Prediction of social dimensions in a heterogeneous social network

Aiswarya, Radhika M. Pai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advancements in communication and computing technologies allow people located geographically apart to meet on a common platform to share information with each other. Social networking sites play an important role in this aspect. A lot of information can be inferred from such networks if the data is analyzed appropriately by applying a relevant data mining method. The proposed work concentrates on leveraging the connection information of the nodes in a social network for the prediction of social dimensions of new nodes joining the social network. In this work, an edge clustering algorithm and a multilabel classification algorithm are proposed to predict the social dimensions of the nodes joining an existing social network. The results of the proposed algorithms are found out to be satisfactory.

Original languageEnglish
Title of host publicationAdvances in Machine Learning and Data Science - Recent Achievements and Research Directives
PublisherSpringer Verlag
Pages139-147
Number of pages9
ISBN (Print)9789811085680
DOIs
Publication statusPublished - 01-01-2018
Event1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017 - Goa, India
Duration: 25-10-201727-10-2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume705
ISSN (Print)2194-5357

Conference

Conference1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017
CountryIndia
CityGoa
Period25-10-1727-10-17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science(all)

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  • Cite this

    Aiswarya, & Pai, R. M. (2018). Prediction of social dimensions in a heterogeneous social network. In Advances in Machine Learning and Data Science - Recent Achievements and Research Directives (pp. 139-147). (Advances in Intelligent Systems and Computing; Vol. 705). Springer Verlag. https://doi.org/10.1007/978-981-10-8569-7_15