A Review of Learning from Heterogeneous Behavior for Social Identity Linkage

A Review of Learning from Heterogeneous Behavior for Social Identity Linkage
Author(s):
K. Swathi
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.
Article Details
Unique Paper ID: 144644 Publication Volume & Issue: Volume 3, Issue 3 Page(s): 231 - 235
Article Preview & Download

Share This Article

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org