LINK PREDICTION ON SOCIAL NETWORKS BASED ON DEEP LEARNING

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Keikha Mohammad Mehdi; Rahgozar Maseud

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2018 | Issue:4

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Information Seminar Paper

Title

LINK PREDICTION ON SOCIAL NETWORKS BASED ON DEEP LEARNING

Author(s)

Keikha Mohammad Mehdi | Rahgozar Maseud

Keywords

LINK PREDICTION

DEEP LEARNING

FEATURE VECTOR OF NODE

NETWORK COMMUNITIES

SOCIAL NETWORKS

Abstract

LINK PREDICTION ON SOCIAL NETWORKS IS ONE OF THE ISSUES THAT HAS ATTRACTED MANY RESEARCHERS IN RECENT YEARS. IN THIS PROBLEM, MISSING AND FUTURE LINKS ARE PREDICTED BY USING EXISTING LINKS IN THE. ONE OF THE NEWEST APPROACHES TO THIS PROBLEM IS THE USE OF DEEP LEARNING TO EXTRACT THE VECTOR OF THE FEATURES OF EACH NODE AND THEN FIND MISSING AND FUTURE LINKS. THIS PAPER PRESENTS A METHOD FOR LEARNING THE VECTOR REPRESENTATION OF NETWORK NODES BASED ON THE INFORMATION OF THE NODES ADJACENT TO EACH NODE IN THE SOCIAL NETWORK AND THE VARIOUS LINKS PRESENT ON THE NETWORK. THE RESULTS SHOW THAT THE PROPOSED METHOD PROVIDES GOOD RESULTS FOR LINK PREDICTION IN COMPARISON WITH OTHER METHODS.

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