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IJMLC 2020 Vol.10(4): 576-581 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.4.975

Introducing Hidden Nodes in a Relationship Graph of Instagram Users

Unmanee Unmuang and Sukree Sinthupinyo

Abstract—Online social networks (OSNs) like Facebook, Twitter, and Instagram assist their users in establishing new social relations by making recommendations. In order to make relevant recommendations to users, social network providers need to understand the relations between them. Many studies have discovered unknown relations between users using similarity-based metrics. The most frequently used similarity-based metric is a common neighbor (CN) metric. However, such a metric assumes that the networks are homogeneous, while real-world social networks actually contain various types of entities and relations, making them heterogeneous. It also only considers structural information without leveraging the contextual information of the networks. Consequently, the discovered relations contain no hidden meaning.
In this paper, we analyze the relations between Instagram users considering both structural and contextual information. As a result, we leverage the heterogeneity of the network and discover hidden nodes that carry the semantics of the relations. We perform an analysis in two steps as follows: 1. we consider the structural information of the networks by using common neighbors between two users. We select the top 40 user pairs that share the most common neighbors to execute the next step; 2. We perform a contextual analysis between each user in the user pairs by using posts and comments. We then calculate the term frequency of each token in the comments. We observe that tokens with a high-term frequency value represent contextual information between two users. Finally, we represent these tokens as hidden nodes in the relation between two users using the heterogeneous relation graph.

Index Terms—Hidden node, users relationship, social media, heterogeneous graph, structural information, contextual information, directed networks, combination, sequence matcher, content-based.

The authors are with the Department of Computer Engineering, Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330 Thailand. (e-mail:6170975121@student.chula.ac.th, sukree.s@chula.ac.th).


Cite: Unmanee Unmuang and Sukree Sinthupinyo, "Introducing Hidden Nodes in a Relationship Graph of Instagram Users," International Journal of Machine Learning and Computing vol. 10, no. 4, pp. 576-581, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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