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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT)
Debby Oghenevwede Otakore
Federal University of Petroleum Resources Effurun
Computer Science & IT Engineering

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Intelligent cluster connectionist recommender system using implicit graph friendship algorithm for social networks Arnold Adimabua Ojugo; Debby Oghenevwede Otakore
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 3: September 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1103.679 KB) | DOI: 10.11591/ijai.v9.i3.pp497-506

Abstract

Implicit clusters are formed as a result of the many interactions between users and their contacts. Online social platforms today provide special link-types that allows effective communication. Thus, many users can hardly categorize their contacts into groups such as “family”, “friends” etc. However, such contact clusters are easily represented via implicit graphs. This has arisen the need to analyze users’ implicit social graph and enable the automatic add/delete of contacts from/to a group via a suggestion algorithm – making the group creation process dynamic (instead of static, where users are manually added or removed). The study implements the friend suggest algorithm, which analyzes a user’s implicit social graph to create custom contact group using an interaction-based metric to estimate a user’s affinity to his contacts and groups. The algorithm starts with a small seed set of contacts – already categorized by a user as friends/groups; And, then suggest other contacts to be added to a group. The result inherent demonstrates the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.
Computational solution of networks versus cluster grouping for social network contact recommender system Arnold Adimabua Ojugo; Debby Oghenevwede Otakore
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 9, No 3: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (439.078 KB) | DOI: 10.11591/ijict.v9i3.pp185-194

Abstract

Graphs have become the dominant life-form of many tasks as they advance a structural system to represent many tasks and their corresponding relationships. A powerful role of networks and graphs is to bridge local feats that exist in vertices or nodal agents as they blossom into patterns that helps explain how nodes and their corresponding edges impacts a complex effect that ripple via a graph. User cluster are formed as a result of interactions between entities – such that many users today, hardly categorize their contacts into groups such as “family”, “friends”, “colleagues”. The need to analyze such user social graph via implicit clusters, enables the dynamism in contact management. Study seeks to implement this dynamism via a comparative study of the deep neural network and friend suggest algorithm. We analyze a user’s implicit social graph and seek to automatically create custom contact groups using metrics that classify such contacts based on a user’s affinity to contacts. Experimental results demonstrate the importance of both the implicit group relationships and the interaction-based affinity in suggesting friends.
Co-Authors Arnold Adimabua Ojugo