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Identifying Multiple Influential Nodes for Complex Networks Based on Multi-agent Deep Reinforcement Learning
conference contribution
posted on 2023-02-23, 02:52 authored by S Kong, L He, G Zhang, L Tao, Zili ZhangZili ZhangThe identification of multiple influential nodes that influence the structure or function of a complex network has attracted much attention in recent years. Distinguished from individual significant nodes, the problem of overlapping spheres of influence among influential nodes becomes a key factor that hinders their identification. Most approaches artificially specify the spacing distance between selected nodes through graph coloring and greedy selection. However, these approaches either fail to find the best combination accurately or have high complexity. Therefore, we propose a novel identification framework, namely multi-agent identification framework (MAIF), which selects multiple influential nodes in a distributed and simultaneous manner. Based on multi-agent deep reinforcement learning, the framework introduce several optimization models and extend to complex networks to solve distributed problems. With sufficient training, MAIF can be applied to real-world problems quickly and effectively, and perform well in large-scale networks. Based on SIR model-based simulations, the effectiveness of MAIF is evaluated and compared with three baseline methods. The experimental results show that MAIF outperforms the baselines on all four real-world networks. This implies using multiple agents to find multiple influential nodes in a distributed manner is an efficient and accurate new way to differentiate from the greedy methods.
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Volume
13631 LNCSPagination
120-133Publisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783031208676Title of proceedings
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Publisher
Springer Nature SwitzerlandSeries
Lecture Notes in Computer ScienceUsage metrics
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