The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Network deconvolution as a general method to distinguish direct dependencies in networks
Open accessAbstract
By mathematically 'silencing' spurious, indirect correlations in networks, two groups devise approaches for improving many different types of network analyses. Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.
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Cites
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Wu, X., Jiang, J., Gu, Z., Zhang, J., Chen, Y., & Liu, X. (2020) · Stem Cell Research & Therapy · doi:10.1186/s13287-020-01855-9 · Cited by 343
Connecting the Dots: Identifying Network Structure via Graph Signal Processing
Mateos, G., Segarra, S., Marques, A. G., & Ribeiro, A. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2018.2890143 · Cited by 369
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 299
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Metadata
- Authors
- Feizi, S., Marbach, D., Médard, M., & Kellis, M.
- Venue
- Nature Biotechnology
- Published
- 2013-07-14
- Volume
- 31
- Issue
- 8
- Pages
- 726-733
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2015208937
- Cited by
- 287
- References
- 8
- FWCI
- 11.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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