Difference between revisions of "Goyal et al., 2010, Learning Influence Probabilities In Social Networks"
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== Online version == | == Online version == | ||
| − | An online version of this paper is available at the [ | + | An online version of this paper is available at the [[http://portal.acm.org/ft_gateway.cfm?id=1718518&type=pdf&CFID=7653019&CFTOKEN=17876787 ACM digital library]]. |
== Summary == | == Summary == | ||
| − | This [[Category::paper]] introduces | + | Influence propagation in social networks has interesting application, especially for viral marketing. Most past studies assume as input a graph with nodes for each person, and weighted edges between the nodes if there is influence between the two persons. However, less attention has been put on how to build this graph using social media data. This [[Category::paper]] introduces a model of influence built using social graph data on one hand, and a log of action (e.g., joining a community) on the other hand. |
== Key Contributions == | == Key Contributions == | ||
| − | The biggest contribution claimed by the authors in this paper | + | The biggest contribution claimed by the authors in this paper is the evidence of influence in social networks. |
== Experiments and Evaluation == | == Experiments and Evaluation == | ||
Revision as of 14:34, 2 February 2011
Online version
An online version of this paper is available at the [ACM digital library].
Summary
Influence propagation in social networks has interesting application, especially for viral marketing. Most past studies assume as input a graph with nodes for each person, and weighted edges between the nodes if there is influence between the two persons. However, less attention has been put on how to build this graph using social media data. This paper introduces a model of influence built using social graph data on one hand, and a log of action (e.g., joining a community) on the other hand.
Key Contributions
The biggest contribution claimed by the authors in this paper is the evidence of influence in social networks.
