To enable more accurate estimation of connectivity, we propose a data-driven and theoretically grounded framework for optimally designing perturbation inputs, based on formulating the neural model as ...
As an emerging technology in the field of artificial intelligence (AI), graph neural networks (GNNs) are deep learning models designed to process graph-structured data. Currently, GNNs are effective ...
Abstract: Complex networks with their nontrivial topological features and rich patterns of interactions are commonly used to model real-world systems, including social networks, biological systems, ...
We constructed a backbone network based on commenter overlap and conducted a social network analysis (SNA) to examine the temporal dynamics. We further applied exponential random graph models (ERGMs) ...
Graph database provider Neo4j Inc. today announced that it will invest $100 million to accelerate its role as what it calls the “default knowledge layer” for agentic systems and generative artificial ...
According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now both been proved wrong. It started with a bet. In the late 1980s, at a ...
Abstract: We study human mobility networks through timeseries of contacts between individuals. Our proposed Random Walkers Induced temporal Graph (RWIG) model generates temporal graph sequences based ...
While Shohei Ohtani made history at the plate and on the basepaths in 2024, the Los Angeles Dodgers superstar could continue to rewrite the record books when he returns to the mound. Shohei Ohtani has ...
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