Research Interests
My research interests are graph algorithms, with a particular focus on the design of scalable algorithms for large graphs.
My previous work primarily centers on sublinear-time algorithms for random-walk probability estimation. I am also interested in local clustering and graph neural networks.
I am on the job market! Please email me if you are interested. I am also happy to give talks on my research in related seminars.
|
Publications
Note: Papers marked with ** use alphabetic ordering of authors, following the convention of theoretical computer science.
**Optimal Dynamic Parameterized Subset Sampling.
Junhao Gan,
Seeun William Umboh,
Hanzhi Wang,
Anthony Wirth,
Zhuo Zhang
PODS, 2025
We study the fundamental subset sampling problem in a general dynamic setting and achieve optimal complexity results for both query and update time simultaneously.
|
Revisiting Local PageRank Estimation on Undirected Graphs: Simple and Optimal.
Hanzhi Wang
KDD, 2024
Technical Report
/
slides
We establish matching upper and lower bounds for the worst-case computational complexity of single-node PageRank computation on undirected graphs, using surprisingly simple algorithms and analysis.
|
Efficient Algorithms for Personalized PageRank Computation: A Survey.
Mingji Yang,
Hanzhi Wang,
Zhewei Wei,
Sibo Wang,
Ji-Rong Wen
TKDE, 2024
Technical Report
We recap several frequently used techniques for Personalized PageRank (PPR) computation and conduct a comprehensive survey of various recent PPR algorithms from an algorithmic perspective.
|
**Revisiting Local Computation of PageRank: Simple and Optimal.
Hanzhi Wang,
Zhewei Wei,
Ji-Rong Wen,
Mingji Yang
STOC, 2024
Technical Report
/
slides
We establish matching upper and lower bounds for the worst-case computational complexity of single-node PageRank computation on directed graphs. Additionally, for the celebrated ApproxContributions algorithm (also known as Backward Push or Local Push, proposed by Andersen, Borgs, Chayes, Hopcroft, Mirrokni, and Teng in WAW 2007), we upper bound its worst-case computational complexity on directed graphs and prove its optimality by establishing a matching lower bound.
|
Estimating Single-Node PageRank in O(min(dt, m1/2)) Time.
Hanzhi Wang,
Zhewei Wei
VLDB, 2023
Technical Report
/
code
/
slides
/
poster
For the problem of estimating a single node's PageRank score in an undirected graph, we tighten the upper bound for query time complexity from O((ndt)1/2) to O(min(dt, m1/2)) (omitting logarithmic factors). Here n and m denote the number of nodes and edges in the given graph, respectively. dt denotes the degree of the given target node t.
|
Optimal Dynamic Subset Sampling: Theory and Applications.
Lu Yi,
Hanzhi Wang,
Zhewei Wei
KDD, 2023
Technical Report
/
slides
/
poster
We study the fundamental problem of sampling indepedent events (called subset sampling) in a dynamic setting. We present the ODSS algorithm, which is the first to achieve the optimal time complexity per sample and per update simultaneously.
|
Edge-based Local Push for Personalized PageRank.
Hanzhi Wang,
Zhewei Wei,
Junhao Gan,
Ye Yuan,
Xiaoyong Du,
Ji-Rong Wen
VLDB, 2022
Technical Report
/
code
/
slides
The state-of-the-art algorithm, LocalPush, for Personalized PageRank computation can be rather inefficient on weighted graphs. In this paper, we propose an Edge-based Push Method (EdgePush), which decomposes the push operation of LocalPush into separate edge-based push operations and achieves superior query efficiency over LocalPush on weighted graphs.
|
Instant Graph Neural Networks for Dynamic Graphs.
Yanping Zheng,
Hanzhi Wang,
Zhewei Wei,
Jiajun Liu,
Sibo Wang
KDD, 2022
Technical Report
/
code
We propose Instant Graph Neural Network (InstantGNN), an incremental computation approach to support instant updates of graph representation results on dynamic graphs.
|
Approximate Graph Propagation
Hanzhi Wang,
Mingguo He,
Zhewei Wei,
Sibo Wang,
Ye Yuan,
Xiaoyong Du,
Ji-Rong Wen
KDD, 2021
Technical Report
/
code
/
slides
/
poster
We propose Approximate Graph Propagation (AGP), a unified randomized algorithm that computes various proximity queries and GNN feature propagation in almost optimal time complexity.
|
ExactSim: benchmarking single-source SimRank algorithms with high-precision ground truths
Hanzhi Wang,
Zhewei Wei,
Yu Liu,
Ye Yuan,
Xiaoyong Du,
Ji-Rong Wen
VLDB Journal, 2021
This work is an extended version of the conference paper "Exact Single-Source SimRank Computation on Large Graphs" accepted by SIGMOD 2020. With the ground truths computed by ExactSim, we conduct the first experimental study of the accuracy/cost trade-offs of existing approximate SimRank algorithms on large graphs.
|
Personalized PageRank to a Target Node, Revisited
Hanzhi Wang,
Zhewei Wei,
Junhao Gan,
Sibo Wang,
Zengfeng Huang
KDD, 2020
Technical Report
/
code
/
slides
We propose Randomized Backward Search (RBS), a novel algorithm that answers approximate single-target personalized PageRank queries (also known as PageRank contribution queries) with near optimal time complexity.
|
Exact Single-Source SimRank Computation on Large Graphs
Hanzhi Wang,
Zhewei Wei,
Ye Yuan,
Xiaoyong Du,
Ji-Rong Wen
SIGMOD, 2020
Technical Report
/
code
/
slides
We propose ExactSim, the first algorithm that enables probabilistic exact single-source SimRank queries on large graphs. ExactSim can provide the ground truth with a precision up to 7 decimal places for single-source SimRank queries on large graphs within a reasonable query time.
|
Patents
A Friend Recommendation Method based on Personalized PageRank
(一种基于个性化佩奇排名的好友推荐方法)
Zhewei Wei,
Hanzhi Wang,
Junhao Gan,
Sibo Wang,
Zengfeng Huang
Chinese National Patent, ZL202010850777.2
A Friend Recommendation Method based on the Exact Results of Single-Source SimRank
(基于单源SimRank精确解的好友推荐方法)
Zhewei Wei,
Hanzhi Wang,
Ye Yuan,
Ji-Rong Wen,
Xiaoyong Du
Chinese National Patent, ZL202010536506.X
A Collaborative Filtering Recommendation Method based on Single-Source SimRank Results
(基于单源SimRank的协同过滤推荐方法)
Zhewei Wei,
Xiaodong He,
Hanzhi Wang,
Xiaokui Xiao,
Sibo Wang,
Yu Liu,
Xiaoyong Du
Chinese National Patent, ZL201910577524.X
|
Honors and Awards
Microsoft Research Asia Fellowship, 2022 (awarded to 12 PhD students in the Asia-Pacific region).
Baidu Scholarship, 2021 (awarded to 10 graduate students worldwide).
Wu Yuzhang Scholarship, 2024 (awarded to 10 students in Renmin University of China).
National Scholarship, 2021.
Beijing Outstanding Graduate, 2024.
Certified Excellence in Reviewing for KDD 2023.
The Outstanding Innovative Talents Cultivation Funded Programs of Renmin University of China, 2020.
China National Petroleum Corporation (CNPC) Scholarship, 2020.
|
Academic Services
PC member/Reviewer:
IEEE International Conference on Data Engineering (ICDE), 2025
International Conference on Knowledge Discovery and Data Mining (KDD), 2023/2024/2025
International Conference on Learning Representations (ICLR), 2024/2025
ACM International Conference on Web Search and Data Mining (WSDM), 2023/2024/2025
ACM International Conference on Information and Knowledge Management (CIKM), 2024
Annual Conference on Neural Information Processing Systems (NeurIPS), 2023
SIAM International Conference on Data Mining (SDM), 2024
Australasian Database Conference (ADC), 2023
IEEE Communications Magazine
Information Retrieval Journal
International Conference on Machine Learning (ICML), 2022
|
Invited Talks
Approximate Graph Propagation.
[slide]
AMiner
Online, Oct. 2021
Efficient Computation of Random Walk and the Applications in Graph Representation Learning.
[slide]
DataFun
Online, Aug. 2021
A Near Optimal Algorithm for Single-Target Personalized PageRank.
[slide]
The 9th National Social Media Processing Conference (SMP, 2020)
Online, Sept. 2020
|
Last update: Sept. 2024      Template
|
|