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A Learning Approach for Topic-Aware Influence Maximization

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11641)

Abstract

Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics. This problem has been proved to be NP-hard and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we encode the feature of each node by a vector and introduce a deep learning model, called deep-influence-evaluation-model (DIEM), to evaluate users’ influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.

Keywords

Social network Influence maximization Graph embedding Reinforcement learning 

Notes

Acknowledgements

This work is supported by the National Key Research and Development Program of China (Project Number: 2018YFB1003402), key projects of the national natural science foundation of China (Project Number: U1811263) and the Fundamental Research Funds for the Central Universities (Project Number: 2042017kf1017).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.HuaweiShenzhenChina

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