Scholar
Meihao Liao
Google Scholar ID: O6tPNgUAAAAJ
Beijing Institute of Technology (BIT)
Citations & Impact
Citations
162
H-index
8
i10-index
7
Publications
19
Co-authors
13
list available
Publications
Resume (English only)
Academic Achievements
- 1. Efficient Exact Resistance Distance Computation on Small-Treewidth Graphs: A Labelling Approach (SIGMOD, 2026)
- 2. One Index for All: Towards Efficient Personalized PageRank Computation for Every Damping Factor (SIGMOD, 2026)
- 3. Improved Algorithms for Effective Resistance Computation on Graphs (COLT, 2025)
- 4. Efficient Index Maintenance for Effective Resistance Computation on Evolving Graphs (SIGMOD, 2025)
- 5. Efficient and Provable Effective Resistance Computation on Large Graphs: an Index-based Approach (SIGMOD, 2024)
- 6. Scalable Algorithms for Laplacian Pseudo-inverse Computation (In Submission)
- 7. LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning (VLDB, 2024)
- 8. Efficient Maximal Biplex Enumerations with Improved Worst-Case Time Guarantee (SIGMOD, 2024)
- 9. Privacy-Preserving Graph Embedding based on Local Differential Privacy (CIKM, 2024)
- 10. Efficient Resistance Distance Computation: The Power of Landmark-based Approaches (SIGMOD, 2023)
- 11. Efficient Personalized PageRank Computation: The Power of Variance-Reduced Monte Carlo Approaches (SIGMOD, 2023)
- 12. Maximal Defective Clique Enumeration (SIGMOD, 2023)
- 13. Hereditary Cohesive Subgraphs Enumeration on Bipartite Graphs: The Power of Pivot-based Approaches (SIGMOD, 2023)
- 14. Efficient Personalized PageRank Computation: A Spanning Forests Sampling Based Approach (SIGMOD, 2023)
Research Experience
- Pursuing a PhD at the School of Computer Science and Technology, Beijing Institute of Technology, focusing on spectral graph theory and numerical linear algebra.
Education
- PhD student at the School of Computer Science and Technology, Beijing Institute of Technology, advised by Prof. Ronghua Li; Received bachelor's degree from Beijing Institute of Technology in 2020.
Background
- Research Interests: spectral graph theory, numerical linear algebra and their applications in graph data management, graph machine learning. Interested in implementing theoretically fast algorithms.
Miscellany
- Expected to graduate in June 2026, currently on the job market.
