AI RESEARCH
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
arXiv CS.LG
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ArXi:2510.01510v3 Announce Type: replace We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation models (KGFMs) address this task by enforcing equivariance over both nodes and relations, which enables them to learn structural properties of nodes and relations that transfer to novel KGs with similar structure.