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Title: | Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning |
Authors: | Ma, L Shao, Z Li, X Lin, Q Li, J Leung, VCM Nandi, AK |
Keywords: | complex networks,;influence maximization;deep reinforcement learning;evolutionary algorithm;optimization |
Issue Date: | 13-Jan-2022 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Citation: | Ma, L. et al. (2022) 'Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning', IEEE Transactions on Emerging Topics in Computational Intelligence, 7 (4), pp. 995 - 1009. doi: 10.1109/TETCI.2021.3136643. |
Abstract: | Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes. |
URI: | https://bura.brunel.ac.uk/handle/2438/23840 |
DOI: | https://doi.org/10.1109/TETCI.2021.3136643 |
Other Identifiers: | ORCiD: Lijia Ma https://orcid.org/0000-0002-1201-8051 ORCiD: Qiuzhen Lin https://orcid.org/0000-0003-2415-0401 ORCiD: Jianqiang Li https://orcid.org/0000-0002-2208-962X ORCiD: Victor C. M. Leung https://orcid.org/0000-0003-3529-2640 ORCiD: Asoke K. Nandi https://orcid.org/0000-0001-6248-2875 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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