Malik Khizar Hayat
Biographic Data
| ID | 9551907 |
|---|---|
| NAME | Malik Khizar Hayat |
| GIVEN NAMES | Malik Khizar |
| FAMILY NAME | Hayat |
| SIGNATURE | HAYAT M K |
| AFFILIATIONS | Macquarie University |
| ORCID | 0000-0001-8177-2042 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Dynamic Hypergraph for Cross-Domain Session-Based Social Recommendations
The dynamic and temporal nature of user interactions on social media platforms has increased the importance of session-based recommendations. The adoption of graph neural networks in these systems has increased due to their capacity to incorporate structural topology and node information. Nevertheless, current session-based recommendation methods relying on pairwise graph structure ignore intricate and evolving interactions between users, items, …
Heading Towards Sub-Discipline Rankings for Higher Education Institutions
Although the system for annual rankings of higher education institutions (HEIs) faces considerable criticism, these rankings are here to stay. Having become competent in assigning holistic ranking scores to HEIs, reputed ranking entities have now started focusing on subject-specific and regional rankings. However, in experts’ opinion, the process of assigning rankings should be more consistent, transparent, and representative. This study focuses …
Identifying Rising Stars via Supervised Machine Learning
Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform pre…
Finding rising stars in bibliometric networks
No prominent works on this page.
Finding rising stars in bibliometric networks
Heading Towards Sub-Discipline Rankings for Higher Education Institutions
Although the system for annual rankings of higher education institutions (HEIs) faces considerable criticism, these rankings are here to stay. Having become competent in assigning holistic ranking scores to HEIs, reputed ranking entities have now started focusing on subject-specific and regional rankings. However, in experts’ opinion, the process of assigning rankings should be more consistent, transparent, and representative. This study focuses …
Identifying Rising Stars via Supervised Machine Learning
Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform pre…
Dynamic Hypergraph for Cross-Domain Session-Based Social Recommendations
The dynamic and temporal nature of user interactions on social media platforms has increased the importance of session-based recommendations. The adoption of graph neural networks in these systems has increased due to their capacity to incorporate structural topology and node information. Nevertheless, current session-based recommendation methods relying on pairwise graph structure ignore intricate and evolving interactions between users, items, …
Computer Science (4 works) · Mathematics (3 works) · World Wide Web (3 works) · Advanced Graph Neural Networks (2 works) · Artificial Intelligence (2 works) · Complex Network Analysis Techniques (2 works) · Data science (2 works) · Economics (2 works) · Information retrieval (2 works) · Productivity (2 works)