TY - GEN
T1 - Discovering latent network structure in point process data
AU - Linderman, Scott W.
AU - Adams, Ryan P.
N1 - Publisher Copyright:
Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved.
PY - 2014
Y1 - 2014
N2 - Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include economic interactions linking financial instruments and patterns of reciprocity in gang violence. In these cases, we are limited to noisy observations of events associated with each node. To enable analysis of these implicit networks, we develop a probabilistic model that combines mutually- exciting point processes with random graph models. We show how the Poisson superposition principle enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm. We evaluate this new model empirically on several datasets.
AB - Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include economic interactions linking financial instruments and patterns of reciprocity in gang violence. In these cases, we are limited to noisy observations of events associated with each node. To enable analysis of these implicit networks, we develop a probabilistic model that combines mutually- exciting point processes with random graph models. We show how the Poisson superposition principle enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm. We evaluate this new model empirically on several datasets.
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M3 - Conference contribution
AN - SCOPUS:84919792106
T3 - 31st International Conference on Machine Learning, ICML 2014
SP - 3268
EP - 3281
BT - 31st International Conference on Machine Learning, ICML 2014
PB - International Machine Learning Society (IMLS)
T2 - 31st International Conference on Machine Learning, ICML 2014
Y2 - 21 June 2014 through 26 June 2014
ER -