论文标题
TPM：过渡概率矩阵 - 基于图结构特征的嵌入
TPM: Transition Probability Matrix -- Graph Structural Feature based Embedding
论文作者
论文摘要
在这项工作中，提出了过渡概率矩阵（TPM）作为提取图中节点特征的新方法。提出的方法使用随机步行来捕获节点关闭社区的连通性结构。从随机步行中获得的信息转换为匿名步道，以提取节点的拓扑特征。在节点的嵌入过程中，使用了匿名步道，因为它们比随机步行更好地捕获了连接性的拓扑相似性。因此，所获得的嵌入向量具有有关潜在连接结构的更丰富信息。该方法应用于节点分类和链接预测任务。所提出的算法的性能优于最近文献中最新的算法。此外，有关类似网络的连接结构的提取信息用于链接全新图表的预测和节点分类任务。
In this work, Transition Probability Matrix (TPM) is proposed as a new method for extracting the features of nodes in the graph. The proposed method uses random walks to capture the connectivity structure of a node's close neighborhood. The information obtained from random walks is converted to anonymous walks to extract the topological features of nodes. In the embedding process of nodes, anonymous walks are used since they capture the topological similarities of connectivities better than random walks. Therefore the obtained embedding vectors have richer information about the underlying connectivity structure. The method is applied to node classification and link prediction tasks. The performance of the proposed algorithm is superior to the state-of-the-art algorithms in the recent literature. Moreover, the extracted information about the connectivity structure of similar networks is used to link prediction and node classification tasks for a completely new graph.
