People usually get involved in multiple social networks to enjoy new services or to fulfill their needs. 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 inter-network link (called anchor link) is formed between the source and target networks. In this paper, we concentrated 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. To solve this problem, we use meta-paths 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. Extensive experiments on real-world heterogeneous social networks demonstrate the effectiveness of the proposed method against the recent methods.
Table shows a comparison between link prediction, anchor link inference, and anchor link prediction problems. Besides the conventional link prediction studies, there are other works that are related to the problem of anchor link prediction. In @cite_11 , the most important factors that cause users to switch between social networks are investigated, empirically. They used the Push-Pull-Mooring model of migration @cite_23 as a basis, and categorized different factors into push, pull, and mooring. However, they did not propose a model for prediction of user migration in social networks. In another related work @cite_10 , have studied the formation and evolution of communities in social networks. They have shown that for a user, both number of her friends and the associated share of her friends' activities in the target community have great impact on her to join that community. We have used the results of these studies as a background theory for the proposed method.
@cite_10 @cite_23 @cite_11
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The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences - political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time.Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are connected to one another. We use decision-tree techniques to identify the most significant structural determinants of these properties. We also develop a novel methodology for measuring movement of individuals between communities, and show how such movements are closely aligned with changes in the topics of interest within the communities. This paper seeks to offer strategic recommendations for SNS providers.We identify characteristics that combine to distinguish SNSs from conventional ISs.Our PPM-based model extends research on IS continuance and service switching.The findings reveal four significant factors that promote switching.SNS providers can devise user strategies grounded in these factors. Users are the most critical strategic resource of any online social networking service (SNS). This paper offers strategic recommendations for SNS providers based on an empirical study exploring why users switch from a primary SNS to others. We first identify important characteristics that combine to distinguish SNSs from conventional information systems, then develop a "cyber migration" research model that includes push, pull and mooring factors which influence user intention to switch from one SNS to another. Findings from a field survey of 180 users reveal four significant factors that promote switching: dissatisfaction with socialization support, dissatisfaction with entertainment value, continuity cost, and peer influence. Strategies grounded in these factors are suggested for SNS providers to better attract and retain users.