Abstract: Graphs are essential for modeling complex relationships, analyzing networks, and offering versatile representations that capture diverse data structures. Graph Neural Networks (GNNs) excel ...
Abstract: Graph has been proven to be an emerging tool for spectrum sensing (SS), with detection performance closely related to the graph characteristics. Existing graph-based SS has been mainly ...
Dear Eric: We have a condo at the beach that has a pool. There is a couple that we know through other people that we are not friendly with, and they have a condo in another building without a pool.
ABSTRACT: This research investigates the impact of the road network topological structure on facility location modeling. We create four types of road networks, i.e., the radial, the grid, the ring, ...
Abstract: In this talk, I will present a new combinatorial algorithm for maximum flow that is based on running the weighted push-relabel algorithm introduced in [BBST ...
The so-called Department of Government Efficiency (DOGE) is starting to put together a team to migrate the Social Security Administration’s (SSA) computer systems entirely off one of its oldest ...
Deep Machine Learning and Big Data Resources for Transcriptional Regulation Analysis, Volume II ...
Directed graphs are crucial in modeling complex real-world systems, from gene regulatory networks and flow networks to stochastic processes and graph metanetworks. Representing these directed graphs ...
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