Author Name Hazem KRICHENE (University of Hyogo) / ARATA Yoshiyuki (Fellow, RIETI) / Abhijit CHAKRABORTY (University of Hyogo) / FUJIWARA Yoshi (University of Hyogo) / INOUE Hiroyasu (University of ...
Temporal Exponential Random Graph Models (TERGM) estimated by maximum pseudolikelihood with bootstrapped confidence intervals or Markov Chain Monte Carlo maximum likelihood (MCMC MLE). Goodness of fit ...
Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...
This lecture course is devoted to the study of random geometrical objects and structures. Among the most prominent models are random polytopes, random tessellations, particle processes and random ...
At some point in time, all users of social networking applications such as Facebook or LinkedIn have been pleasantly surprised at seeing an old school friend, or an ex-colleague from that first job, ...
Graph technology is allowing pharma to model data in a way that offers invaluable insights for marketing, R&D and compliance teams alike. Google, Facebook and LinkedIn are among those utilising graph ...
Amazon Neptune: 6 Ways to Use the AWS Graph Database Your email has been sent Learn how you can use Amazon Neptune's Graph Database to simplify building and running graph applications. When Amazon Web ...
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