Noise Robust Network Alignment is a network alignment framework that belongs to the class of node correspondence methods in graph analysis and is distinguished by its capacity to reliably infer structural equivalences between networks despite the presence of topological noise, attribute inaccuracies, or spurious connections.
5
Publications
334
Citations
20
Authors
15
Institutions
Publications per year
2017–2026
3
Parents
Publications
5
Jing Zhang, Bo Chen, Xianming Wang et al. · 2018 · 73 citations
Computing, Data Science, Data Mining +14
Robust Multi-Network Clustering via Joint Cross-Domain Cluster Alignment
Rui Liu, Wei Cheng, Hanghang Tong et al. · 2015 · 30 citations
1–5 of 5
Authors
20
- GS
1
h-index
- RL
1
h-index
- HC
1
h-index
- JZ
1
h-index
- XW
1
h-index
1–5 of 20
Institutions
15
Peking University
Beijing, China
1 h-index
1 publications
73 citations
University of California, Los Angeles
Los Angeles, United States
1 h-index
1 publications
30 citations
The University of Queensland
Brisbane, Australia
1 h-index
1 publications
81 citations
University of Michigan
Ann Arbor, United States
1 h-index
4 publications
188 citations
Indiana University Bloomington
Bloomington, United States
1 h-index
3 publications
309 citations
Tsinghua University
Beijing, China
1 h-index
1 publications
73 citations
Griffith University
Brisbane, Australia
1 h-index
2 publications
162 citations
Arizona State University
Tempe, United States
1 h-index
1 publications
30 citations
1–8 of 15