A Review of Learning from Heterogeneous Behavior for Social Identity Linkage

K. Swathi

A Review of Learning from Heterogeneous Behavior for Social Identity Linkage

Authors: K. Swathi

Unique Paper ID: 144644
Volume: 3
Issue: 3
PageNo: 231-235

Keywords: Social Identity Linkage Structured Learning multi-objective optimization social identity linkage structureconsistency user behavior trajectory.

Abstract:
Social identity linkage from corner to corner diverse social media platforms is of critical prominence to business intelligence by acquisition from social data a deeper understanding and more accurate profiling of users.In this paper we suggests HYDRA framework with k-mean clustering whichincludes the social media networks which measures two users mention to one person when one of their attributes is same.The action of the user accounts are formed as a cluster by using k-mean clustering and thus the cluster has a data about theuser where it mean to be efficient when proliferation of user increasing. Statistical models of topic distribution constructingstructural consistency graph to evaluate the high-order structure consistency. Lastly, discovering the mapping function bymulti-objective optimization compiled both the supervised learning and the cross platform structure consistencymaximization. Henceforth, this model is able to find the hidden relationships of group of users with high delivery data speed.

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Cite This Article

ISSN: 2349-6002
Volume: 3
Issue: 3
PageNo: 231-235

A Review of Learning from Heterogeneous Behavior for Social Identity Linkage

Available:https://ijirt.org/Article?manuscript=144644

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