Point process modeling for directed interaction networks

TL;DR

This paper introduces a Cox multiplicative intensity model for directed interaction networks, enabling analysis of how traits and behaviors predict repeated sender-receiver interactions over time.

Contribution

It develops a novel point process model for directed interactions, proves its statistical properties, and applies it to real email network data for predictive insights.

Findings

Identifies traits and network effects influencing message recipient choice

Provides a consistent and asymptotically normal estimator for the model

Demonstrates the model's effectiveness on corporate email data

Abstract

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.