Information Diffusion and Social Influence > Source Codes

• Viral Advertising in OSN

  The VirAds algorithm overcomes severe scalability problem in the natural greedy algorithm by favoring the vertex that can activate the most number of edges as well as considering the number of active neighbors around each vertex at the same time.
  
  Specifically, at early steps, the algorithm behaves similarly to the degree-based heuristics that favors vertices with high degree. However, when a certain number of vertices is selected,VirAds will make the selection based on the information within d-hop neighbors around the considered vertices rather than only one-hop neighbors as in the degree-based heuristic. Then in each iteration, we select the node with high-effectiveness, which is defined by the total number of activated edges and activated nodes. After this, it will update the status of remaining vertices by using CELF, which further help it speed up.
  
  Refer: T. N. Dinh, H. Zhang, D. T. Nguyen, and M. T. Thai, Cost-effective Viral Marketing for Time-critical Campaigns in Large-scale Social Networks, IEEE/ACM Transactions on Networking (ToN), DOI: 10.1109/TNET.2013.2290714, 2013
  
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• Coupling Networks

  Algorithms for normalization of networks with overlapping users, generation of coupling networks, and generation of synthesis networks are included here. Please refer the readme file and comments in the source codes for more details.
  
  Refer: D. T. Nguyen, H. Zhang, S. Das, M. T. Thai, and T. N. Dinh, Least Cost Influence in Multiplex Social Networks: Model Representation and Analysis, in Proceedings of the IEEE Int Conference on Data Mining (ICDM), 2013.
  
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