ABSTRACT

One of the main areas of research in Social Network Analysis (SNA) is Link Prediction (LP). The LP problem is useful in understanding the evolution mechanism of social networks, as well as in different applications such as recommendation systems, bioinformatics and marketing. In LP algorithms, prior network information is used to predict future connections in social networks. In this paper, we introduce a multi-wave cellular learning automaton (MWCLA) and use it to solve the LP problem in social networks. This model is a new CLA with a connected structure and a module of LAs in each cell where the neighbours of the cell module are its successors. The MWCLA method uses multiple waves at the same time in the network in order to improve convergence speed as well as accuracy. For predicting links in the social network, multiple waves can be used to consider different aspects of the network. Here we show that the model converges upon a stable and compatible configuration. Compared to some state-of-the-art approaches, MWCLA produces significantly better results when applied to the LP problem.

摘要

社交网络分析 (SNA) 的主要研究领域之一是链接预测 (LP)。LP 问题有助于理解社交网络的演化机制，以及推荐系统、生物信息学和营销等不同应用。在 LP 算法中，先验网络信息用于预测社交网络中的未来连接。在本文中，我们介绍了一种多波元胞学习自动机 (MWCLA)，并用它来解决社交网络中的 LP 问题。该模型是一个新的 CLA，具有连接结构和每个单元中的 LA 模块，其中单元模块的邻居是其后继者。MWCLA 方法在网络中同时使用多个波，以提高收敛速度和准确性。为了预测社交网络中的链接，可以使用多个波来考虑网络的不同方面。在这里，我们表明该模型收敛于稳定且兼容的配置。与一些最先进的方法相比，MWCLA 在应用于 LP 问题时产生了明显更好的结果。