Imperial College London

Professor Nick Heard

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics



+44 (0)20 7594 1490n.heard Website




543Huxley BuildingSouth Kensington Campus






BibTex format

author = {Sanna, Passino F and Heard, NA},
doi = {10.3934/fods.2019013},
journal = {Foundations of Data Science},
pages = {293--306},
title = {Modelling dynamic network evolution as a Pitman-Yor process},
url = {},
volume = {1},
year = {2019}

RIS format (EndNote, RefMan)

AB - Dynamic interaction networks frequently arise in biology, communications technology and the social sciences, representing, for example, neuronal connectivity in the brain, internet connections between computers and human interactions within social networks. The evolution and strengthening of the links in such networks can be observed through sequences of connection events occurring between network nodes over time. In some of these applications, the identity and size of the network may be unknown a priori and may change over time. In this article, a model for the evolution of dynamic networks based on the Pitman-Yor process is proposed. This model explicitly admits power-laws in the number of connections on each edge, often present in real world networks, and, for careful choices of the parameters, power-laws for the degree distribution of the nodes. A novel empirical method for the estimation of the hyperparameters of the Pitman-Yor process is proposed, and some necessary corrections for uniform discrete base distributions are carefully addressed. The methodology is tested on synthetic data and in an anomaly detection study on the enterprise computer network of the Los Alamos National Laboratory, and successfully detects connections from a red-team penetration test.
AU - Sanna,Passino F
AU - Heard,NA
DO - 10.3934/fods.2019013
EP - 306
PY - 2019///
SN - 2639-8001
SP - 293
TI - Modelling dynamic network evolution as a Pitman-Yor process
T2 - Foundations of Data Science
UR -
UR -
VL - 1
ER -