dc.contributor.author | Zambon, D | |
dc.contributor.author | Alippi, C | |
dc.contributor.author | Livi, L | |
dc.date.accessioned | 2019-12-09T10:20:11Z | |
dc.date.issued | 2019-12-06 | |
dc.description.abstract | Given a finite sequence of graphs, e.g. coming from
technological, biological, and social networks, the paper proposes
a methodology to identify possible changes in stationarity in the
stochastic process that generated such graphs. We consider a
general family of attributed graphs for which both topology
(vertices and edges) and associated attributes are allowed to
change over time, without violating the stationarity hypothesis.
Novel Change-Point Methods (CPMs) are proposed that map
graphs onto vectors, apply a suitable statistical test in vector
space and detect changes –if any– according to a user-defined
confidence level; an estimate for the change point is provided
as well. In particular, we propose two multivariate CPMs: one
designed to detect shifts in the mean, the other to address
more complex changes affecting the distribution. We ground
our methods on theoretical results that show how the inference
in the numerical vector space is related to the one in graph
domain, and vice-versa. We also extend the methodology to
handle multiple changes occurring in a single sequence. Results
show the effectiveness of what proposed in relevant application
scenarios. | en_GB |
dc.description.sponsorship | Swiss National Science Foundation | en_GB |
dc.identifier.citation | Published online 15 November 2019 | en_GB |
dc.identifier.doi | 10.1109/TSP.2019.2953596 | |
dc.identifier.grantnumber | 200021_172671 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/40023 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be
obtained for all other uses, in any current or future media, including
reprinting/republishing this material for advertising or promotional purposes, creating new
collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted
component of this work in other works. | en_GB |
dc.subject | Change-point analysis | en_GB |
dc.subject | Graphs | en_GB |
dc.subject | Graph process | en_GB |
dc.subject | Change in stationarity | en_GB |
dc.title | Change-point methods on a sequence of graphs | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-12-09T10:20:11Z | |
dc.identifier.issn | 1053-587X | |
dc.description | This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record | en_GB |
dc.identifier.journal | IEEE Transactions on Signal Processing | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-11-07 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-12-06 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-12-06T17:11:55Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-12-09T10:20:13Z | |
refterms.panel | B | en_GB |