10.5281/zenodo.1242988
https://zenodo.org/records/1242988
oai:zenodo.org:1242988
Yang, Sikun
Sikun
Yang
TU Darmstadt
Koeppl, Heinz
Heinz
Koeppl
TU Darmstadt
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
Zenodo
2018
artificial intelligence
algorithms
probabilistic model
network
computation
sparse dynamic
model interference
2018-02-02
eng
10.5281/zenodo.1242987
https://zenodo.org/communities/precise
https://zenodo.org/communities/eu
Creative Commons Attribution Non Commercial No Derivatives 4.0 International
We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma distributed. The latent memberships evolve according to a Markov process. The optimal number of latent groups can be determined by data itself. The computational complexity of our method scales with the number of non-zero links, which makes it scalable to large sparse dynamic relational data. We present batch and online Gibbs sampling algorithms to perform model inference. Finally, we demonstrate the model’s performance on both synthetic and real-world datasets compared to state-of-the-art methods.
European Commission
10.13039/501100000780
668858
PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION