iSIDG (Iterative Structural Inference of Directed Graphs)
What it is
A machine learning framework for inferring the structure of directed graphs. It implements an iterative approach to recover graph topologies from data.
The problem it solves
Accurately inferring the underlying structure of directed graphs from noisy or incomplete data.
What you could use it for
- Inferring biological networks
- Analyzing social network structures
- Reconstructing directed graphs from physics-based simulations (e.g., springs, charged particles)
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Summary written by an AI model from the source; check the source before you rely on it.