Long Yuan
Scholar

Long Yuan

Google Scholar ID: xKq4VQUAAAAJ
Wuhan University of Technology
DatabasesGraph MiningData Mining
Citations & Impact
All-time
Citations
1,411
 
H-index
19
 
i10-index
29
 
Publications
20
 
Co-authors
29
list available
Resume (English only)
Academic Achievements
  • Triangle Counting in Hypergraph Streams: A Complete and Practical Approach, SIGMOD, 2026 (corresponding author)
  • Revisiting Graph Analytics Benchmarks, SIGMOD, 2025 (corresponding author)
  • GPUSCAN++: Efficient Structural Graph Clustering on GPUs, TPDS, 2025
  • HINSCAN: Efficient Structural Graph Clustering over Heterogeneous Information Networks, ICDE, 2025
  • Efficient Maximum Balanced k-biplex Search over Bipartite Graphs, ICDE, 2025
  • Efficient k-Truss Breaking and Minimization, ICDE, 2025
  • Most Probable Maximum Weighted Butterfly Search, ICDE, 2025
  • Simpler is More: Efficient Top-K Nearest Neighbors Search on Large Road Networks, VLDB, 2025 (corresponding author)
  • I/O Efficient Label-Constrained Reachability Queries in Large Graphs, VLDB, 2024
  • Batch Hop-constrained ST Simple Path Query Processing in Large Graphs, ICDE, 2024
  • Parallel Contraction Hierarchies Construction on Road Networks, TKDE, 2024 (corresponding author)
  • A Survey of Distributed Graph Algorithms on Massive Graphs, CSUR, 2024 (corresponding author)
Research Experience
  • Was a Professor at Nanjing University of Science and Technology, China; worked as a Research Associate in the Data and Knowledge Research Group at the University of New South Wales (UNSW), Australia, from 2017 to 2025.
Education
  • Obtained his PhD in Computer Science and Engineering from UNSW in 2017, supervised by Prof. Xuemin Lin and co-supervised by Prof. Lu Qin; obtained his master’s/bachelor’s degree from Sichuan University in 2010/2013, both in Computer Science.
Background
  • Currently a Professor at the school of Computer Science and Artificial Intelligence, Wuhan University of Technology. His research interests lie in intelligent big data analytics, especially for graph/network data.
Miscellany
  • Looking for self-motivated students to join his group.