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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 48773 (19)
- University: Sharif University of Technology
- Department: Computer Engineering
- Advisor(s): Rabiee, Hamid Reza
- Abstract:
- Online social networks have become very popular in recent years. Most people usually get involved in multiple social networks to enjoy new contents and different social interactions. Many new social networks try to attract users of other existing networks to increase the number of their users. Once a user (called source user) of a social network (called source network) joins a new social network (called target network), a new internetwork link, known as anchor link, is formed between the source and target networks. In this thesis, we concentrate on predicting the formation of such anchor links between heterogeneous social networks. Unlike conventional link prediction problems in which the formation of a link between two existing users within a single network is predicted, in anchor link prediction, the target user is missing and will be added to the target network once the anchor link is created. We tackle the problem of anchor link prediction in two steps. In the first step, we use meta-path as a powerful tool for utilizing heterogeneous information in both the source and target networks. To this end, we propose an effective general meta-path-based approach called Connector and Recursive Meta-Paths (CRMP). By using those two different categories of meta-paths, we model different aspects of social factors that may affect a source user to join the target network, resulting in the formation of a new anchor link. We formulate the problem as a binary classification task and utilize connector and recursive meta-paths to extract a feature vector for each non-anchor user in order to predict formation of anchor links. In the second step, we use the feature vectors extracted in the first step and propose a probabilistic method called Non-Parametric Generalized Linear Model (NP-GLM) to predict the time in which an anchor link will be created. Extensive experiments both on real-world heterogeneous social networks and synthetic datasets demonstrate the effectiveness of the proposed method against the existing ones
- Keywords:
- Link Prediction ; Heterogeneous Networks ; Social Networks ; Aligned Networks ; Meta-Path ; Anchor Links
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