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    188宝金博页面版: What effects topological changes in dynamic graphs

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    内容提示： ORIGINAL ARTICLEWhat effects topological changes in dynamic graphs?Elucidating relationships between vertex attributes and the graph structureMehdi Kaytoue 1 ? Yoann Pitarch 2 ? Marc Plantevit 3 ? Ce?line Robardet 1Received: 21 January 2015/Revised: 20 July 2015/Accepted: 5 September 2015/Published online: 22 September 2015? Springer-Verlag Wien 2015Abstract To describe the dynamics taking place in net-works that structurally change over time, we propose anapproach to search for vertex attributes whose...

    文档格式：PDF | 页数：17 | 浏览次数：9 | 上传日期：2016-01-21 22:40:33 | 文档星级：
    ORIGINAL ARTICLEWhat effects topological changes in dynamic graphs?Elucidating relationships between vertex attributes and the graph structureMehdi Kaytoue 1 • Yoann Pitarch 2 • Marc Plantevit 3 • Ce´line Robardet 1Received: 21 January 2015/Revised: 20 July 2015/Accepted: 5 September 2015/Published online: 22 September 2015? Springer-Verlag Wien 2015Abstract To describe the dynamics taking place in net-works that structurally change over time, we propose anapproach to search for vertex attributes whose valuechanges impact the topology of the graph. In severalapplications, it appears that the variations of a group ofattributes are often followed by some structural changes inthe graph that one may assume they generate. We for-malize the triggering pattern discovery problem as amethod jointly rooted in sequence mining and graphanalysis. We apply our approach on three real-worlddynamic graphs of different natures—a co-authoring net-work, an airline network, and a social bookmarking sys-tem—assessing the relevancy of the triggering patternmining approach.Keywords Data mining ? Mining methods and analysis ?Attributed graph mining ? Topological patterns ? Dynamicgraphs1 IntroductionIn the last years, graph mining has become a critical area ofresearch but also an important tool for uncovering phe-nomena hidden in social networks. It allows a betterunderstanding of their nature but also of the Humaninteractions and behaviors on the Web, and provides asupport for many tasks such as social recommendations(Jiang et al. 2012), community discovery (Girvan andNewman 2002), social inf l uence propagation (Goyal et al.2013), and link prediction (Bringmann et al. 2010). Indeed,real-world phenomena such as social interactions are oftendepicted by graphs whose vertices represent entities andedges represent their relationships or interactions. With therapid development of social media, sensor technologies andbioinformatic assay tools, such kind of graph abstractionhas become ubiquitous. By nature, most of these systemsare dynamic. Vertices and edges may appear or disappearin time. Besides, the status of a vertex is often described byattributes whose values also change over time. A timelychallenge is thus the design of effective graph miningmethods to discover actionable insights in such dynamicattributed graphs, to bring new knowledge on the commonrules that govern the networks transformations.In data mining, dynamic graphs have been analyzedfrom two main research tracks: (a) the study of the prop-erties that describe the topology of the graph (de Meloet al. 2011; Tong et al. 2008), or (b) the extraction ofspecif i c subgraphs to describe the graph evolution (Ber-lingerio et al. 2009; Robardet 2009; You et al. 2009). Veryfew approaches (Desmier et al. 2013) extract patterns thatcombine information about attribute values and graphtopology, but fail to identify the temporal relationships thatmay exist between the changes of these two components. Inthis paper, we strive to elucidate the temporal relationships& Mehdi Kaytouemehdi.kaytoue@insa-lyon.frYoann Pitarchyoann.pitarch@irit.frMarc Plantevitmarc.plantevit@liris.cnrs.frCe ´line Robardetceline.robardet@insa-lyon.fr1INSA-Lyon, CNRS, LIRIS UMR5205,69621 Villeurbanne Cedex, France2Universite´ de Toulouse, CNRS, IRIT UMR5505,31071 Toulouse, France3Universite´ Claude Bernard Lyon 1, CNRS, LIRIS UMR5205,69621 Villeurbanne Cedex, France123Soc. Netw. Anal. Min. (2015) 5:55DOI 10.1007/s13278-015-0294-9

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