egonetcloud: event-based egocentric dynamic network
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EgoNetCloud: Event-based Egocentric Dynamic Network
VisualizationQingsong Liu, Yifan Hu, Lei Shi, Xinzhu Mu, Yutao Zhang, Jie Tang
IEEE VIS 2015
Presented by: Dylan
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Context Event-based Egocentric Dynamic Network• time-varying graph
discrete time point
continuous time period
time set
activation time
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Context Event-based Egocentric Dynamic Network• in event-based network,
discrete time point (continuous time period) of the edge is associated with an event
• every dynamic network can be seen as event-based
• establishing a friendship tie in online social networkssending a mobile short message
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Context Event-based Egocentric Dynamic Network• subgraph of the full-scale graph
• node: ego node vs. alter node
• edge: ego -> alter; alter -> alter
• help understand the role of the ego in full-scale network
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Problems• visual clutter
• edge crossing
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Goals• reveal egocentric network structure
• reveal the temporal dynamics of the ego/ alter nodes
• requirements on performance, visual metaphor, layout constraint
• redesign interaction
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Contributions• Data-driven empirical algorithms: prune, compress and filter
networks into smaller but more informative abstractions
• EgoNetCloud visual metaphor and interactions: display and explore both the egocentric network structure and their temporal dynamics
• Fast and constrained layout computation: fulfill requirement of the new visual metaphor and maintain fine readability
• Comprehensive evaluations: demonstrate the effectiveness of the EgoNetCloud design through a user study and a real-world case study
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Levels of DesignDomain situationObserve target users using existing tools
Visual encoding/interaction idiomJustify design with respect to alternatives
AlgorithmMeasure system time/memoryAnalyze computational complexity
Observe target users after deployment (field study)
Measure adoption
Analyze results qualitativelyMeasure human time with lab experiment (user study)
Data/task abstraction
problem-driven work
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FrameworkSystem EgoNetCloud
What: Data Event-based egocentric dynamic network data
Why: Tasks Identify clusters, values, trends
How: Encode Nodes linked with connections; size; category colors;
How: Reduce Edge pruning; node compression; graph filtering
How: Manipulate Select
How: Facet NetCloud; EgoCloud; Static Ego Network
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How
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Edge Pruning• remove low-weight edges
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prune as many edges as possible
retain important edges
preserve the connectivity
smallest connected maximum weighted spanning
graph
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• authors not listed in alphabetical order
• sparse matrix
• cosine similarity as weight
• recency based scaling: inverse of paper’s age
• author ordering based scaling
• authors listed in alphabetical order
• credit allocation algorithm [Shen, H. W., & Barabási, A. L. (2014). Collective credit allocation in science. Proceedings of the National Academy of Sciences, 111(34), 12325-12330.]
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Node Compression• group nodes with the same or similar
connection pattern
• graph adjacency matrix
• merge nodes with exactly the same connectivity
• merge nodes with the same connectivity and linked to each other
• fuzzy compression
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Graph Filtering• reduce nodes and related edges by rule-based policy
• importance degree
• time period
• # citations
• # collaborations
• # publications
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Layout Algorithm• initial layout
• alter’s interaction time & frequency with ego
• constrained stress majorization approach
• deal with position constraints
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EgoNetCloud
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Case Study
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User Study• temporal information related
• the egocentric network related
• a combination of the two
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Critique• suspicious about result of weighted graphs
• nodes compression algorithm for unweighted graphs
• “no edge in the complement of the simplified subgraph has weight greater than any of the edges in this subgraph”
• efficiency should be 1
• can’t see the particular benefit apply to other networks
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Questions
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