TY - JOUR AU - Wang, Yueyang AU - Duan, Ziheng AU - Liao, Binbing AU - Wu, Fei AU - Zhuang, Yueting PY - 2019/07/17 Y2 - 2024/03/28 TI - Heterogeneous Attributed Network Embedding with Graph Convolutional Networks JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 33 IS - 01 SE - Student Abstract Track DO - 10.1609/aaai.v33i01.330110061 UR - https://ojs.aaai.org/index.php/AAAI/article/view/5167 SP - 10061-10062 AB - <p>Network embedding which assigns nodes in networks to lowdimensional representations has received increasing attention in recent years. However, most existing approaches, especially the spectral-based methods, only consider the attributes in homogeneous networks. They are weak for heterogeneous attributed networks that involve different node types as well as rich node attributes and are common in real-world scenarios. In this paper, we propose HANE, a novel network embedding method based on Graph Convolutional Networks, that leverages both the heterogeneity and the node attributes to generate high-quality embeddings. The experiments on the real-world dataset show the effectiveness of our method.</p> ER -