In recent years, social networks have surged in popularity as one of the main applications of the Internet. One key aspect of social network research is exploring important unobserved network information which is not explicitly represented. This study first introduces a new path identification problem to identify the existences of unobserved paths between nodes. Given a partial social network structure where the indications of observed nodes about unobserved paths are assumed to exist, we propose a multiple-level classification based path identification method (MCPIM) for graph-based social networks. MCPIM presents the new multiple-level similarity to efficiently represent the structural positions of subgraph placeholders. Subsequently, a quantum mechanism based genetic classification algorithm (QGCA) is constructed to efficiently divide subgraph placeholders into different clusters. The nodes whose subgraph placeholders are in the same cluster owning large structural similarities are inferred to have unobserved paths. Results obtained by comparing with state-of-the-art methods via extensive experiments using disparate real-world social networks show that MCPIM can well identify the existences of unobserved paths between nodes in graph-based social networks.

近年来，社交网络作为Internet的主要应用之一而迅速普及。社交网络研究的一个关键方面是探索未明确表示的重要的未观察到的网络信息。这项研究首先介绍了一个新的路径识别问题，以识别节点之间未观察到的路径的存在。给定一个假设存在观察节点关于未观察路径的指示的部分社交网络结构，我们为基于图的社交网络提出了一种基于多级分类的路径识别方法（MCPIM）。消费者价格指数提出了新的多级相似性，以有效地表示子图占位符的结构位置。随后，构建了基于量子机制的遗传分类算法（QGCA），以有效地将子图占位符划分为不同的簇。推断其子图占位符位于具有相同结构相似性的同一群集中的节点具有不可观察的路径。通过使用不同的真实世界社交网络进行的广泛实验与最新技术进行比较所获得的结果表明，MCPIM可以很好地识别基于图的社交网络中节点之间未观察到的路径的存在。