Heterogeneous Graph Embedding Based on Graph Convolutional Neural Networks

• Main Authors: Su, Yu-Sheng, 蘇裕勝
• Other Authors: Tsai, Ming-Feng
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/222yfr

碩士 === 國立政治大學 === 資訊科學系 === 108 === In recent years, information network embedding has become popular because the techniques enable to encode information into low-dimensions representation, even for a graph/network with multiple types of nodes and relations. In addition, graph neural network (GNN) has also shown its effectiveness in learning large-scale node representations on node classification. In this paper, therefore, we propose a framework based on the heterogeneous network embedding and the idea of graph neural network. In our framework, we first generate node representations by various network embedding methods. Then, we split a homogeneous network graph into subgraphs and concatenate the learned node representations into the same embedding space. After that, we apply one of variant GNN, called GraphSAGE, to generate representations for the tasks of link prediction and recommendation. In our experiments, the results on the tasks of link prediction and recommendation both show the effectiveness of the proposed framework.