Prediction of social dimensions in a heterogeneous social network

Aiswarya; Radhika M. Pai

doi:10.1007/978-981-10-8569-7_15

Prediction of social dimensions in a heterogeneous social network

Aiswarya, Radhika M. Pai

Department of Information and Communication Technology, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Advances in Machine Learning and Data Science - Recent Achievements and Research Directives
Publisher	Springer Verlag
Pages	139-147
Number of pages	9
ISBN (Print)	9789811085680
DOIs	https://doi.org/10.1007/978-981-10-8569-7_15
Publication status	Published - 01-01-2018
Event	1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017 - Goa, India Duration: 25-10-2017 → 27-10-2017

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	705
ISSN (Print)	2194-5357

Conference

Conference	1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017
Country/Territory	India
City	Goa
Period	25-10-17 → 27-10-17

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Computer Science(all)

Access to Document

10.1007/978-981-10-8569-7_15

Cite this

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title = "Prediction of social dimensions in a heterogeneous social network",

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.",

author = "Aiswarya and Pai, {Radhika M.}",

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booktitle = "Advances in Machine Learning and Data Science - Recent Achievements and Research Directives",

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note = "1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017 ; Conference date: 25-10-2017 Through 27-10-2017",

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Aiswarya, & Pai, RM 2018, Prediction of social dimensions in a heterogeneous social network. in Advances in Machine Learning and Data Science - Recent Achievements and Research Directives. Advances in Intelligent Systems and Computing, vol. 705, Springer Verlag, pp. 139-147, 1st International conference on Latest Advances in Machine learning and Data Science, LAMDA 2017, Goa, India, 25-10-17. https://doi.org/10.1007/978-981-10-8569-7_15

Prediction of social dimensions in a heterogeneous social network. / Aiswarya, ; Pai, Radhika M.
Advances in Machine Learning and Data Science - Recent Achievements and Research Directives. Springer Verlag, 2018. p. 139-147 (Advances in Intelligent Systems and Computing; Vol. 705).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

AB - 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.

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Prediction of social dimensions in a heterogeneous social network

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