dc.contributor.author | Grattarola, D | |
dc.contributor.author | Zambon, D | |
dc.contributor.author | Livi, L | |
dc.contributor.author | Alippi, C | |
dc.date.accessioned | 2019-10-15T10:26:29Z | |
dc.date.issued | 2019-07-30 | |
dc.description.abstract | The space of graphs is often characterized by a nontrivial geometry, which complicates learning and inference in practical applications. A common approach is to use embedding techniques to represent graphs as points in a conventional Euclidean space, but non-Euclidean spaces have often been shown to be better suited for embedding graphs. Among these, constant-curvature Riemannian manifolds (CCMs) offer embedding spaces suitable for studying the statistical properties of a graph distribution, as they provide ways to easily compute metric geodesic distances. In this paper, we focus on the problem of detecting changes in stationarity in a stream of attributed graphs. To this end, we introduce a novel change detection framework based on neural networks and CCMs, which takes into account the non-Euclidean nature of graphs. Our contribution in this paper is twofold. First, via a novel approach based on adversarial learning, we compute graph embeddings by training an autoencoder to represent graphs on CCMs. Second, we introduce two novel change detection tests operating on CCMs. We perform experiments on synthetic data, as well as two real-world application scenarios: the detection of epileptic seizures using functional connectivity brain networks and the detection of hostility between two subjects, using human skeletal graphs. Results show that the proposed methods are able to detect even small changes in a graph-generating process, consistently outperforming approaches based on Euclidean embeddings. | en_GB |
dc.description.sponsorship | Swiss National Science Foundation | en_GB |
dc.description.sponsorship | Canada Research Chairs Program | en_GB |
dc.identifier.citation | Published online 30 July 2019 | en_GB |
dc.identifier.doi | 10.1109/TNNLS.2019.2927301 | |
dc.identifier.uri | http://hdl.handle.net/10871/39205 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_GB |
dc.relation.url | https://www.ncbi.nlm.nih.gov/pubmed/31380770 | 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 | Manifolds | en_GB |
dc.subject | Geometry | en_GB |
dc.subject | Extraterrestrial measurements | en_GB |
dc.subject | Topology | en_GB |
dc.subject | Monitoring | en_GB |
dc.subject | Data models | en_GB |
dc.subject | Adversarial learning | en_GB |
dc.subject | change detection test (CDT) | en_GB |
dc.subject | constant-curvature manifold | en_GB |
dc.subject | graph stream | en_GB |
dc.subject | seizure prediction | en_GB |
dc.title | Change detection in graph streams by learning graph embeddings on constant-curvature manifolds. | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-10-15T10:26:29Z | |
dc.identifier.issn | 2162-237X | |
exeter.place-of-publication | United States | en_GB |
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 Neural Networks and Learning Systems | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-07-30 | |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-07-30 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-10-15T10:14:55Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-10-15T10:26:34Z | |
refterms.panel | B | en_GB |