2018
DOI: 10.1002/widm.1286
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Change detection in dynamic attributed networks

Isuru U. Hewapathirana

Abstract: A network provides powerful means of representing complex relationships between entities by abstracting entities as vertices, and relationships as edges connecting vertices in a graph. Beyond the presence or absence of relationships, a network may contain additional information that can be attributed to the entities and their relationships. Attaching these additional attribute data to the corresponding vertices and edges yields an attributed graph. Moreover, in the majority of real‐world applications, such as … Show more

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Cited by 9 publications
(6 citation statements)
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“…15 The four tensor slices with edge distribution F 1 , during group-change scenario for complex situation Fig. 16 The four tensor slices with edge distribution F 1 , during hetero-to-homo for complex situation Fig. 17 The four tensor slices with edge distribution F 1 , during fragment for complex situation the baseline methods.…”
Section: Resultsmentioning
confidence: 99%
See 2 more Smart Citations
“…15 The four tensor slices with edge distribution F 1 , during group-change scenario for complex situation Fig. 16 The four tensor slices with edge distribution F 1 , during hetero-to-homo for complex situation Fig. 17 The four tensor slices with edge distribution F 1 , during fragment for complex situation the baseline methods.…”
Section: Resultsmentioning
confidence: 99%
“…One of the important tasks in dynamic network analysis is change detection, where the objective is to continuously monitor a network for changes in the behavior of entities and their relationships. Since this problem has many vital applications, a lot of solutions have been proposed in the literature [2,16,20,38,51].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Some articles mix both of those proposals in the same model ( [17]). As presented in [14]: a (discrete) dynamic weighted network can be mathematically represented as a time sequence of weighted graphs, (Gt = (Vt, Et, ωt)) t∈N , where Vt and Et are the set of vertices and the set of edges, respectively, and ωt : Et → R + is a weight function at time t. If the set of vertices remains unchanged over time; that is, Vt = V for all t, then the weighted graph at any time t will be simply Gt = (V, Et, ωt). In this case, a dynamic weighted network can be encoded as a weighted matrix, W (t) = (Wi,j(t)), where Wi,j(t) is the weight of the edge between vertices i and j at time t. Now, by applying the method described in Subsection 2.2 on each weighted graph Gt, we end up with a times series of persistence diagrams (∆t)t. To induce a scalar time series from (∆t)t, we consider a fixed persistence diagram ∆, then the 2-Wasserstein distances, W2(∆, ∆t), between the persistence diagrams ∆t and ∆, give rise to a (scalar) time series.…”
Section: Description Of the Methodsmentioning
confidence: 99%
“…A network is an efficient representation of a given set of entities and the existing relationships between them. Actually a network is a graph which entities are the vertices and the relations between them are presented as edges (see for instance [14] and [17]). Usually, these relations are quantified by (mostly positive) real numbers and in this case we say that the graph (the network) is weighted.…”
Section: Introductionmentioning
confidence: 99%