This library solves the problem of working with graphs in a structured, reusable way. It provides implementations of essential graph algorithms that are commonly used in computer science applications, ...
This engine models spatial networks as directed graphs with weighted edges. Core use cases include shortest-path computation, reachability analysis, and finding optimal routes under ...
Abstract: Traffic flow prediction faces challenges in spatial relationship modeling and risk-aware external factor integration. Current graph-based methods typically rely on single adjacency matrices ...
Abstract: Accurate short-term load forecasting (STLF) requires capturing complex spatio-temporal dependencies, a task where standard Graph Neural Networks (GNNs) struggle due to static graph ...
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