【摘要】 针对同质性关系分类器基于一阶Markov假设简化处理的局限性，在类分布关系近邻分类器构建类向量和参考向量时，引入局部图排序激活扩散方法，并结合松弛标注的协作推理方法，通过适当扩大分类时邻居节点的范围增加网络数据中待分类节点的同质性，从而降低分类错误率.对比实验结果表明，该方法扩大了待分类节点的邻域，在网络数据上分类精度较好.更多还原

【Abstract】 Aiming at the limitation of the simplifying the processing of homophily relational classifiers based on first-order Markov assumption, when constructing the class vector and reference vector in the class-distribution relational neighbor classifier, we introduced the activation spreading algorithm of local graph ranking, combined with the relaxation labeling collective inference method. By appropriately expanding the range of neighboring nodes during classification, we increased the homophily of nodes to be classified in network data, thereby reducing the error rate of classification. The comparative experimental results show that this method expands the neighborhood of nodes to be classified, and has good classification accuracy on network data.更多还原