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 ...
Researchers at Shanghai Jiao Tong University have made a groundbreaking discovery in the field of Temporal Knowledge Graphs (TKGs), challenging the conventional reliance on graph-based techniques and ...
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, ...
Decoding emotional states from electroencephalography (EEG) signals is a fundamental goal in affective neuroscience. This endeavor requires accurately modeling the complex spatio-temporal dynamics of ...
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) ...
ABSTRACT: The exact solutions for deterministic chaos, stochastic chaos, and wave turbulence have been developed in terms of exponential oscillons and pulsons, which are governed by the nonstationary ...
ABSTRACT: The exact solutions for deterministic chaos, stochastic chaos, and wave turbulence have been developed in terms of exponential oscillons and pulsons, which are governed by the nonstationary ...
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 ...
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