Stability of similarity measurements for bipartite networks
arXiv:1512.01432
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--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.
arXiv comment11 pages, 5 figures
References in corpus (12)
- Emergence of scaling in random networks
- Statistical mechanics of complex networks
- Hierarchical organization of modularity in metabolic networks
- Hierarchical structure and the prediction of missing links in networks
- Predicting Missing Links via Local Information
- How to project a bipartite network?
- Solving the apparent diversity-accuracy dilemma of recommender systems
- The complex network of global cargo ship movements
- Vertex similarity in networks
- Popularity versus Similarity in Growing Networks
- Heat Conduction Process on Community Networks as a Recommendation Model
- Information filtering via biased heat conduction