Link prediction (LP) is a crucial issue in the online social network (OSN) evolution analysis. Since OSNs are growing in size on a daily basis, a growing need for scalable LP algorithms is being felt. OSNs are innately evolutionary, such that the characteristics, behavior, and activities of their components (including nodes and links) change over time. In analyzing social networks which are based on the time evolution model, LP helps us realize the logic of social network growth. Deriving time patterns of evolutionary changes according to the communities and neighbors of nodes in a network can be aptly used for LP. This article introduces a new algorithm based on irregular cellular learning automata (ICLAs) for LP in the near future in OSNs. The algorithm we propose here models the network as an ICLA. The ICLA weighs the real links in the network according to entities' participation in forming communities over consecutive time periods. This method lies in the premise that social networks include communities. Based on the communities formed over successive time periods, the presented method calculates the probability of link formation between every pair of nodes which are unconnected at the present time, estimating the chances of their connection in the near future. Experiments performed on real social networks show that the proposed algorithm produces good results in predicting link formation in OSNs.

链接预测（LP）是在线社交网络（OSN）演变分析中的关键问题。由于OSN的大小每天都在增长，因此人们感到对可伸缩LP算法的需求不断增长。OSN是天生的进化，因此其组件（包括节点和链接）的特性，行为和活动会随时间而变化。在分析基于时间演化模型的社交网络时，LP帮助我们实现了社交网络增长的逻辑。根据网络中节点的社区和邻居得出演化变化的时间模式可以适当地用于LP。本文介绍了一种基于不规则细胞学习自动机（ICLA）的OSN中新的LP算法。我们在此提出的算法将网络建模为ICLA。ICLA根据实体在连续时间段内参与形成社区的参与权衡网络中的真实链接。这种方法的前提是社交网络包括社区。基于在连续时间段上形成的社区，提出的方法计算了当前未连接的每对节点之间的链路形成概率，从而估计了在不久的将来它们建立连接的机会。在真实社交网络上进行的实验表明，该算法在预测OSN中的链接形成方面产生了良好的效果。提出的方法计算了当前未连接的每对节点之间建立链接的概率，从而估计了在不久的将来它们之间建立连接的机会。在真实社交网络上进行的实验表明，该算法在预测OSN中的链接形成方面产生了良好的效果。提出的方法计算了当前未连接的每对节点之间建立链接的概率，从而估计了在不久的将来它们之间建立连接的机会。在真实社交网络上进行的实验表明，该算法在预测OSN中的链接形成方面产生了良好的效果。