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Real-time topic-aware influence maximization using preprocessing.

Wei Chen1, Tian Lin2, Cheng Yang3

  • 1Microsoft Research, No. 5 Danling Street, 100080 Beijing, China.

Computational Social Networks
|January 23, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces efficient preprocessing algorithms for topic-aware influence maximization in social networks. One algorithm achieves microsecond response times and competitive influence spread with minimal preprocessing.

Keywords:
Influence maximizationInformation diffusionTopic-aware influence modeling

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Area of Science:

  • Social Network Analysis
  • Information Diffusion Modeling

Background:

  • Influence maximization identifies key nodes to maximize information spread in networks.
  • Topic-aware diffusion models account for the context-dependent nature of user influence.
  • Information propagation often involves complex mixtures of topics.

Purpose of the Study:

  • To develop efficient preprocessing methods for topic-aware influence maximization.
  • To avoid recalculating influence spread for every new topic mixture.
  • To optimize the identification of influential seed nodes in complex networks.

Main Methods:

  • Exploration of two novel preprocessing algorithms for topic-aware influence maximization.
  • Theoretical analysis and justification of the proposed algorithms.
  • Empirical evaluation using existing datasets.

Main Results:

  • One preprocessing algorithm demonstrated superior performance.
  • Achieved microsecond online response times for influence maximization queries.
  • Maintained competitive influence spread compared to baseline methods.

Conclusions:

  • The proposed preprocessing approach significantly enhances efficiency for topic-aware influence maximization.
  • The standout algorithm offers a practical solution for real-time influence analysis.
  • Effective preprocessing balances computational effort with performance gains.