Date of Award

8-1-2021

Publication Type

Dissertation

Degree Name

Ph.D.

Department

Computer Science

First Advisor

E. Bagheri

Second Advisor

D. Martinovic

Third Advisor

S. Samet

Keywords

Artificial Intelligence, Breakup Prediction, Deep Learning, Link Dynamics, Machine Learning, Social Network analysis

Rights

info:eu-repo/semantics/openAccess

Abstract

Social Network Analysis (SNA) is an appealing research topic, within the domain of Artificial Intelligence (AI), owing to its widespread application in the real world. In this dissertation, we have proposed effective Machine Learning (ML) and Deep Learning (DL) approaches toward resolving these open problems with regard to SNA, viz: Breakup Prediction, Link Prediction, Node Classification, Event-based Analysis, and Trend/Pattern Analysis. SNA can be employed toward resolving several real-world problems; and ML as well as DL have proven to be very effective methodologies for accomplishing Artificial Intelligence (AI)- related goals. Existing literature have focused on studying the apparent and latent interactions within social graphs as an n-ary operation, which yields binary outputs comprising positives (friends, likes, etc.) and negatives (foes, dislikes, etc.). Inasmuch as interactions constitute the bedrock of any given Social Network (SN) structure; there exist scenarios where an interaction, which was once considered a positive, transmutes into a negative as a result of one or more indicators which have affected the interaction quality. At present, this transmutation has to be manually executed by the affected actors in the SN. These manual transmutations can be quite inefficient, ineffective, and a mishap might have been incurred by the constituent actors and the SN structure prior to a resolution. Thus, as part of the research contributions of this dissertation, we have proposed an automatic technique toward flagging positive ties that should be considered for breakups or rifts (negative-tie state), as they tend to pose potential threats to actors and the SN. Furthermore, in this dissertation, we have proposed DL-based approaches based on edge sampling strategy for resolving the problems of Breakup Prediction, Link Prediction, and Node Classification. Also, we have proposed ML-based approaches for resolving the problems of Event-based Analysis and Trend/Pattern Analysis. We have evaluated our respective approaches against benchmark social graphs, and our results have been comparatively encouraging as documented herein.

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