I am a researcher currently working on topics related to Spectral Hypergraph Theory, Personalized PageRank,
Machine Learning, and Local Clustering. I am being guided by Dr. Amitabha Bagchi in my academic journey.
Research Interests
My research focuses on advanced algorithms in graph theory, with a special emphasis on:
- Spectral Hypergraph Theory: This area explores the spectral properties of hypergraphs, which generalize graphs by allowing edges (hyperedges) to connect more than two vertices. The focus is on developing algorithms to efficiently analyze the structure of hypergraphs and study their eigenvalues, eigenvectors, and their application to problems such as community detection and clustering in high-dimensional data.
- Personalized PageRank: A variation of the traditional PageRank algorithm that tailors the ranking of nodes in a hypergraph based on a user's personalized query.
- Local Clustering: Methods for finding clusters or communities in large-scale hypergraphs that are locally sensitive and computationally efficient.
Click here to view or download my resume.
Useful Resources
Research Publications
2024
- Raj Kamal, Amitabha Bagchi.
A Lovasz-Simonovits Theorem for Hypergraphs with Application to Local Clustering.
Accepted to ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2025), June 2025.
Published as Proc. ACM. Managment of Data, 2(4):1-27, Article No. 190, 30 September 2024.
doi:10.1145/3677126.
Download here.
- Mahdihusain Momin, Raj Kamal, Shantwana Dixit, Sayan Ranu, Amitabha Bagchi.
KWIQ: Answering k-core Window Queries in Temporal Networks.
In 26th International Conference on Extending Database Technology (EDBT '23), pp 208-220, March 2023.
doi:10.48786/EDBT.2023.17
Contact
You can reach out to me through the following channels:
- Email: raj.cse.iitd@gmail.com
- GitHub: https://github.com/Raj-Kamal-CSE-IITD
- Google-Scholar: https://scholar.google.com/citations?user=HbnGm3cAAAAJ&hl=en