Social influence analysis in large-scale networks

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by Tang et al. (tang2009), SIGKDD

Tang et al. (2009) propose Topical Affinity Propagation (TAP) for determining the topic-level social influence of nodes in large networks. They introduce a distributed learning algorithm based on map reduce to compute influence graphs using TAP for identifying

  • the most representative nodes for a given topic
  • the influence of neighbor nodes on a particular node
  • ways to quickly connect to a particular node through strong social ties
According to Tang et al. the main challenges for computing social influence graphs are:

  • multi-aspect: nodes can have different influence on each other depending on the topic
  • scalability
  • link-specific importance (weight) between nodes rather than fixed node importance values.