Deep Learning with Graph Convolutional Networks: An Overview and Latest Applications in Computational Intelligence
Hainan University · Balochistan University of Information Technology, Engineering and Management Sciences · +1 more institution
Abstract
Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real‐life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI),…
Citation impact
- FWCI
- 67.54
- Percentile
- 100%
- References
- 151
Authors
5Topics & keywords
- Computer science
- Graph
- Artificial intelligence
- Pooling
- Convolutional neural network
- Graph database
- Power graph analysis
- Deep learning
- Industry, innovation and infrastructure