Multi-graph-view Learning for Graph Classification

By Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang. 2014

52 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hui Xiong
2. Chuan Zhou
3. Jia Wu
4. Quan Z. Sheng
5. Shan Xue
6. Jian Yang
7. Xiaoxiao Ma
8. Philip S. Yu

In Computer Vision and Pattern Recognition

1. Hui Xiong
2. Jia Wu
3. Chuan Zhou
4. Philip S. Yu
5. Cécile Paris
6. Surya Nepal
7. Quan Z. Sheng
8. Di Jin

Citations in the corpus

• A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “, news in social media are represented as bag-of-words [20], and images in web pages are represented as color histograms [21]), and then detect outlying data points in the vector space [22]– [24], as shown in Fig.”
• A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “Attributed multi-views networks provide a new way to describe relational information in multiple informative views, each containing a type of attributes [183].”