A Novel Link Prediction Approach on Social Networks

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Rezavandi Shoaii S.; ZARE H.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR Year:2018 | Volume:16 | Issue:3 Page(s): 223-230

Download Full-Text

Persian Verion

View:

1,495

Download:

Cites:

Information Journal Paper

Title

A Novel Link Prediction Approach on Social Networks

Author(s)

Rezavandi Shoaii S. | ZARE H. | Issue Writer Certificate

Keywords

Link predictionQ1

social networksQ1

link miningQ1

supervised learningQ1

similarity measureQ1

heterogeneous networkQ2

Abstract

Nowadays the network science has been attracted many researchers from a wide variety of different fields and many problems in engineering domains are modelled through social networks measures. One of the most important problems in social networks is the prediction of evolution and structural behavior of the networks that is known as Link prediction problem in the related literature. Nowadays people use multiple and different social networks simultaneously and it causes to demonstrate a new domain of research known as heterogenous social networks. There exist a few works on Link prediction problem on heterogenous networks. In this paper, first a novel similarity measure for users in heterogenous networks is defined. Then a novel Link prediction algorithm is described through a supervised learning approach which is consisted by the generated features from the introduced similarity measures. We employ the standard evaluation criteria for verification of the proposed approach. The comparison of the proposed algorithm to the other well-known earlier works showed that our proposed method has better performance than the other methods based on testing on several network datasets.

Cites

No record.

References

No record.

Cite

APA: Copy

Rezavandi Shoaii, S., & ZARE, H.. (2018). A Novel Link Prediction Approach on Social Networks. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, 16(3 ), 223-230. SID. https://sid.ir/paper/228410/en

Vancouver: Copy

Rezavandi Shoaii S., ZARE H.. A Novel Link Prediction Approach on Social Networks. NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR[Internet]. 2018;16(3 ):223-230. Available from: https://sid.ir/paper/228410/en

IEEE: Copy

S. Rezavandi Shoaii, and H. ZARE, “A Novel Link Prediction Approach on Social Networks,” NASHRIYYAH -I MUHANDISI -I BARQ VA MUHANDISI -I KAMPYUTAR -I IRAN, B- MUHANDISI -I KAMPYUTAR, vol. 16, no. 3 , pp. 223–230, 2018, [Online]. Available: https://sid.ir/paper/228410/en

Related Journal Papers