Dynamic extensions of Stochastic block model (SBM) are of importance in several fields that generate temporal interaction data. These models, besides producing compact and interpretable network representations, can be useful in applications such as link prediction or network forecasting. In this paper we present a conditional pseudo-likelihood based extension to dynamic SBM that can be efficiently estimated by optimizing a regularized objective. Our formulation leads to a highly scalable approach that can handle very large networks, even with millions of nodes. We also extend our formalism to causal impact for networks that allows us to quantify the impact of external events on a time dependent sequence of networks. We support our work with extensive results on both synthetic and real networks.
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On Dynamic Network Models and Application to Causal Impact
Semantic Scholar · Computer Science · 2019
Abstract
Dynamic extensions of Stochastic block model (SBM) are of importance in several fields that generate temporal interaction data. These models, besides producing compact and interpretable network representations, can be useful in applications such as link prediction or network forecasting. In this paper we present a conditional pseudo-likelihood based extension to dynamic SBM that can be efficiently estimated by optimizing a regularized objective. Our formulation leads to a highly scalable approach that can handle very large networks, even with millions of nodes. We also extend our formalism to causal impact for networks that allows us to quantify the impact of external events on a time dependent sequence of networks. We support our work with extensive results on both synthetic and real networks.