Hypergraph learning, Graph Neural Networks
Social and Information Network Analysis, Anomaly Detection
Scientometrics, Informetrics
Predictive Modeling, Recommender Systems
Classification
During my Ph.D. studies at Macquarie University, completed in December 2024, I focused on graph data mining, particularly exploring the intricate patterns and dynamics of hypergraphs within real-world graph structures. My research emphasized moving beyond traditional pairwise relationships to study multifaceted interactions, leading to the development of hypergraph neural networks designed to capture and analyze these complex relationships. By designing innovative models, I sought to address the limitations of traditional graphs, enabling more robust representations of complex systems. These hypergraph structures provide richer data representations, uncovering subtle patterns and higher-order dependencies often missed in conventional approaches. This approach not only advances theoretical understanding but also facilitates practical implementations, opening new avenues for solving complex problems in data science. This comprehensive framework allows for more accurate and insightful analyses, empowering data-driven decision-making across sectors such as social network analysis, education, business intelligence, and medical informatics.
Prior to this, during my Master's research at the International Islamic University Islamabad, I specialized in anomaly detection within bibliographic networks, addressing the challenges of identifying evolution-based anomalies in dynamic data. My work also encompassed mining social and information networks, focusing on evolution structures, semantics, and diffusion processes in heterogeneous information networks. This research informed practical applications in areas such as anomaly detection, recommender systems, and influence analysis.
Funding Agency: Deanship of Scientific Research, University of Jeddah, Jeddah, Saudi Arabia
Funding: 30, 000 SAR (10, 000 SAR/project)
Title: Measuring the Impact of COVID-19 Surveillance Variables over International Oil Market Grant Number: UJ-20-DR-044
Title: Indexing Important Drugs from Medical Literature, Grant Number: UJ-20-DR-047
Title: Measuring Impact of Co-author Count on Citation Count of Research Publications, Grant Number: UJ-20-DR-048