网络表示学习算法的分析与验证

Analysis and Validation of Network Representation Algorithms

网络表示学习算法是社交网络分析领域的一个热点问题。该文旨在研究现有的各种网络表示学习算法,并分析各类算法在不同结构的网络数据中的性能,对3大类别、共10种网络表示学习算法在8个网络上进行了网络节点的多标签分类以验证算法的性能,以此来全面评价各类算法的效果、效率和应用范围。实验结果表明,DeepWalk这种流行的深度学习算法在各种类型的网络中有着稳定而较好的效果。而基于矩阵分解算法的应用,则受限于其较高的空间复杂度。

The network representation learning algorithm is a popular issue in social network analysis,and this paper is to verify the existing network representation learning algorithms by network data with different structures.To evaluate the effect,the efficiency and the application limits of various algorithms,we choose the multi-label classification task of network nodes to compare ten algorithms of three categories on eight data sets.The experimental results show that Deep Learning algorithms like DeepWalk have stable and good performance on various types of networks,and the application of algorithms based on matrix factorization are limited by their high space complexity.