Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases

By Wook-Shin Han, Jinsoo Lee, Jeong-Hoon Lee. ACM SIGMOD Conference, 2013

439 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b02aba65fb2792cee4fb0cbae12858195f383764

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. Lu Qin
2. W. Zhang
3. Xuemin Lin
4. Fei Bi
5. Lijun Chang

In Computer Vision and Pattern Recognition

1. Lu Qin
2. W. Zhang
3. Xuemin Lin
4. Fei Bi
5. Lijun Chang

In Signal Processing

1. Lu Qin
2. W. Zhang
3. Xuemin Lin
4. Fei Bi
5. Lijun Chang

Citations in the corpus

• Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting (2020, neutral): “[52], [53], [54], [55], [56], [57] provide practical implementa-”
• Efficient Subgraph Matching by Postponing Cartesian Products (2016, methodology): “The technique TurboISO in [8] proposes to exactly enumerate all paths to overcome the limitations in [22] that possibly overestimates the join cardinality by an estimation formula.”