The evolution of online social networks is highly dependent on the recommended links. Most of the existing works focus on predicting intra-community links efficiently. However, it is equally important to predict inter-community links with high accuracy for diversifying a network. In this work, we propose a link prediction method, called HM-EIICT, that considers both the similarity of nodes and their community information to predict both kinds of links, intra-community links as well as inter-community links, with higher accuracy. The proposed framework is built on the concept that the connection likelihood between two given nodes differs for inter-community and intra-community node-pairs. The performance of the proposed methods is evaluated using link prediction accuracy and network modularity reduction. The results are studied on real-world networks and show the effectiveness of the proposed method as compared to the baselines. The experiments suggest that the inter-community links can be predicted with a higher accuracy using community information extracted from the network topology, and the proposed framework outperforms several measures especially proposed for community-based link prediction. The paper is concluded with open research directions.

在线社交网络的发展高度依赖于推荐链接。大多数现有工作都集中在有效预测社区内链接上。然而，为了使网络多样化，以高精度预测社区间链接同样重要。在这项工作中，我们提出了一种称为 HM-EIICT 的链接预测方法，该方法同时考虑节点的相似性及其社区信息，以更高的精度预测两种链接、社区内链接以及社区间链接。所提出的框架建立在两个给定节点之间的连接可能性对于社区间和社区内节点对不同的概念上。使用链路预测精度和网络模块化减少来评估所提出方法的性能。结果在现实世界的网络上进行了研究，并显示了与基线相比所提出方法的有效性。实验表明，使用从网络拓扑中提取的社区信息可以更准确地预测社区间链接，并且所提出的框架优于特别为基于社区的链接预测提出的几种措施。论文以开放的研究方向结束。