Node copying: A random graph model for effective graph sampling
نویسندگان
چکیده
There has been an increased interest in applying machine learning techniques on relational structured-data based observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a generative model conditioned allows to take uncertainty into account. Various existing either rely restrictive assumptions, fail preserve topological properties within samples or are prohibitively expensive for larger graphs. work, we introduce node copying constructing distribution over Sampling random carried out by replacing each node’s neighbors those randomly sampled similar node. The graphs key characteristics structure without explicitly targeting them. Additionally, sampling from extremely simple and scales linearly with We show usefulness three tasks. First, classification, Bayesian formulation achieves higher accuracy sparse data settings. Second, employ our proposed mitigate effect adversarial attacks topology. Last, incorporation recommendation system setting improves recall state-of-the-art methods.
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ژورنال
عنوان ژورنال: Signal Processing
سال: 2022
ISSN: ['0165-1684', '1872-7557']
DOI: https://doi.org/10.1016/j.sigpro.2021.108335