论文标题
重新考虑集群结构在复杂网络中的积极作用以进行链接预测任务
Rethinking the positive role of cluster structure in complex networks for link prediction tasks
论文作者
论文摘要
聚类是网络分析中的一个基本问题，该问题可以找到紧密连接的节点组并将其与图中的其他节点分开，而链接预测是预测网络中的两个节点是否可能具有链接。两者的定义自然决定聚类必须在获得准确的链接预测任务中起积极作用。然而，研究人员长期以来一直忽略或使用不适当的方法来破坏这种积极的关系。在本文中，我们构建了一个简单但有效的聚类驱动的链接预测框架（ClusterLP），其目的是直接利用群集结构，以在未方向的图和有向图中尽可能准确地获得节点之间的连接。具体而言，我们建议在没有方向图中具有相似表示向量和群集趋势的节点之间建立链接更容易，而有向图中的节点可以更容易地指向类似于其表示向量的节点，并且在其自身的群集中具有更大的影响。我们分别为无向图和有向图定制了clusterLP的实现，并在链接预测任务上使用多个现实世界网络的实验结果表明，我们的模型与现有基线模型具有很高的竞争力。我们使用的clusterLP和基线的代码实现可在https://github.com/zinux1998/clusterlp上获得。
Clustering is a fundamental problem in network analysis that finds closely connected groups of nodes and separates them from other nodes in the graph, while link prediction is to predict whether two nodes in a network are likely to have a link. The definition of both naturally determines that clustering must play a positive role in obtaining accurate link prediction tasks. Yet researchers have long ignored or used inappropriate ways to undermine this positive relationship. In this article, We construct a simple but efficient clustering-driven link prediction framework(ClusterLP), with the goal of directly exploiting the cluster structures to obtain connections between nodes as accurately as possible in both undirected graphs and directed graphs. Specifically, we propose that it is easier to establish links between nodes with similar representation vectors and cluster tendencies in undirected graphs, while nodes in a directed graphs can more easily point to nodes similar to their representation vectors and have greater influence in their own cluster. We customized the implementation of ClusterLP for undirected and directed graphs, respectively, and the experimental results using multiple real-world networks on the link prediction task showed that our models is highly competitive with existing baseline models. The code implementation of ClusterLP and baselines we use are available at https://github.com/ZINUX1998/ClusterLP.
