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dc.contributor.authorFournier-Viger, Philippeen_NZ
dc.contributor.authorHe, Ganghuanen_NZ
dc.contributor.authorLin, Jerry Chun-Weien_NZ
dc.contributor.authorGomes, Heitor Muriloen_NZ
dc.contributor.editorSong, Minen_NZ
dc.contributor.editorSong, Il-Yeolen_NZ
dc.contributor.editorKotsis, Gabrieleen_NZ
dc.contributor.editorTjoa, A Minen_NZ
dc.contributor.editorKhalil, Ismailen_NZ
dc.coverage.spatialBratislava, Slovakiaen_NZ
dc.date.accessioned2021-02-09T01:46:54Z
dc.date.available2021-02-09T01:46:54Z
dc.date.issued2020en_NZ
dc.identifier.citationFournier-Viger, P., He, G., Lin, J. C.-W., & Gomes, H. M. (2020). Mining attribute evolution rules in dynamic attributed graphs. In M. Song, I.-Y. Song, G. Kotsis, A. M. Tjoa, & I. Khalil (Eds.), Proceeding of 22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2020) (Vol. LNCS 12393, pp. 167–182). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-59065-9_14en
dc.identifier.isbn9783030590642en_NZ
dc.identifier.issn0302-9743en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/14107
dc.description.abstractA dynamic attributed graph is a graph that changes over time and where each vertex is described using multiple continuous attributes. Such graphs are found in numerous domains, e.g., social network analysis. Several studies have been done on discovering patterns in dynamic attributed graphs to reveal how attribute(s) change over time. However, many algorithms restrict all attribute values in a pattern to follow the same trend (e.g. increase) and the set of vertices in a pattern to be fixed, while others consider that a single vertex may influence its neighbors. As a result, these algorithms are unable to find complex patterns that show the influence of multiple vertices on many other vertices in terms of several attributes and different trends. This paper addresses this issue by proposing to discover a novel type of patterns called attribute evolution rules (AER). These rules indicate how changes of attribute values of multiple vertices may influence those of others with a high confidence. An efficient algorithm named AER-Miner is proposed to find these rules. Experiments on real data show AER-Miner is efficient and that AERs can provide interesting insights about dynamic attributed graphs.en_NZ
dc.language.isoen
dc.publisherSpringeren_NZ
dc.rightsThis is a post-peer-review, pre-copyedit version of an article published in Big Data Analytics and Knowledge Discovery. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-030-59065-9_14”
dc.sourceDaWaK 2020en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdynamic graphsen_NZ
dc.subjectattributed graphsen_NZ
dc.subjectpattern miningen_NZ
dc.subjectattribute evolution rulesen_NZ
dc.titleMining attribute evolution rules in dynamic attributed graphsen_NZ
dc.typeConference Contribution
dc.identifier.doi10.1007/978-3-030-59065-9_14en_NZ
dc.relation.isPartOfProceeding of 22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2020)en_NZ
pubs.begin-page167
pubs.elements-id257596
pubs.end-page182
pubs.finish-date2020-09-17en_NZ
pubs.place-of-publicationCham, Switzerland
pubs.publication-statusPublisheden_NZ
pubs.start-date2020-09-14en_NZ
pubs.volumeLNCS 12393en_NZ
dc.identifier.eissn1611-3349en_NZ


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