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Found 6 papers, 0 papers with code
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.
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.
System Analysis for Responsible Design of Modern AI/ML Systems
The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years.
A supervised approach to time scale detection in dynamic networks
We introduce a framework that tackles both of these issues: By measuring the performance of the time scale detection algorithm based on how well a given task is accomplished on the resulting network, we are for the first time able to directly compare different time scale detection algorithms to each other.
Consistent Alignment of Word Embedding Models
Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word co-occurrence patterns.
Handling oversampling in dynamic networks using link prediction
We show that not only does oversampling affect the quality of link prediction, but that we can use link prediction to recover from the effects of oversampling.
