A multi-purposed unsupervised framework for comparing embeddings of undirected and directed graphs

نویسندگان

چکیده

Abstract Graph embedding is a transformation of nodes network into set vectors. A good should capture the underlying graph topology and structure, node-to-node relationship, other relevant information about graph, its subgraphs, themselves. If these objectives are achieved, an meaningful, understandable, often compressed representation network. Unfortunately, selecting best challenging task very requires domain experts. In this paper, we extend framework for evaluating embeddings that was recently introduced in [15]. Now, assigns two scores, local global, to each measure quality evaluated tasks require and, respectively, global properties The embedding, if needed, can be selected unsupervised way, or identify few worth further investigation. flexible scalable deal with undirected/directed weighted/unweighted graphs.

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ژورنال

عنوان ژورنال: Network Science

سال: 2022

ISSN: ['2050-1250', '2050-1242']

DOI: https://doi.org/10.1017/nws.2022.27