ArticleProceedings of the National Academy of Sciences of the United States of America2015
Toward link predictability of complex networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers.
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Who cites it
62 citing papers in PubMed.
- Predictability of complex networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- MiRInter-Trans: a transformer-based framework for microRNA interaction prediction.Bioinformatics advances · 2026Article
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Neighbor-Enhanced Link Prediction in Bipartite Networks.Entropy (Basel, Switzerland) · 2025Article
- Link prediction of heterogeneous complex networks based on an improved embedding learning algorithm.PloS one · 2025Article
- Inconsistency among evaluation metrics in link prediction.PNAS nexus · 2024Article
- The maximum capability of a topological feature in link prediction.PNAS nexus · 2024Article
- On the complexity of quantum link prediction in complex networks.Scientific reports · 2024Article
- Drug-drug interaction prediction: databases, web servers and computational models.Briefings in bioinformatics · 2023Review
- Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage.Briefings in bioinformatics · 2023Article
- Enhancing Predictability Assessment: An Overview and Analysis of Predictability Measures for Time Series and Network Links.Entropy (Basel, Switzerland) · 2023Review
- HEM: An Improved Parametric Link Prediction Algorithm Based on Hybrid Network Evolution Mechanism.Entropy (Basel, Switzerland) · 2023Article
- MSResG: Using GAE and Residual GCN to Predict Drug-Drug Interactions Based on Multi-source Drug Features.Interdisciplinary sciences, computational life sciences · 2023Article
- Link Prediction with Continuous-Time Classical and Quantum Walks.Entropy (Basel, Switzerland) · 2023Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- "Stealing fire or stacking knowledge" by machine intelligence to model link prediction in complex networks.iScience · 2023Article
- How regularly do people visit service places?Computers, environment and urban systems · 2023Article
- The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis.GigaScience · 2022Article
- SPCMLMI: A structural perturbation-based matrix completion method to predict lncRNA-miRNA interactions.Frontiers in genetics · 2022Article
- RETRACTED ARTICLE: An Intelligent Centrality Measures for Influential Node Detection in COVID-19 Environment.Wireless personal communications · 2022Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.