We propose a novel probabilistic framework to model continuous-time interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal interactions among entities, and also to exploit how the community structure influences the interaction dynamics among these nodes. To this end, we model the reciprocating interactions between individuals using mutually-exciting Hawkes processes. The base rate of the Hawkes process for each pair of individuals is built upon the latent representations inferred using the hierarchical gamma process edge partition model (HGaP-EPM). In particular, our model allows the interaction dynamics between each pair of individuals to be modulated by their respective affiliated communities. Moreover, our model can flexibly incorporate the auxiliary individuals' attributes, or covariates associated with interaction events. Efficient Gibbs sampling and Expectation-Maximization algorithms are developed to perform inference via Pólya-Gamma data augmentation strategy. Experimental results on real-world datasets demonstrate that our model not only achieves competitive performance for temporal link prediction compared with state-of-the-art methods, but also discovers interpretable latent structure behind the observed temporal interactions.
Spectral clustering for dependent community Hawkes process models of temporal networks
Department of Statistics The Ohio State University（统计学系俄亥俄州立大学）；Department of Electrical Engineering and Computer Science University of Toledo（电气工程与计算机科学系托莱多大学）；Department of Computer and Data Sciences Case Western Reserve University（计算机与数据科学系凯斯西储大学）
Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis
School of Artificial Intelligence and Advanced Computing, Xi’an Jiaotong-Liverpool University（人工智能与先进计算学院，西安交通大学-利物浦大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Monash University（墨尔本大学）；Fisk University（菲斯克大学）
