为利用图注意力机制解决复杂网络中的关键节点识别问题,综合考虑节点传播力和结构影响力,在生成网络上构建训练标签,通过图注意力网络模型学习节点重要性。实验证明该算法在影响最大化和免疫隔离两个关键任务中表现出色。
This study aims to address the problem of vital nodes identification in complex networks using graph attention mechanism. This paper integrates both node′s virus transmissibility and structural impact, constructing training labels on the generated network to learn node importance through a graph attention network model. Experimental results demonstrate the excellence of this algorithm in two critical tasks: influence maximization and immune isolation.
[1] BARABÁSI A L. Network science[J]. Philosophical Transactions of the Royal Society a: Mathematical, Physical and Engineering Sciences, 2013, 371(1987): 20120375.
[2] ZHONG S, ZHANG H, DENG Y. Identification of influential nodes in complex networks: a local degree dimension approach[J]. Information Sciences, 2022, 610: 9941009.
[3] LUO J, WU J, YANG W. A relationship matrix resolving model for identifying vital nodes based on community in opportunistic social networks[J]. Transactions on Emerging Telecommunications Technologies, 2022, 33(1): e4389.
[4] HE Q, WANG X, MAO F, et al. CAOM: A community-based approach to tackle opinion maximization for social networks[J]. Information Sciences, 2020, 513: 252269.
[5] SUN P G, QUAN Y N, MIAO Q G, et al. Identifying influential genes in protein-protein interaction networks[J]. Information Sciences, 2018, 454: 229241.
[6] WANDELT S, SHI X, SUN X. Estimation and improvement of transportation network robustness by exploiting communities[J]. Reliability Engineering & System Safety, 2021, 206: 107307.
[7] BONACICH P. Factoring and weighting approaches to status scores and clique identification[J]. Journal of Mathematical Sociology, 1972, 2(1): 113120.
[8] PAGE L, BRIN S, MOTWANI R, et al. The PageRank citation ranking: Bringing order to the web[R]. Stanford InfoLab, 1999.
[9] ZHU D, WANG H, WANG R, et al. Identification of key nodes in a power grid based on modified PageRank algorithm[J]. Energies, 2022, 15(3): 797.
[10] SABIDUSSI G. The centrality index of a graph[J]. Psychometrika, 1966, 31(4): 581603.
[11] NEWMAN M E J. A measure of betweenness centrality based on random walks[J]. Social networks, 2005, 27(1): 3954.
[12] ZHANG J, WANG B. Dismantling complex networks by a neural model trained from tiny networks[C]//Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Atlanta GA, USA, 2022: 25592568.[DB/OL].
[13] VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[DB/OL].[20230624]. https://arxiv.org/abs/1710.10903.
[14] GRASSIA M, DE DOMENICO M, MANGIONI G. Machine learning dismantling and early-warning signals of disintegration in complex systems[J]. Nature Communications, 2021, 12(1): 110.
[15] HAMILTON W, YING Z, LESKOVEC J. Inductive representation learning on large graphs[J]. Advances in Neural Information Processing Systems, 2017, 30.
[16] HETHCOTE H W. Three basic epidemiological models[J]. Applied Mathematical Ecology, 1989: 119144.
[17] CURADO M, TORTOSA L, VICENT J F. A novel measure to identify influential nodes: return random walk gravity centrality[J]. Information Sciences, 2023, 628: 177195.
[18] WANG M, LI W, GUO Y, et al. Identifying influential spreaders in complex networks based on improved k-shell method[J]. Physica A: Statistical Mechanics and Its Applications, 2020, 554: 124229.
[19] Ullah A, Wang B, Sheng J F, et al. Identification of nodes influence based on global structure model in complex networks[J]. Scientific Reports, 2021, 11(1): 111.
[20] LEI M, CHEONG K H. Node influence ranking in complex networks: a local structure entropy approach[J]. Chaos, Solitons & Fractals, 2022, 160: 112136.
[21] MAJI G, DUTTA A, MALTA M C, et al. Identifying and ranking super spreaders in real world complex networks without influence overlap[J]. Expert Systems with Applications, 2021, 179: 115061.
[22] PASTOR R, VESPIGNANI A. Epidemic spreading in scale-free networks[J]. Physical Review Letters, 2001, 86(14): 3200.
