Timezone: »
We propose a dynamic edge exchangeable network model that can capture sparse connections observed in real temporal networks, in contrast to existing models which are dense. The model achieved superior link prediction accuracy when compared to a dynamic variant of the blockmodel, and is able to extract interpretable time-varying community structures. In addition to sparsity, the model accounts for the effect of social influence on vertices’ future behaviours. Compared to the dynamic blockmodels, our model has a smaller latent space. The compact latent space requires a smaller number of parameters to be estimated in variational inference and results in a computationally friendly inference algorithm.
Author Information
Yin Cheng Ng (University College London)
More from the Same Authors
-
2018 Poster: Bayesian Semi-supervised Learning with Graph Gaussian Processes »
Yin Cheng Ng · Nicolò Colombo · Ricardo Silva -
2017 : Posters »
Reihaneh Rabbany · Tianxi Li · Jacob Carroll · Yin Cheng Ng · Xueyu Mao · Alexandre Hollocou · Jeric Briones · James Atwood · John Santerre · Natalie Klein · Pranamesh Chakraborty · Zahra Razaee · Chandan Singh · Arun Suggala · Beilun Wang · Andrew R. Lawrence · Aditya Grover · FARSHAD HARIRCHI · radhika arava · Qing Zhou · Takatomi Kubo · Josue Orellana · Govinda Kamath · Vivek Kumar Bagaria -
2016 Poster: Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages »
Yin Cheng Ng · Pawel M Chilinski · Ricardo Silva