English | FLV 1024x768 | MP3 48 Kbps | 85 MB

Genre: eLearning

author:Amit Goyal, Department of Computer Science, University of British Columbia.

Conferences » The ACM Conference Series - ACM International Conference on Web Search and Data Mining » Third ACM International Conference on Web Search and Data Mining - WSDM 2010.

Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users.

However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.

0:00 Learning Influence Probabilities in Social Networks

0:24 Word of Mouth and Viral Marketing

1:02 Viral Marketing

1:24 Viral Marketing as an Optimization Problem

2:11 Some Questions

3:03 Input Data

3:34 Our contributions - 1

4:15 Our contributions - 2

4:33 Overview

4:46 Background

4:52 General Threshold (Propagation) Model

5:59 General Threshold Model - Example

6:38 Our Framework

6:41 Solution Framework

7:53 Influence Models

8:32 Static Models

8:55 Time Conscious Models

9:25 Continuous Time Models

9:48 Evaluation Strategy - 1

10:29 Evaluation Strategy - 2

11:20 Algorithms

11:53 Experimental Evaluation

11:56 Dataset

12:20 Comparison of Static, CT and DT models

12:48 Runtime

13:02 Predicting Time –Distribution of Error

13:37 Predicting Time –Coverage vs Error

14:03 User Influenceability

14:53 Action Influenceability

15:30 Related Work

16:11 Other applications of Influence Propagations

16:46 Conclusions - 1

17:12 Conclusions - 2

17:36 Future Work

18:11 Thanks!!

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