Point Process Modelling for Directed Interaction Networks

Abstract

Summary. Network data often take the form of repeated interactions between senders and receivers tabulated over time. A primary question to ask of such data is which traits and behaviors are predictive of interaction. To answer this question, a model is introduced for treating directed interactions as a multivariate point process: a Cox multiplicative intensity model using covariates that depend on the history of the process. Consistency and asymptotic normality are proved for the resulting partial-likelihood-based estimators under suitable regularity conditions, and an efficient fitting procedure is described. Multicast interactions-those involving a single sender but multiple receivers-are treated explicitly. The resulting inferential framework is then employed to model message sending behavior in a corporate e-mail network. The analysis gives a precise quantification of which static shared traits and dynamic network effects are predictive of message recipient selection.

Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events

“…In line with the basic SAOM (Snijders 1996), and similar to previous approaches Vu et al 2011;Perry and Wolfe 2013), the proposed model is a two-step process with a linear predictor at its core. In the first step, the waiting time until an actor becomes active to send an event is modeled.…”

“…We believe that this is a good time for proposing a new method to enrich the toolbox of social scientists for the analysis of network data, given the constantly growing interest in analyzing relational events over the past decade. Recent examples include the study of communicational dynamics of animals (Tranmer et al 2015) and emergency responders (Butts 2008), brokerage and receiver choice in communication networks (Quintane and Carnabuci 2016;Stadtfeld, Geyer-Schulz, and Allmendinger 2011), e-mail communication in organizations (Perry and Wolfe 2013), interaction within teams (Leenders, Contractor, and DeChurch 2016), exchange of patients between hospitals (Kitts et al 2017;Vu et al 2017), and collaboration on online platforms (Vu et al 2011). Further, international conflict relations were analyzed in a relational event framework (Lerner et al 2013).…”

“…Rather, under the assumption of proportional hazards, Cox models are unconcerned about the actual underlying distribution. Perry and Wolfe (2013) proposed proportional hazard estimation for event data. This is reasonable as long as the model still assumes piecewise stable process states as in this article; this means that Cox models also need to consider right-censored intervals.…”

A scalable signal processing architecture for massive graph analysis

“…There are also clusters corresponding to subfields of a topic area; for example, chemistry is split into analytical, applied, and other subfields. Using this information has the potential to increase our modeling ability, providing a better expected value model than the one discussed in Section 2.3 using tools from link prediction and the point process model of [9].…”

Dynamic Network Actor Models: Investigating Coordination Ties through Time

“…However, this model conceives of actors as primarily issuing ties and cannot deal with undirected ties that are the outcome of a mutual agreement between two involved parties. Related actor-oriented models have been proposed, for example, by Perry and Wolfe (2013). The conceptual differences between SAOMs and ERGMs as presented above equally apply to actor-oriented and tie-oriented relational event models.…”