DeepAI AI Chat
Log In Sign Up

Heterogeneous Attributed Network Embedding with Graph Convolutional Networks

07/12/2020
by   Ziheng Duan, et al.
0

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.

READ FULL TEXT

page 1

page 2

04/08/2021

QD-GCN: Query-Driven Graph Convolutional Networks for Attributed Community Search

Recently, attributed community search, a related but different problem t...
11/09/2022

Optimal Graph Filters for Clustering Attributed Graphs

Many real-world systems can be represented as graphs where the different...
10/31/2019

Semi-supervisedly Co-embedding Attributed Networks

Deep generative models (DGMs) have achieved remarkable advances. Semi-su...
05/14/2019

ActiveHNE: Active Heterogeneous Network Embedding

Heterogeneous network embedding (HNE) is a challenging task due to the d...
04/18/2019

node2bits: Compact Time- and Attribute-aware Node Representations for User Stitching

Identity stitching, the task of identifying and matching various online ...
07/03/2020

Adaptive Graph Encoder for Attributed Graph Embedding

Attributed graph embedding, which learns vector representations from gra...
11/19/2018

Outlier Aware Network Embedding for Attributed Networks

Attributed network embedding has received much interest from the researc...