NATO Advanced Study Institute on Mining Massive Data Sets for Security

Diffusion and Cascading Behaviour in Networks

author: Jure Leskovec, CMU

Description

Diffusion is a process by which information, viruses, ideas and new behavior spread over the network. For example, adoption of a new technology begins on a small scale with a few “early adopters”, then more and more people adopt it as they observe friends and neighbors using it. Eventually the adoption of the technology may spread through the social network as an epidemic “infecting” most of the network. As it spreads over the network it creates a cascade. Cascades have been studied for many years by sociologists concerned with the diffusion of innovation; more recently, researchers have investigated cascades for selecting trendsetters for viral marketing, finding inoculation targets in epidemiology, and explaining trends in blogspace.

Categories

Top: Computer Science: Network Analysis

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*Slides*
0:00	Diffusion and Cascading Behavior in Networks
0:08	Networks –Social and Technological
1:41	Examples of Networks
2:11	Networks of the Real‐world (1)
2:40	Networks of the Real‐world (2)
3:07	Mining Social Network Data
4:07	Networks as Phenomena
4:27	Models and Laws of Networks
5:11	Networks: Rich Data
5:51	Networks: A Matter of Scale
6:26	Networks: Scale Matters
7:26	Structure vs. Process
8:02	Diffusion in Social Networks
8:23	Overview
9:06	Diffusion in Social Networks
10:05	Empirical Studies of Diffusion (1)
11:53	Empirical Studies of Diffusion (2)
13:20	Diffusion Curves (1)
15:44	Diffusion Curves (2)
17:41	Part 1: Mathematical Models
19:18	A) Models of Virus Propagation
20:08	The Model
20:51	Question: Epidemic Threshold τ
21:22	Epidemic Threshold τ
21:49	Epidemic Threshold
22:35	Experiments (AS graph)
23:35	B) Models of Diffusion in Networks
24:03	Threshold Model [Granovetter ‘78]
25:24	Independent Contagion Model
26:35	General Contagion Model
27:59	Most Influential Subset of Nodes
29:22	An Approximation Result (1)
30:34	An Approximation Result (2)
31:58	Analysis: Independent Contagion
33:30	Analysis: Alternative View (1)
34:04	Analysis: Alternative View (2)
35:02	Part 2: Empirical Analysis
36:28	Diffusion in Blogs
37:53	Diffusion in Viral Marketing
39:00	Diffusion of Community Membership
39:48	How do diffusion curves look like? (1)
40:49	How do diffusion curves look like? (2)
41:21	How do diffusion curves look like? (3)
41:56	What are we really measuring?
42:49	More subtle features: Communities
43:33	Connectedness of Friends (1)
44:43	Connectedness of Friends (2)
44:57	Connectedness of Friends (1)
45:06	Connectedness of Friends (2)
45:30	A Puzzle
46:50	Connectedness of Friends (1)
47:10	A Puzzle
47:57	More subtle features: Viral marketing (1)
48:34	More subtle features: Viral marketing (2)
49:21	More subtle features: Viral marketing (3)
51:12	Cascading of Recommendations
51:48	Viral Marketing: More subtleties
53:15	Predicting recommendation success
54:53	Viral Marketing: Why?
56:40	How do people get recommendations?
57:18	Is there still room for Viral Marketing?
57:36	Viral Marketing: Notspreading virally
58:48	Viral Marketing: Consequences
60:11	How Do Cascades Look Like?
61:00	Cascades as Graphs
61:22	Viral Marketing: Frequent Cascades
61:23	Cascades as Graphs
61:26	Viral Marketing: Frequent Cascades
62:55	Viral Marketing Cascades
63:19	Viral Marketing: Frequent Cascades
63:35	Viral Marketing Cascades
64:23	Information Cascades in Blogs
66:02	Cascades: Shape and Frequency
66:29	Cascade Size: Viral Marketing –Books
67:23	Cascade Size: Viral Marketing –DVDs
68:19	Cascade Size: Blogs
68:36	Cascade Size: Consequences

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