Siqiang Luo
Associate Professor
Works on data management, data mining and machine learning, including graph learning and data-intensive algorithms.
Siqiang Luo's lab at NTU works on data management & big data, graph mining & networks and graph learning. 46 papers since 2023, 36 at top venues, mostly VLDB and SIGMOD. 9 PhD students and 1 master's student. 7 alumni, most often to ByteDance and Xiaomi.
At a glance
- papers, 2023–2026
- 12/ year
- at T1 venues
- 78%
- PhD students
- 9
- MS students
- 1
- alumni listed
- 7
Papers by yearfrom the lab homepage, 2023–2026
- 15 papers2026 · T1 9 · collecting
- 12 papers2025 · T1 12
- 11 papers2024 · T1 8
- 8 papers2023 · T1 7
T1 venuesOtherVenues: VLDB, SIGMOD, ICDE, NeurIPS, TKDE
Research focuspaper titles that use each topic's words
- Graph learning25 papers
- Graph mining & networks20 papers
- Data management & big data2 papers
- data1 paper
- data-intensive0 papers
Paper list
From the lab homepage.
202615 papers
- SIGMOD (T1)A Comprehensive Benchmark on Spectral GNNs: The lmpact on Efficiency, Memory, and Effectiveness.
- EDBTAdCache: Adaptive Cache Management with Admission Control for LSM-tree Key-Value Stores.
- VLDB (T1)ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads.
- SIGMOD (T1)Are Learned DBMS Components Robust to Workload Drift?
- SIGKDDCausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting.
- ICDE (T1)DIFFCOM: Conditional Discrete Diffusion Model for Community Search.
- EDBTEvaluating Learned Indexes in LSM-tree Systems: Benchmarks, Insights and Design Choices.
- ACM Computing SurveyGraph-Based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
- ICLR (T1)Modality-free Graph In-context Alignment.
- PODSNear-Optimality for Single-Source Personalized PageRank.
- SIGMOD (T1)RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage
- VLDB (T1)Scalable Graph Kernel Query with Error Guarantees.
202512 papers
- VLDB (T1)Are Joins over LSM-trees Ready: Take RocksDB as an Example.
- SIGMOD (T1)Aster: Enhancing LSM-structures for Scalable Graph Database.
- SIGMOD (T1)CAMAL: Optimizing LSM-trees via Active Learning.
- ICDE (T1)Finding Near-Optimal Maximum Set of Disjoint k-Cliques in Real-World Social Networks.
- TKDE (T1)Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning.
- SIGMOD (T1)How to Grow an LSM-tree? Towards Bridging The Gap Between Theory and Practice.
- NeurIPS (T1)HubGT: Fast Graph Transformers with Decoupled Hierarchy Labelling.
- ICDE (T1)OMeGa: Boosting Large-scale Graph Embeddings with Heterogeneous Memory Processing.
- ICDE (T1)SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation.
- NeurIPS (T1)Towards Graph Foundation Models: Training on Knowledge Graphs Enables Transferability to General Graphs.
- ICML (T1)Unifews: You Need Fewer Operations for Efficient Graph Neural Networks.
- SIGMOD (T1)VEGA: An Active-tuning Learned Index with Group-Wise Learning Granularity.
202411 papers
- VLDB (T1)BIRD: Efficient Approximation of Bidirectional Hidden Personalized PageRank.
- VLDB (T1)GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs.
- SIGMOD (T1)Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads.
- Findings of ACLLLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs.
- IJCAI (T1)Massively Parallel Single-Source SimRanks in o(log n) Rounds.
- VLDB (T1)Oasis: An Optimal Disjoint Segmented Learned Range Filter.
- ICDE (T1)Personalized PageRanks over Dynamic Graphs -- The Case for Optimizing Quality of Service.
- SIGKDDResurrecting Label Propagation for Graphs with Heterophily and Label Noise.
- SIGMOD (T1)Structural Designs Meet Optimality: Exploring Optimized LSM-tree Structures in A Colossal Configuration Space.
- VLDB (T1)TIGER: Training Inductive Graph Neural Network for Large-scale Knowledge Graph Reasoning.
- SIGKDDTopology-monitorable Contrastive Learning on Dynamic Graphs.
20238 papers
- ICDE (T1)COCLEP: Contrastive Learning-based Semi-Supervised Community Search.
- VLDB (T1)Distributed Graph Embedding with Information-Oriented Random Walks.
- VLDB (T1)Effective and Efficient Route Planning Using Historical Trajectories on Road Networks.
- TKDE (T1)Efficient Community Search in Edge-Attributed Graphs.
- ICDE (T1)Example Searcher: A Spatial Query System via Example.
- NeurIPS (T1)LD2: Scalable Heterophilous Graph Neural Network with Decoupled Embedding.
- EDBTMulti-Task Processing in Vertex-Centric Graph Algorithms: Evaluations and Insights.
- TKDE (T1)Seesaw Counting Filter: A Dynamic Filtering Framework for Vulnerable Negative Keys.
Where alumni went7 listed, 50% to industry
Industry 3Academia 3
Most often to
- ByteDance
- Xiaomi
Also to
- Shanghai Jiao Tong University
Apply to NTUFall 2027
Not announced yet
Computing and Data Science PhD
- Round
- Main
- Opens
- —
- GRE
- not required
- English
- TOEFL/IELTS
- Fee
- See program page
Not yet confirmed for this intake.
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