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Found 5 papers, 0 papers with code
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.
Graph-SCP: Accelerating Set Cover Problems with Graph Neural Networks
We look specifically at the Set Cover Problem (SCP) and propose Graph-SCP, a graph neural network method that can augment existing optimization solvers by learning to identify a much smaller sub-problem that contains the solution space.
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).
