Sampling
Graph sampling algorithms can be used to reduce the size of large and complex graphs while preserving structural properties. This can help to reduce bias, and ensure privacy, and making graph analysis more scalable. Sampling algorithms are widely used in machine learning, social network analysis, and many other applications.
You can choose between different sampling methods.
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Random walk with restart - taking random walks from a set of start nodes.
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Common Neighbour Aware Random Walk - avoids getting caught in local loops. This is especially useful for graphs with solid dense regions.