This repository for the paper 📘: A Survey of Deep Graph Learning under Distribution Shifts: from Graph Out-of-Distribution Generalization to Adaptation. The README file here maintains a list of ...

Abstract: Graph representation is an important part of graph clustering. Recently, contrastive learning, which maximizes the mutual information between augmented graph views that share the same ...

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...

Abstract: Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be ...

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AWS Context is a self-learning knowledge graph for enterprise data — it propagates agent-discovered relationships automatically, with no manual re-curation needed.

INSIGHT recovers the secret key without requiring scan-access, i.e., in an oracle-less setting for 7 unbroken learning resilient locking techniques, including 2 industry-adopted logic locking ...

Heeva Alavi, an Iranian-American, writes about her family’s mixed emotions about the World Cup, while Aariv Shah reflects on the SpaceX I.P.O. By The Learning Network We invited teenagers to create an ...

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?