no code implementations • 12 Oct 2023 • Zohair Shafi, Benjamin A. Miller, Tina Eliassi-Rad, Rajmonda S. Caceres
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.
no code implementations • 12 Oct 2023 • Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee, Tina Eliassi-Rad, Rajmonda S. Caceres
We consider an APX-hard problem, where an adversary aims to attack shortest paths in a graph by removing the minimum number of edges.
no code implementations • 19 Apr 2022 • Virginia H. Goodwin, Rajmonda S. Caceres
The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years.
no code implementations • 24 Feb 2017 • Benjamin Fish, Rajmonda S. Caceres
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.
no code implementations • 24 Feb 2017 • Cem Safak Sahin, Rajmonda S. Caceres, Brandon Oselio, William M. Campbell
Word embedding models offer continuous vector representations that can capture rich contextual semantics based on their word co-occurrence patterns.
no code implementations • 24 Apr 2015 • Benjamin Fish, Rajmonda S. Caceres
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.