计算机科学
关系(数据库)
依赖关系(UML)
嵌入
节点(物理)
多路复用
光学(聚焦)
理论计算机科学
人工智能
图形
网络拓扑
数据挖掘
依赖关系图
计算机网络
工程类
生物信息学
结构工程
生物
光学
物理
作者
Shimeng Zhan,Nianwen Ning,Kai Zhao,Lianwei Li,Bin Wu,Bai Wang
出处
期刊:International Joint Conference on Neural Network
日期:2021-07-18
被引量:1
标识
DOI:10.1109/ijcnn52387.2021.9534065
摘要
Multiplex networks contain multiple types of relations between nodes, where each relation type is modeled as one layer. In the real world, a relation type may only depend on certain attribute features of nodes. Most existing multiplex network embedding methods only focus on preserving consistent information or complementary information from multiplex networks. However, these methods ignore the dependency between node attributes and the topology of each relation. To address the problem, we propose a model called DAME (Disentangled-based Adversarial Network for Multiplex Network Embedding). We utilize generative adversarial learning to preserve the consistent and complementary information between different relation types. Meanwhile, we develop a disentangled graph convolution network (DGCN) based on disentangled learning, enabling DAME to capture the dependency between node attributes and each relation type. We conduct extensive experiments on five realworld datasets. Experimental results indicate the effectiveness of DAME on link prediction and node classification tasks.
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