Approximate Network Alignment Algorithms is a computational method that belongs to the class of network alignment techniques and is distinguished by its use of heuristic or probabilistic strategies to efficiently find near-optimal node correspondences between networks without guaranteeing exact alignment.
4
Publications
249
Citations
11
Authors
4
Institutions
Publications per year
2017–2026
0
Parents
Publications
4
Automatic Parameter Learning for Multiple Local Network Alignment
Jason Flannick, Antal Novak, Balaji S. Srinivasan et al. · Journal of Computational Biology · 2009 · 96 citations
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Authors
11
- DF
3
h-index
- MHMahantesh Halappanavar
Pacific Northwest National Laboratory, United States
2 publications · 46 citations
2
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- AP
2
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- AK
2
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- YW
1
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1–5 of 11
Institutions
4
Purdue University West Lafayette
West Lafayette, United States
3 h-index
7 publications
245 citations
Pacific Northwest National Laboratory
Richland, United States
2 h-index
2 publications
46 citations
Stanford University
Stanford, United States
2 h-index
7 publications
694 citations
Google (United States)
Mountain View, United States
1 h-index
1 publications
107 citations
1–4 of 4
Venues
- Journal of Computational Biology
1 publications · 96 citations
1
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- ACM Transactions on Knowledge Discovery from Data
TKDD · 1 publications · 107 citations
1
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- IEEE International Conference on High Performance Computing, Data, and Analytics
Conference · 1 publications · 23 citations
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- 2012 International Conference for High Performance Computing, Networking, Storage and Analysis
Conference · 1 publications · 23 citations
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