Event based analysis in social network using the approaches of machine learning

Registration ID: IJNRD_204938

Published ID: IJNRD2309110

DOI: Click Here to Get

Naga Hari Babu , Swapna Vanguru , Bhavani Vanama

Keywords: Social Networks, Machine learning, Clustering, Classification, WWW

Abstract : The importance of machine learning for social network analysis is realized as an inevitable tool in forthcoming years. This is due to the rapid growth of data in social network, increased by the proliferation of social media websites and the embedded heterogeneity and complexity. In recent times, Social Network Analysis has become a very important and interesting subject matter with regard to Artificial Intelligence in that a vast variety of processes, comprising animate and inanimate entities, can be examined by means of SNA. In this regard, SNA has been employed by security agencies for counter-intelligence and law enforcement purposes. SNA has been used in medicine and pharmaceuticals for gaining insights into protein-protein interactions. Also, SNA has been employed in the World Wide Web (WWW) for hyperlink analysis, cyber society analysis, sentiment analysis, etc. Furthermore, prediction tasks within social network structures have become significant research problems in SNA. Thus, hidden facts and details embedded in social network structures can be effectively and efficiently harnessed for training AI models with the goal of predicting several missing components (such as links/ties, nodes/actors, structure type, etc.) within a given social network. Therefore, important factors such as the individual attributes of spatial social actors, and the underlying patterns of relationship binding these social actors must be taken into consideration; because these factors are 1 relevant in understanding the nature and dynamics of a given social network structure. SNA is a subdomain (or research topic) within the domain (or research area) of AI; and several open problems still exist with regard to SNA. Some of these open problems with respect to SNA are included . This paper proposes two important things. 1) the various approaches which dealt by machine learning with concern to deep learning viz: Information Diffusion, Community Detection, Event-Based Analysis, Multi-Layer Network Analysis, Trends and Patterns Analyses, Sentiment Analysis, Collaboration and Knowledge Management, Node Classification, Link Detection, Breakup Prediction, etc. Thus, in this dissertation, we have proposed effective Machine Learning (ML) approaches toward resolving the following SNA (research) problems, namely: Breakup Prediction, Link Prediction, Node Classification, Event-based Analysis, and Trend/Pattern Analysis. 2) Analysing the Event based analysis in social network using the approaches of machine learning.

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Naga Hari Babu, Swapna Vanguru, & Bhavani Vanama (September-2023). Event based analysis in social network using the approaches of machine learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(9), b97-b105. https://ijnrd.org/papers/IJNRD2309110.pdf

Volume 8 Issue 9, September-2023

Pages : b97-b105

Paper Reg. ID: IJNRD_204938

Published Paper Id: IJNRD2309110

Downloads: 000121978

Research Area: Engineering

Country: Medchal, Telanagana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2309110.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2309110

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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