Trend Mining in Dynamic Attributed Graphs

Desmier, Elise; Plantevit, Marc; Robardet, Céline; Boulicaut, Jean-François

doi:10.1007/978-3-642-40988-2_42

Cited by 18 publications

(11 citation statements)

References 20 publications

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“…Thus, dynamic ontologies describe the same static knowledge but assume its incompleteness. So do the dynamic graphs (Demetrescu et al, 2005) and dynamic attributed graphs (Desmier et al, 2013), which perhaps can be applied to represent dynamic knowledge but this is not their virtue denoted as "dynamic". Similarly to ontologies, "dynamic" in this case means "editable" and refers not to the knowledge, but to the model that describes it and to the method to create such model.…”

Section: Event-based Approachmentioning

confidence: 99%

Knowledge engineering in volcanology: Practical claims and general approach

Pshenichny

2014

Journal of Volcanology and Geothermal Research

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Section: Event-based Approachmentioning

confidence: 99%

Knowledge engineering in volcanology: Practical claims and general approach

Pshenichny

2014

Journal of Volcanology and Geothermal Research

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“…In [9], the authors define a new a pattern that relies on the graph structure and the temporal evolution of the attribute values. It enables to discover set of vertices satisfying a maximum diameter constraint that follow the same trends w.r.t.…”

Section: Related Workmentioning

confidence: 99%

“…From lines 9 to 11, prefix-closed sequences ending by a topological variation are built from closed sequences, and the growth-rate is computed (in negligible time, see section V). C ←TRIGAT_enum(s, Δ |s , Gaggr, minCov) 6: end for 7: Eliminate non-closed sequences from C 8: C ← prefix closed patterns s, X k ∈ C, s.t.X k ∈ (M × S) 9: for all P = s, X k ∈ C do 10: Add P to P if GR( s, X k , Δ X k ) ≥ minGr 11 We provide an empirical evaluation of our methodology. Experiments were performed on 2.5GHz and 16GB of RAM machines and TRIGAT is written in C++ 2 .…”

Section: Algorithm Trigatmentioning

confidence: 99%

“…However, very few approaches [9] extract patterns that combine information about attribute values and graph topology, but do not identify the temporal relationships that may exist between the changes of these two components. In this paper we seek to elucidate the temporal relationships between the evolution of vertex attribute values and the graph structure.…”

Section: Introductionmentioning

confidence: 99%

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Triggering patterns of topology changes in dynamic graphs

Kaytoue

Pitarch

Plantevit

et al. 2014

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014)

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To describe the dynamics taking place in networks that structurally change over time, we propose an approach to search for attributes whose value changes impact the topology of the graph. In several applications, it appears that the variations of a group of attributes are often followed by some structural changes in the graph that one may assume they generate. We formalize the triggering pattern discovery problem as a method jointly rooted in sequence mining and graph analysis. We apply our approach on three real-world dynamic graphs of different natures -a co-authoring network, an airline network, and a social bookmarking system -assessing the relevancy of the triggering pattern mining approach.

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“…Dynamic attributed graphs In Desmier et al (2013), Desmier et al define a new pattern domain that relies on the graph structure and the temporal evolution of the attribute values. It makes it possible to discover subgraphs of small diameter whose vertex attributes follow the same trends.…”

Section: Related Workmentioning

confidence: 99%

Exceptional contextual subgraph mining

et al. 2017

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Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a χ 2 test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.

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Trend Mining in Dynamic Attributed Graphs

Cited by 18 publications

References 20 publications

Knowledge engineering in volcanology: Practical claims and general approach

Knowledge engineering in volcanology: Practical claims and general approach

Triggering patterns of topology changes in dynamic graphs

Exceptional contextual subgraph mining

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