关键词: 故障短文本, 图节点嵌入, 局部密度, 图节点聚类

Abstract: To effectively mine the cross-text vocabulary association in fault short text,the global feature representation of fault entity nodes was constructed,and the fault entity node clustering label was obtained.An improved graph node embedding and clustering method for fault short text was proposed.In this method,the calculation method of edge weight was innovated in the process of graph construction to distinguish the association between words with different distances under the same window.The graph node structure feature acquisition method was improved to reflect the influence of node value differences on embedding.Then,the structural features and relational features of nodes were fused to enhance the similarity between nodes with similar neighbor nodes.In the clustering stage,a parameter called alternative nodes number was designed to alleviate the sensitivity of cut-off distance.The parameter analysis and performance evaluation were carried out on the open data set and real business data,and the results showed that the proposed method could obtain accurate and effective clustering results of fault entity nodes.

Key words: fault short text, graph node embedding, local density, graph node clustering