supporting the visual analysis of dynamic networks by...

Supporting the Visual Analysisof Dynamic Networks by

Clustering associatedTemporal Attributes

Steffen HadlakHeidrun SchumannClemens H. CapTill Wollenberg

18.10.2013 University of Rostock | Steffen Hadlak : Supporting the Visual Analysis of Dynamic Networks by Clustering associated Temporal Attributes


Motivation

dynamic networks are important subjects, e.g.

• social networks• citation networks• power grids• communication networks

one important analysis goal = identification of certain subsets of nodes and edges

i.e. identifying groups that share similar attribute trends over time, e.g.

• stable, evolving or unreliable parts of the network

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Related Work

focus on structureanalyze each time point

focus on timeanalyze each node/edge

too many time points too many nodes and edges

abstraction into supergraph butloss of temporal

information

abstraction byclustering but

done separately foreach time point

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goal = identification of groups sharing similar attribute trends

basic idea• apply clustering to supergraph

regarding associated time-varying attributesutilize clustering for time series data

temporal clustering of dynamic networks

1. selection of time series

2. clustering of time series

3. defining connected subgraphs

Novel Approach

dynamic network

time series

clustered time series

clustered supergraph

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1. Selection of Time Series

straight forward if node/edge exists at all time points

gather all attribute values

decision needed when node/edge is missing at some time points

more similar?

missing indicator

structural co-existence behavior

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2. Clustering of Time Series

time series clustering requires

a similarity measure, e.g.

• Euclidean distance

• correlation coefficient

• trend-based similarity

a clustering method, e.g.

• k-means• hierarchical

~ ~/

~ ~/

~

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3. Defining connected Subgraphs

clustering is solely based on time seriesand does not consider structural information 2 additional steps

I. extraction of induced subgraphs

• add incident edges tocontained nodes

• add incident nodes tocontained edges

II. path-preserving clustering

• split subgraphs intoconnected components

nodes clusters

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Visual Design

visualization of groups sharing similar attribute trendsby multiple views

1) network view communicates groups and their relations

• node-link visualization of the clustered supergraph

• embedding of small glyphs

• foreground:major temporal trend

• background:cluster homogeneity

explore groups

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Visual Design

2) time series view communicates attribute trends

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Visual Design

2) time series view communicates attribute trends

• a time series for each group

• foreground:representative / average

• + standard deviation

• background:heterogeneity-bandsfor multiple temporal scales

explore attribute trends

icicle plot communicates clusterhomogeneity

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homogeneousinhomogeneous


Visual Design

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3) similarity view

provides controls to steer the clustering

communicates the similarity between all time series

allows the comparison of different clustering settings


Visual Design

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final setup


Use Case

wireless mesh networkaround Rostock, North Germany

• 290 nodes• 1888 edges with

link quality in [0, 1]

• 21,733 time points from 01/01 – 31/05/2011

goal = maintain a network quality satisfying all users

• identify general problems on a global level

• localize source of sporadic events on a local level map ©OpenStreetMap contributors

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Global Analysis

k-means with

goodoverall high

quality values

pooroverall high

quality values

averageoverall medium quality values

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new subgraph

disconnectedsubgraph

currently, our domain experts distinguish three levels of reliability of links:

5 clusters3 clusters


Global Analysis

hierarchical clustering

good

poor

average

average

new subgraph

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Local Analysis

events affecting :

all time series certain time series16


Local Analysis

good

poor

average

event affected 3 villages

clustered for selected time intervals17


Conclusion

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goal = identification of groups sharing similar attribute trends

temporal clustering of dynamic networks regarding time-varying attributes

• globally, i.e. regarding the whole temporal domain

• locally, i.e. regarding certain time intervals

visual design to communicate the groups and the associated time series


Future Work

approach was developed together with domain experts, however

user study may still reveal further insights for future extensions

• influence of attributes,similarity measure orclustering method

on the analysis goal

• additional viewsfor instance to support direct comparison of clustering results

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Thank you!

Questions?

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