.png){kind=icon}
.png){kind=icon}
Return
Representation learning using Attention Network and CNN for networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2021.115628.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Network embedding (NE), also known as network representation learning (NRL), is a method to learn a low dimensional latent representation of nodes in an information network. The real-world data is usually presented in the form of heterogeneous information network (HIN) with multiple types of nodes and edges. Because of the rich information in HINs, it is necessary for a network embedding method to incorporate this information into the low-dimensional potential representation of the nodes as much as possible. In this paper, we propose a semi supervised representation learning model using a graph attention network and a convolutional neural network (CNN) for HINs, called RANCH. In the part of the graph attention network, we construct a heterogeneous graph attention network using heterogeneous edges to preserve the features of nodes and the structure of network. In the part of the CNN, we leverage a 1D-CNN sentence classification model from natural language processing (NLP) community by adopting edge-constrained truncated random walks to generate node sequences, which can be treated as a corpus of words and sentences. The latter part further integrates the structural information of the network on the basis of the previous part and strengthens the influence of the node's label information on the node representation. We have performed experiments of node classification on three real-world datasets, and the result shows that our model performs better than the state-of-the-arts.
Keywords:
Network representation learning
Heterogeneous information network
Graph attention network
Convolutional neural network
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W