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Found 5 papers, 1 papers with code
Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks
Machine learning (ML) approaches are increasingly being used to accelerate combinatorial optimization (CO) problems.
GRASP: Accelerating Shortest Path Attacks via Graph Attention
We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges.
Topological Effects on Attacks Against Vertex Classification
Vertex classification is vulnerable to perturbations of both graph topology and vertex attributes, as shown in recent research.
Assessing Functional Neural Connectivity as an Indicator of Cognitive Performance
Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network.
A Spectral Framework for Anomalous Subgraph Detection
Leveraging this analytical tool, we show that the framework has a natural power metric in the spectral norm of the anomalous subgraph's adjacency matrix (signal power) and of the background graph's residuals matrix (noise power).
