论文标题
通过图表示学习识别复杂网络中的关键节点
Identifying critical nodes in complex networks by graph representation learning
论文作者
论文摘要
由于其广泛的应用，关键节点识别已成为网络科学的微观研究主题。影响最大化是关键节点开采的主要问题之一，通常以启发式方式处理。在本文中，提出了一个深图学习框架IMGNN，并设计了相应的培训样本方案。该框架将网络中的节点的中心作为输入和最佳初始散布器中的节点作为输出的概率。通过对大量小型合成网络进行培训，IMGNN比基于人类的启发式方法更有效地最大程度地减少了固定感染量表的初始散布器的大小。一个合成和五个真实网络的实验结果表明，与传统的非著作节点排名算法相比，IMGNN在固定最终感染量表时在不同感染概率下的初始散布器比例最小。重新排序的IMGNN版本优于所有最新的关键节点挖掘算法。
Because of its wide application, critical nodes identification has become an important research topic at the micro level of network science. Influence maximization is one of the main problems in critical nodes mining and is usually handled with heuristics. In this paper, a deep graph learning framework IMGNN is proposed and the corresponding training sample generation scheme is designed. The framework takes centralities of nodes in a network as input and the probability that nodes in the optimal initial spreaders as output. By training on a large number of small synthetic networks, IMGNN is more efficient than human-based heuristics in minimizing the size of initial spreaders under the fixed infection scale. The experimental results on one synthetic and five real networks show that, compared with traditional non-iterative node ranking algorithms, IMGNN has the smallest proportion of initial spreaders under different infection probabilities when the final infection scale is fixed. And the reordered version of IMGNN outperforms all the latest critical nodes mining algorithms.
