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Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links

Javari, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1145/2501977
  3. Abstract:
  4. Social network analysis and mining get ever-increasingly important in recent years, which is mainly due to the availability of large datasets and advances in computing systems. A class of social networks is those with positive and negative links. In such networks, a positive link indicates friendship (or trust), whereas links with a negative sign correspond to enmity (or distrust). Predicting the sign of the links in these networks is an important issue and hasmany applications, such as friendship recommendation and identifyingmalicious nodes in the network. In this manuscript, we proposed a new method for sign prediction in networks with positive and negative links. Our algorithm is based first on clustering the network into a number of clusters and then applying a collaborative filtering algorithm. The clusters are such that the number of intra-cluster negative links and inter-cluster positive links are minimal, that is, the clusters are socially balanced as much as possible (a signed graph is socially balanced if it can be divided into clusters with all positive links inside the clusters and all negative links between them). We then used similarity between the clusters (based on the links between them) in a collaborative filtering algorithm. Our experiments on a number of real datasets showed that the proposedmethod outperformed previousmethods, including those based on social balance and status theories and one based on a machine learning framework (logistic regression in this work)
  5. Keywords:
  6. Cluster identification ; Social balance theory ; Social networks ; Social status theory ; Collaborative filtering ; Forecasting ; Signal filtering and prediction ; Collaborative filtering algorithms ; Learning frameworks ; Logistic regressions ; Number of clusters ; Signed networks ; Social balances ; Social network analysis and minings ; Social status ; Social networking (online)
  7. Source: ACM Transactions on Intelligent Systems and Technology ; Vol. 5, issue. 2 , 2014 ; ISSN: 21576904
  8. URL: http://dl.acm.org/citation.cfm?doid=2611448.2501977