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Showing 1 to 3 of 3 for “"graph generative models"”.

  1. Graph Representation Learning for Drug Discovery

    … structure (drug-target interaction) into the models in order to leverage heterogeneous single-compound assays as well as to provide a mechanism to assess drug combinations through competitive binding to such targets. Third, we extend the search for new drugs beyond known chemical matter by …

    mit Repository record for Graph Representation Learning for Drug Discovery (opens in a new tab)

  2. Network motif prediction using generative models for graphs

    Graphs are commonly used to represent pairwise interactions between different entities in networks. Generative graph models create new graphs that mimic the properties of already existing graphs. Generative models are successful at retaining the pairwise interactions of the underlying networks but …

    uiuc Repository record for Network motif prediction using generative models for graphs (opens in a new tab)

  3. Topological evolution of networks : case studies in the US airlines and language Wikipedias

    … theory relevant to topological evolution and use graph-theoretical methods to analyze real systems, represented as networks. Using existing graph generative models, we develop a profile of canonical graphs and tools to compare a real network to that profile. The developed metrics are used to track …

    mit Repository record for Topological evolution of networks : case studies in the US airlines and language Wikipedias (opens in a new tab)