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Social friend recommendation based on multiple network correlation

Journal Article


Abstract


  • Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or 'friend's friends are friends,' which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR). To accomplish this goal, we correlate different 'social role' networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) related networks are aligned by selecting important features from each network, and 2) the network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods.

Authors


  •   Huang, Shangrong (external author)
  •   Zhang, Jian (external author)
  •   Wang, Lei
  •   Hua, Xian-Sheng (external author)

Publication Date


  • 2016

Citation


  • Huang, S., Zhang, J., Wang, L. & Hua, X. (2016). Social friend recommendation based on multiple network correlation. IEEE Transactions on Multimedia, 18 (2), 287-299.

Scopus Eid


  • 2-s2.0-84962314923

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5702

Number Of Pages


  • 12

Start Page


  • 287

End Page


  • 299

Volume


  • 18

Issue


  • 2

Abstract


  • Friend recommendation is an important recommender application in social media. Major social websites such as Twitter and Facebook are all capable of recommending friends to individuals. However, most of these websites use simple friend recommendation algorithms such as similarity, popularity, or 'friend's friends are friends,' which are intuitive but consider few of the characteristics of the social network. In this paper we investigate the structure of social networks and develop an algorithm for network correlation-based social friend recommendation (NC-based SFR). To accomplish this goal, we correlate different 'social role' networks, find their relationships and make friend recommendations. NC-based SFR is characterized by two key components: 1) related networks are aligned by selecting important features from each network, and 2) the network structure should be maximally preserved before and after network alignment. After important feature selection has been made, we recommend friends based on these features. We conduct experiments on the Flickr network, which contains more than ten thousand nodes and over 30 thousand tags covering half a million photos, to show that the proposed algorithm recommends friends more precisely than reference methods.

Authors


  •   Huang, Shangrong (external author)
  •   Zhang, Jian (external author)
  •   Wang, Lei
  •   Hua, Xian-Sheng (external author)

Publication Date


  • 2016

Citation


  • Huang, S., Zhang, J., Wang, L. & Hua, X. (2016). Social friend recommendation based on multiple network correlation. IEEE Transactions on Multimedia, 18 (2), 287-299.

Scopus Eid


  • 2-s2.0-84962314923

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5702

Number Of Pages


  • 12

Start Page


  • 287

End Page


  • 299

Volume


  • 18

Issue


  • 2