Information spread is an intriguing topic to study in network science, which investigates how information, influence, or contagion propagate through networks. Graph burning is a simplified deterministic model for how information spreads within networks. The complicated NP-complete nature of the problem makes it computationally difficult to solve using exact algorithms. Accordingly, a number of heuristics and approximation algorithms have been proposed in the literature for the graph burning problem. In this paper, we propose an efficient genetic algorithm called Centrality BAsed Genetic-algorithm (CBAG) for solving the graph burning problem. Considering the unique characteristics of the graph burning problem, we introduce novel genetic operators, chromosome representation, and evaluation method. In the proposed algorithm, the well-known betweenness centrality is used as the backbone of our chromosome initialization procedure. The proposed algorithm is implemented and compared with previous heuristics and approximation algorithms on 15 benchmark graphs of different sizes. Based on the results, it can be seen that the proposed algorithm achieves better performance in comparison to the previous state-of-the-art heuristics. The complete source code is available online and can be used to find optimal or near-optimal solutions for the graph burning problem.
翻译:信息传播是研究网络科学的一个令人感兴趣的主题,它调查信息、影响或传染如何通过网络传播。 图表燃烧是信息在网络内部传播的简化确定模式。 问题的复杂NP不完整性质使得很难用精确的算法进行计算。 因此, 文献中为图形燃烧问题提出了数种脂质和近似算法。 在本文中, 我们建议一种有效的遗传算法, 叫做 Centrality Based General-algorithm (CBAG), 用于解决图形燃烧问题。 考虑到图形燃烧问题的独特性, 我们引入了新型基因操作者、 染色体表征、 和评价方法。 在拟议的算法中, 众所周知的中间中心点被用作我们染色体初始化程序的支柱。 拟议的算法已经付诸实施, 与之前的15种不同大小的基准图形的脂质和近似算法相比较。 根据结果, 可以看到, 拟议的算法比以往的状态更佳的性, 可以在网上找到最优的源码, 。