Identity VerifiedWaqas Nawaz, Ph.D.
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Member since December 24, 2020

Assistant Professor


Faculty of Computer and Information Systems, Islamic University of Madinah, KSA

PhD, Kyung Hee University, South Korea




Waqas Nawaz is Assistant Professor at Islamic University Almadinah, Kingdom of Saudi Arabia since 2016. He received his Computer Science PhD degree from Kyung Hee University (South Korea) in 2015. He worked as a Post-Doctoral Fellow in the Institute of Information Systems, Innopolis University, Russia from 2015 to 2016. Afterwards, he joined Faculty of Computer and Information Systems, Islamic University of Madinah, KSA as Assistant Professor. His research interests include graph mining, social network analysis, databases, data mining, big data, image processing, and artificial intelligence.



  •  Institute of Information Systems, Innopolis University, Russia
  •  Aug 2015 - Aug 2019

Researcher, Employee and Teacher Assistant

Course: Data modeling and databases
Worked with Dr. Qiang Qu and Dr. Jooyoung Lee, Assistant Professor at Innopolis University, Russia
Duties and Responsibilities: Deliver lectures occasionally, Take quizzes and Assignments and Grading, Engage the students for doing a semester project, Invigilation etc.



  •  Kyung Hee University, South Korea
  •  Mar 2011 - Aug 2015

Major: Graph Mining

Supervisor: Prof. Young-Koo Lee

Absract: Graph is an extremely versatile data structure in terms of its expressiveness and flexibility to model a range of real life phenomenon, such as social, biological, sensor, and computer networks. Finding groups of vertices based on their similarity is the fundamental graph mining task to get useful insights. The existing methods suffer from scalability issues due to enormous computations of an exact similarity estimation. Therefore, this research introduces Collaborative Similarity Measure (CSM) based on shortest path strategy, instead of all paths, to define structural and semantic relevance among vertices efficiently. The effectiveness of the proposed measure is evaluated for personalized email community detection as an application scenario. However, an abundance of structural information has resulted in non-trivial graph traversals. Shortcut construction is among the utilized techniques implemented for efficient shortest path (SP) traversals on graphs. The shortcut construction, being a computationally intensive task, required to be exclusive and offline, often produces unnecessary auxiliary data. To overcome this issue, we present Shortest Path Overlapped Region (SPORE), a performance-based initiative that improves the shortcut construction performance by exploiting SP overlapped regions. Path overlapping with empirical analysis has been overlooked by shortcut construction systems. SPORE avails this opportunity and provides a solution by constructing auxiliary shortcuts incrementally, using SP trees during traversals, instead of an exclusive step. SPORE is exposed to a graph clustering task, which requires extensive graph traversals to group similar vertices together, for realistic implications. We further suggest an optimization strategy to accelerate the performance of the clustering process using confined subgraph traversals. Leveraging the SPORE with multiple SP computations consistently reduces the latency of the entire clustering process. A parameter-free graph clustering with scalable graph traversal strategy for a billion scale diverse graphs remains an open issue.