Leveraging Cross-Network Information for Graph Sparsification in Influence Maximization

ABSTRACT

When tackling large-scale influence maximization (IM) problem, one effective strategy is to employ graph sparsification as a pre-processing step, by removing a fraction of edges to make original networks become more concise and tractable for the task. In this work, a Cross-Network Graph Sparsification (CNGS) model is proposed to leverage the influence backbone knowledge pre-detected in a source network to predict and remove the edges least likely to contribute to the influence propagation in the target networks. Experimental results demonstrate that conducting graph sparsification by the proposed CNGS model can obtain a good trade-off between efficiency and effectiveness of IM, i.e., existing IM greedy algorithms can run more efficiently, while the loss of influence spread can be made as small as possible in the sparse target networks.

References

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Multi-task learning

        • Transfer learning

      • Supervised learning

        • Ranking

        • Supervised learning by regression

    • Learning settings

      • Semi-supervised learning settings

• Human-centered computing

  • Collaborative and social computing

    • Collaborative and social computing theory, concepts and paradigms

      • Social networks

• Information systems

  • Information retrieval

    • Retrieval models and ranking

      • Learning to rank

      • Rank aggregation

      • Top-k retrieval in databases

  • World Wide Web

    • Web applications

      • Social networks