Graph link prediction in computer networks using Poisson matrix
factorisation
factorisation
File(s)
Working paper
Author(s)
Sanna Passino, Francesco
Turcotte, Melissa JM
Heard, Nicholas A
Type
Journal Article
Abstract
Graph link prediction is an important task in cyber-security: relationships
between entities within a computer network, such as users interacting with
computers, or system libraries and the corresponding processes that use them,
can provide key insights into adversary behaviour. Poisson matrix factorisation
(PMF) is a popular model for link prediction in large networks, particularly
useful for its scalability. In this article, PMF is extended to include
scenarios that are commonly encountered in cyber-security applications.
Specifically, an extension is proposed to explicitly handle binary adjacency
matrices and include known covariates associated with the graph nodes. A
seasonal PMF model is also presented to handle dynamic networks. To allow the
methods to scale to large graphs, variational methods are discussed for
performing fast inference. The results show an improved performance over the
standard PMF model and other common link prediction techniques.
between entities within a computer network, such as users interacting with
computers, or system libraries and the corresponding processes that use them,
can provide key insights into adversary behaviour. Poisson matrix factorisation
(PMF) is a popular model for link prediction in large networks, particularly
useful for its scalability. In this article, PMF is extended to include
scenarios that are commonly encountered in cyber-security applications.
Specifically, an extension is proposed to explicitly handle binary adjacency
matrices and include known covariates associated with the graph nodes. A
seasonal PMF model is also presented to handle dynamic networks. To allow the
methods to scale to large graphs, variational methods are discussed for
performing fast inference. The results show an improved performance over the
standard PMF model and other common link prediction techniques.
Date Issued
2022-09
Date Acceptance
2021-09-05
Citation
Annals of Applied Statistics, 2022, 16 (3), pp.1313-1332
ISSN
1932-6157
Publisher
Institute of Mathematical Statistics
Start Page
1313
End Page
1332
Journal / Book Title
Annals of Applied Statistics
Volume
16
Issue
3
Copyright Statement
© 2021 The Author(s)
Identifier
http://arxiv.org/abs/2001.09456v1
Subjects
stat.AP
stat.AP
cs.SI
stat.AP
stat.AP
cs.SI
Publication Status
Accepted