NATO Advanced Study Institute on Mining Massive Data Sets for Security

Mining Networks through Visual Analytics:

author: Guy Melancon, INRIA

Description

Analysts are faced with massive collections gathering documents, events and actors from which they try to make sense, searching data to locate patterns and discover evidence. Visual and interactive exploration of data has now established as a fruitful strategy to tackle the problem posed by this abundance of information. The Visual Analytics initiative promotes the use of Information Visualization to support analytical reasoning through a sense-making loop based on which the analysis incrementally builds hypotheses.

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*Slides*
0:00	Mining Networks through Visual Analytics - Incremental Hypothesis Building and Validation
0:33	peacokmaps.com
0:35	InfoVis CyberInfraStructure – Pajek
0:39	Tulip – BubbleTree
0:41	Graph Viz Framework Tulip
0:45	Internet traffic
0:48	Voronoï Treemaps
0:50	Cushion Treemaps
0:53	Munzner’s Hyperbolic Browser
1:03	Tulip – Sugiyama Layout
1:05	Visualize? (1)
1:10	Visualize? (2)
2:29	Visual graph mining related to security issues
3:26	Example from NCTC data (1)
4:17	Example from NCTC data (2)
7:57	Example from NCTC data (3)
8:41	Massive data (1)
8:47	Massive data (2)
8:59	Visualization and Moore’s law (1)
9:55	Visualization and Moore’s law (2)
10:42	Added value of visual and interactive mining
11:38	« Sense making loop »
12:27	« Visualization mantras »
13:02	Visualization “pipeline”
14:10	Visualize?
14:50	Organize data prior to visualization
15:32	Case study: ITA 2000 passenger air traffic
18:11	Case study: ITA 2000 passenger air traffic
22:50	TopoLayout – (Topological) Feature-based Hierarchization (1)
24:21	TopoLayout – (Topological) Feature-based Hierarchization (2)
24:57	TopoLayout – (Topological) Feature-based Hierarchization (3)
25:42	TopoLayout – (Topological) Feature-based Hierarchization (4)
26:12	TopoLayout – (Topological) Feature-based Hierarchization (5)
26:36	TopoLayout – (Topological) Feature-based Hierarchization (6)
26:56	TopoLayout
27:44	TopoLayout + interaction: Grouse (1)
30:16	TopoLayout + interaction: Grouse (2)
30:28	TopoLayout + interaction: Grouse (3)
30:30	TopoLayout + interaction: Grouse (4)
31:28	Multilevel navigation of small world networks
33:30	Small world networks (1)
34:04	Multilevel navigation of small world networks
34:48	Small world networks (2)
35:48	Small world networks (3)
40:32	Small world networks (4)
41:00	Small world networks (5)
41:28	Community structure of small world networks (1)
41:46	Community structure of small world networks (2)
42:20	Community structure of small world networks (1)
43:04	Community structure of small world networks (2)
44:00	“Quality” criteria MQ
45:48	MQ / Nice properties (1)
47:30	MQ / Nice properties (2)
47:36	Challenge: find the best possible clustering (according to MQ)
48:07	MQ / Extension (1)
48:46	MQ / Extension (2)
49:08	Conclusion – Future work
49:21	MQ / Extension to graph hierarchies
50:03	Conclusion – Future work
50:41	Conclusion (1)
51:23	Conclusion (2)
51:37	Conclusion (3)

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