2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2016
DOI: 10.1109/asonam.2016.7752228
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Predicting anchor links between heterogeneous social networks

Abstract: Abstract-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 su… Show more

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Cited by 8 publications
(4 citation statements)
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“…In recent years, newer studies have shifted from traditional link prediction on static and homogeneous networks toward newer domains, considering heterogeneity and dynamicity of networks [10,12,14,25,29]. However, most of these works merely focus on one of these aspects, disregarding the other.…”
Section: Sajadmanesh Et Almentioning
confidence: 99%
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“…In recent years, newer studies have shifted from traditional link prediction on static and homogeneous networks toward newer domains, considering heterogeneity and dynamicity of networks [10,12,14,25,29]. However, most of these works merely focus on one of these aspects, disregarding the other.…”
Section: Sajadmanesh Et Almentioning
confidence: 99%
“…For example, the co-authorship meta-path A → P ← A in a bibliographic network creates a sense of similarity between two Author nodes. These type of meta-paths, called similarity meta-paths, are widely used to define topological features for link prediction problem in heterogeneous networks [29,33,46]. Table 1 presents a number of similarity meta-paths that can be defined on DBLP, Delicious, and MovieLens networks to capture the heterogeneous similarity between different node types.…”
Section: Dynamic Feature Extractionmentioning
confidence: 99%
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“…is will result in sparse and incomplete relationships. And, a number of existing works also use UGCs to user identification [4,20]. ese methods are usually based on posting time, location, writing style, or similarity of content [4].…”
Section: Introductionmentioning
confidence: 99%