Maximizing influence through online social networks

Aaron R. Sun, Daniel D. Zeng

Research output: Contribution to conferencePaperpeer-review

5 Scopus citations


Studying information diffusion through social networks has become an active research topic with important implications in viral marketing applications. Traditional information diffusion models such as the Independent Cascade Model (ICM) provide a general analysis framework for such studies. However, ICM makes several overly restrictive assumptions. For instance, it allows no more than one event or opinion to propagate through the network. In this paper, we use the real-world online social network structure extracted from a popular social bookmarking Website, to illustrate ICM's limitations. We then propose model improvements and extensions and compare them with the standard ICM-based approach in solving one fundamental algorithmic problem related to viral marketing: How to select a set of network nodes/individuals to facilitate information diffusion and maximize influence? The preliminary results show that our approach results in node-selection heuristics outperforming well-studied notions of degree centrality and distance centrality based on the ICM.

Original languageEnglish (US)
Number of pages6
StatePublished - 2008
Event2008 Workshop on Information Technologies and Systems, WITS 2008 - Paris, France
Duration: Dec 13 2008Dec 14 2008


Other2008 Workshop on Information Technologies and Systems, WITS 2008

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering


Dive into the research topics of 'Maximizing influence through online social networks'. Together they form a unique fingerprint.

Cite this