

End-to-End Subgraph Detection with GraphDETR