8th Extended Semantic Web Conference (ESWC) 2011

Rhythms of Information Flow through Networks

author: Jure Leskovec, Computer Science Department, Stanford University
introducer: Elena Simperl, School of Electronics and Computer Science, University of Southampton
published: July 7, 2011, recorded: June 2011, views: 844

Slides

Slides
0:00	Rhythms of Information Flow through Networks
3:58	Information and Network
5:16	Online (Social) Media
6:34	Social Media: The New Picture
7:55	Information: Heavily dynamic
8:42	Plan for the talk
9:44	Challenges and Opportunities
11:29	Extracting Units of Information
14:02	Information Cascades in Blogs
15:45	Meme - tracking
18:10	Finding Mutational Variants
20:48	Cluster Volume over Time - 1
21:09	Cluster Volume over Time - 2
23:26	Interaction of News and Blogs
25:57	Patterns of Information Attention
27:17	Discovering Attention Patterns
28:09	Clustering Temporal Signatures
30:10	Patterns of Attention
32:22	Analysis of Attention Patterns - 1
33:32	Analysis of Attention Patterns - 2
34:05	Predicting Information Attention - 1
35:27	Predicting Information Attention - 2
36:39	The Linear Influence Model - 1
38:13	The Linear Influence Model - 2
40:01	Estimating Influence Functions
41:38	The model: Performance
43:35	Analysis of Influence Functions
44:30	Analysis of Influence
45:39	Inferring the Diffusion Network
46:01	Inferring the Diffusion Network
46:47	Examples and Applications
47:51	Inferring Diffusion Networks
48:44	The optimization problem
50:41	Information Diffusion Model
53:55	Probability of a Propagation Tree - 1
54:59	Probability of a Propagation Tree - 2
55:14	NetInf: The Algorithm - 1
56:24	NetInf: The Algorithm - 2
56:53	Experiments: Synthetic data
57:57	Experiments
58:38	Diffusion Network
58:51	Diffusion Network (zoom - in)
60:12	Detecting information outbreaks
61:39	Problem: Solving stories
62:24	Blogs: Information epidemics
63:12	Experimental results
63:59	Conclusions and Connections - 1
64:35	Conclusions and Connections - 2
65:20	Further Qs
66:09	Thanks

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Description

The information we experience online comes to us continuously over time, assembled from many small pieces, and conveyed through our social networks. This merging of information, network structure, and flow over time requires new ways of reasoning about the large-scale behavior of information networks. I will discuss a set of approaches for tracking information as it travels and mutates in online networks. We show how to capture and model temporal patterns in the news over a daily time-scale -- in particular, the succession of story lines that evolve and compete for attention. I will also discuss models to quantify the influence of individual media sites on the popularity of news stories and algorithms for inferring latent information diffusion networks.