Timezone: »

Dependent Relational Gamma Process Models for Longitudinal Networks
Sikun Yang · Heinz Koeppl

Wed Jul 11 09:15 AM -- 12:00 PM (PDT) @ Hall B #46

A probabilistic framework based on the covariate-dependent relational gamma process is developed to analyze relational data arising from longitudinal networks. The proposed framework characterizes networked nodes by nonnegative node-group memberships, which allow each node to belong to multiple latent groups simultaneously, and encodes edge probabilities between each pair of nodes using a Bernoulli Poisson link to the embedded latent space. Within the latent space, our framework models the birth and death dynamics of individual groups via a thinning function. Our framework also captures the evolution of individual node-group memberships over time using gamma Markov processes. Exploiting the recent advances in data augmentation and marginalization techniques, a simple and efficient Gibbs sampler is proposed for posterior computation. Experimental results on a simulation study and three real-world temporal network data sets demonstrate the model’s capability, competitive performance and scalability compared to state-of-the-art methods.

Author Information

Sikun Yang (TU Darmstadt)
Heinz Koeppl (TU Darmstadt)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors