An expert disambiguation method based on attributed graph clustering

Shengxiang Gao, Zhuo Wang, Zhengtao Yu, Jin Jiang, Lin Wu

Abstract: Leveraging expert attributes and their attribute-associated features, we propose an expert disambiguation method based on experts' attributed graph clustering model. In the method, firstly, the attributes and their co-occurrences are identified and extracted. Secondly, based on graph theory, the augmented expert attribute nodes are established and their correlations are connected to form a network of augmented expert attribute graph, which combines experts' attribute consistency and graph' structural consistency. Finally, we establish an entropy model to measure attribute information and structural information, and by minimizing the entropy of super nodes and super edges, we obtain the clustering partition for multiple expert nodes. The experimental results on real-world datasets show that the proposed method significantly outperforms the state-of-art spectral clustering method and the semi-supervised graph clustering method for the accuracy of disambiguation.

External IDs:dblp:conf/ijcnn/GaoWYJW16