Similarity Search in Graph Databases: A Multi-Layered Indexing Approach

By Yongjiang Liang, Peixiang Zhao. 2017

83 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, 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 Computational Theory and Mathematics

1. Ting-Li Chen
2. Tingting Chen
3. Yang Qiao
4. Yizhou Sun
5. Wei Wang
6. Haoyang Ding
7. Yunsheng Bai
8. Ken Gu

In Computer Networks and Communications

1. Ting-Li Chen
2. Yizhou Sun
3. Tingting Chen
4. Yang Qiao
5. Song Bian
6. Wei Wang
7. Haoyang Ding
8. Yunsheng Bai

In Computer Vision and Pattern Recognition

1. Yizhou Sun
2. Ting-Li Chen
3. Tingting Chen
4. Yang Qiao
5. Song Bian
6. Wei Wang
7. Haoyang Ding
8. Yunsheng Bai

Citations in the corpus

• Unsupervised Inductive Whole-Graph Embedding by Preserving Graph Proximity (2019, methodology): “Metrics such as Graph Edit Distance (GED) Bunke (1983) and Maximum Common Subgraph (MCS) Bunke & Shearer (1998) have been widely adopted in graph database search Yan et al. (2005); Liang & Zhao (2017), are well-defined and general to any domain.”
• Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity (2019, methodology): “Metrics such as Graph Edit Distance (GED) [Bunke, 1983] and Maximum Common Subgraph (MCS) [Bunke and Shearer, 1998] have been widely adopted in graph database search [Yan et al., 2005; Liang and Zhao, 2017], are well-defined and general to any domain.”
• SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (2018, methodology): “Computing GED is a primitive operator in graph database analysis, and has been adopted in a series of works on graph similarity search [31, 51, 55, 56, 58].”