Abstract: Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or minimally ...

Abstract: The rich semantic information in Control Flow Graphs (CFGs) of executable programs has made Graph Neural Networks (GNNs) a key focus for malware detection. However, existing CFG-based ...

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 ...

FLASH-RT (Framework for online node anomaly detection in provenance graphs via trend calibration) is a lightweight anomaly detection framework for streaming provenance graphs. It addresses two key ...

A new framework called SkillWeaver tackles AI agent tool routing by skipping full-library loading, cutting token use 99% on ...

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

BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...

We present an algorithm for enumerating all possible faceting arrangements of dihedrally symmetric diamond cuts. We first separate the question into enumerating crowns a ...

Couchbase AI Data Plane combines persistent agent memory, vector search and an enterprise MCP server that runs on-device when ...

This week’s cybersecurity recap covers Firefox and Chrome bugs, EDR-killer tools, a TV botnet, an OpenBSD flaw, Android ...