cdlib.algorithms.walkscan¶
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walkscan
(g_original: object, nb_steps: int = 2, eps: float = 0.1, min_samples: int = 3, init_vector: dict = None) → cdlib.classes.node_clustering.NodeClustering¶ Random walk community detection method leveraging PageRank node scoring.
Parameters: - g_original – a networkx/igraph object
- nb_steps – the length of the random walk
- eps – DBSCAN eps
- min_samples – DBSCAN min_samples
- init_vector – dictionary node_id -> initial_probability to initialize the random walk. Default, random selected node with probability set to 1.
Returns: NodeClustering object
Example: >>> from cdlib import algorithms >>> import networkx as nx >>> G = nx.karate_club_graph() >>> coms = algorithms.walkscan(G)
References: Hollocou, A., Bonald, T., & Lelarge, M. (2016). Improving PageRank for local community detection. arXiv preprint arXiv:1610.08722.
Note
Reference implementation: https://github.com/ahollocou/walkscan