Tracking changes in dynamic information networks

Takaffoli, Mansoureh; Fagnan, Justin; Sangi, Farzad; Zaı̈ane, Osmar R.

doi:10.1109/cason.2011.6085925

Cited by 38 publications

(34 citation statements)

References 22 publications

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“…The community structure of dynamic graphs can be specified manually or derived using automatic approaches. In case of an automatic detection, two general approaches are available [For10]: First, in two‐stage approaches, the community structure is derived for each

G_{t}

independently and relationships between communities at different time steps are inferred successively using community tracking methods [GDC11, TFSZ11]. Second, evolutionary community detection approaches incorporate both, the graph structure of the current and of previous time steps, to determine the evolving community structure of

scriptG

.…”

Section: Data Model and Community Detectionmentioning

confidence: 99%

“…We define it based on Jaccard similarity of communities, similar to Takaffoli et al . [TFSZ11]:

stab (v) = \frac{1}{a - 1} \sum_{n = 1}^{a - 1} sim (C (v, n), C (v, n + 1)),

where function

C (v, n)

returns the

n^{th}

community to which v belongs and a is the number of communities v is part of, that is,

a \leq T

. If the dynamic community to which a vertex belongs does not change over time,

stab (v) = 1

is maximal.…”

Section: Visualization Techniquementioning

confidence: 99%

“…The stability of a vertex v describes its tendency to belong to similar communities over time. We define it based on Jaccard similarity of communities, similar to Takaffoli et al [TFSZ11]:…”

Section: Vertex Stability Colouringmentioning

confidence: 99%

See 2 more Smart Citations

Visualizing the Evolution of Communities in Dynamic Graphs

Vehlow

Beck

Auwärter

et al. 2014

Computer Graphics Forum

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The community structure of graphs is an important feature that gives insight into the high‐level organization of objects within the graph. In real‐world systems, the graph topology is oftentimes not static but changes over time and hence, also the community structure changes. Previous timeline‐based approaches either visualize the dynamic graph or the dynamic community structure. In contrast, our approach combines both in a single image and therefore allows users to investigate the community structure together with the underlying dynamic graph. Our optimized ordering of vertices and selection of colours in combination with interactive highlighting techniques increases the traceability of communities along the time axis. Users can identify visual signatures, estimate the reliability of the derived community structure and investigate whether community evolution interacts with changes in the graph topology. The utility of our approach is demonstrated in two application examples.

show abstract

G_{t}

scriptG

.…”

Section: Data Model and Community Detectionmentioning

confidence: 99%

“…We define it based on Jaccard similarity of communities, similar to Takaffoli et al . [TFSZ11]:

stab (v) = \frac{1}{a - 1} \sum_{n = 1}^{a - 1} sim (C (v, n), C (v, n + 1)),

where function

C (v, n)

returns the

n^{th}

community to which v belongs and a is the number of communities v is part of, that is,

a \leq T

. If the dynamic community to which a vertex belongs does not change over time,

stab (v) = 1

is maximal.…”

Section: Visualization Techniquementioning

confidence: 99%

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Visualizing the Evolution of Communities in Dynamic Graphs

Vehlow

Beck

Auwärter

et al. 2014

Computer Graphics Forum

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“…Another line of research proposes to investigate the problem of modeling and discovering evolutions of communities in dynamic networks in terms of event-based frameworks (e.g. [1,14,21,27,26]), taking advantages from well-known paradigms developed in this scientific area.…”

Section: Related Workmentioning

confidence: 99%

DynamicNet

Cuzzocrea

Folino²,

Pizzuti³

2013

Proceedings of the 17th International Database Engineering &Amp; Applications Symposium on - IDEAS '13

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DynamicNet, an effective and efficient algorithm for supporting community evolution detection in time-evolving information networks is presented and experimentally evaluated in this paper. Dy-namicNet introduces a graph-based model-theoretic approach to represent time-evolving information networks, and to capture how they change over time. A central feature of DynamicNet is represented by the ability of supporting matching-based community evolution detection, by identifying several classes of community transitions. Experimental results clearly demonstrate the reliability and the efficiency of our proposal.

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“…However, this type of modeling captures neither the temporal aspect nor the evolution of the network. Recent methods [3], [4], [5], model the graph as a series of frozen networks, where each network corresponds to a particular point in time. Such modeling has been useful to detect structural changes in the network [4], [5], [6], [7], [8] and to reveal important network information.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

He¹,

Tajeuna

Wang

et al. 2017

2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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In real-world social networks, there is an increasing interest in tracking the evolution of groups of users and detecting the various changes they are liable to undergo. Several approaches have been proposed for this. In studying these approaches, we observed that most of them use a two-stage process. In the first stage, they run an algorithm to identify groups of users at each timestamp. In the second stage, a pairwise comparison based on a similarity measure is employed to track groups of users and detect changes they may undergo. While the majority of existing approaches use a two-stage process, they all run different algorithms to identify communities and rely on different similarity measures to track groups of users over time. Noting that the different approaches may perform differently depending on the dynamic social network under investigation, we decided to make a high level survey of some existing tracking approaches and then do a comparative analysis of some of them. In our analysis, we compared the algorithms in two main situations: (1) when groups of users do not overlap and (2) when the groups are overlapping. The study was done on three different testbeds extracted from the DBLP, Autonomous System (AS) and Yelp datasets.

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Tracking changes in dynamic information networks

Cited by 38 publications

References 22 publications

Visualizing the Evolution of Communities in Dynamic Graphs

Visualizing the Evolution of Communities in Dynamic Graphs

DynamicNet

A Comparative Study of Different Approaches for Tracking Communities in Evolving Social Networks

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