Block-Structure Based Time-Series Models For Graph Sequences
Although the computational and statistical trade-off for modeling single graphs, for instance using block models, is relatively well understood, extending such results to sequences of graphs has proven to be difficult. In this work, we propose two models for graph sequences that capture: (a) link persistence between nodes across time, and (b) community persistence of each node across time. In the first model, we assume that the latent community of each node does not change over time, and in the second model we relax this assumption suitably. For both of these proposed models, we provide computationally efficient inference algorithms, whose unique feature is that they leverage community detection methods that work on single graphs. We also provide experimental results validating the suitability of the models and the performance of our algorithms on synthetic instances.