ArticleScientific reports2016
Stability of similarity measurements for bipartite networks.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Information cocoons in online navigation.iScience · 2023Article
- Cluster-based network proximities for arbitrary nodal subsets.Scientific reports · 2018Article
- Fractal and multifractal analyses of bipartite networks.Scientific reports · 2017Article
- Identifying the perceptive users for online social systems.PloS one · 2017Article
- Social influence on selection behaviour: Distinguishing local- and global-driven preferential attachment.PloS one · 2017Article
- Article
- The robustness of ecosystems to the species loss of community.Scientific reports · 2016Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Similarity is a fundamental measure in network analyses and machine learning algorithms, with wide applications ranging from personalized recommendation to socio-economic dynamics. We argue that an effective similarity measurement should guarantee the stability even under some information loss. With six bipartite networks, we investigate the stabilities of fifteen similarity measurements by comparing the similarity matrixes of two data samples which are randomly divided from original data sets. Results show that, the fifteen measurements can be well classified into three clusters according to their stabilities, and measurements in the same cluster have similar mathematical definitions. In addition, we develop a top-n-stability method for personalized recommendation, and find that the unstable similarities would recommend false information to users, and the performance of recommendation would be largely improved by using stable similarity measurements. This work provides a novel dimension to analyze and evaluate similarity measurements, which can further find applications in link prediction, personalized recommendation, clustering algorithms, community detection and so on.
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Registered trials
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