arxivMay 22bullish

Billion-Scale Graph Foundation Models

arXiv:2602.04768v2 Announce Type: replace Abstract: Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-world graphs is challenging. I

GR1 model #graph-learning #foundation-models #pretraining Read on arxiv →

arxivMay 21bullish

Fast and Featureless Node Representation Learning with Partial Pairwise Supervision

arXiv:2605.19916v1 Announce Type: cross Abstract: We introduce Contrastive FUSE, a fast and unified framework for scalable node representation learning in graphs with partially available pairwise node labels and no available node features. Unlike existing methods, we directly optimize a spectral con

CO1 model #machine-learning #graph-learning #optimization Read on arxiv →

arxivApr 29

Latent-Hysteresis Graph ODEs: Modeling Coupled Topology-Feature Evolution via Continuous Phase Transitions

arXiv:2604.24293v1 Announce Type: cross Abstract: Graph neural ordinary differential equations (Graph ODEs) extend graph learning from discrete message-passing layers to continuous-time representation flows. While it supports adaptive long-range propagation, we show that Graph ODEs with strictly pos

GRHY2 models #graph-learning #machine-learning #artificial-intelligence Read on arxiv →