Xiangyang GOU (苟向阳)
Assistant Professor
Wangxuan Institute of Computer Technology,
Peking University,No. 128 Zhongguancun North Street,
Haidian District, Beijing, 100871, P. R. China
Email: gxy1995 (at) pku.edu.cn
Xiangyang Gou is an Assistant Professor at Peking University.
He received his Ph.D. in Computer Application Technology from the School of Intelligence Science and Technology at Peking University in 2022. From 2023 to 2026, he conducted postdoctoral research at The Chinese University of Hong Kong and the University of New South Wales, Australia.
In 2026, he joined the Wangxuan Institute of Computer Technology at Peking University. His current research focuses on the management and mining of streaming data, time-series data, and graph data.
Positions Opening:
Our team (PKUMOD) are looking for excellent applicants: Research Scientist (Post doc position), Research Assistant Professor, Tenure-track Assistant/Associate Professor. Research directions, including but not limited to the following directions:
1) Database, especially graph database systems, graph query processing, etc.
2) Graph computing, including graph analysis, graph data mining, graph machine learning, and graph algorithm and graph computing-oriented hardware accelerator (GPU, FGPA, etc.).
3) Knowledge graph, including knowledge graph construction, quality control, and knowledge graph storage, retrieval, mining and application.
Please send a detailed CV to me including no more than 3 representative papers
News
Apr 12,2026，start work as an Assistant Professor at the Wangxuan Institute of Computer Technology, Peking University
Graph Data Management
With the rapid growth of social networks, knowledge graphs, and other network-structured data, graph data has become increasingly central to applications such as big data analytics and intelligent recommendation systems. Unlike traditional relational databases, which represent data through tables and join operations, graph data models complex relationships more naturally through vertices and edges. While this flexible schema greatly facilitates the integration of multi-source heterogeneous data, it also introduces significant challenges in graph storage, query optimization, and distributed processing. Our research primarily focuses on graph query processing, particularly continuous queries over streaming graphs formed by the integration of graph and streaming data paradigms, including subgraph matching and the optimization of regular path queries. In addition, we are also interested in approximate storage techniques for streaming graphs, such as summarization and sampling.
Streaming and Time-Series Data Management
With the continuous emergence of applications such as the Internet of Things (IoT), financial transactions, and log monitoring, streaming and time-series data management has become a key technology in the field of big data processing. Unlike traditional databases, which are designed for static and persistent datasets, streaming data is characterized by continuous, unbounded, and high-velocity arrivals, often accompanied by timestamps that indicate when data is generated or received. After being processed in real time by stream processing systems, such data is often persistently stored in the form of time-series data, making streaming data and time-series data closely interconnected in many practical scenarios. Beyond the intersection of graph and streaming data in streaming graphs discussed earlier, our research also focuses on broader streaming data management and query processing, including summary structures for streaming data and multi-way join queries over relational streams. In the area of time-series data, my current research primarily centers on large-scale time-series data cleaning, storage, view management, and intelligent analytics.
Collaborating Ph.D. Students : Linglin Yang, Lisheng Cao, Xinyi Ye，Chunshan Zhao
Undergraduate Research Intern: Miduo Yu
Xiangyang Gou, Lei Zou, Jeffrey Xu Yu, Wenjie Zhang. An Extensive Experimental Study of Indexes in Continuous Subgraph Matching: [Experiments & Analysis]. Proceedings of the ACM on Management of Data （PACMMOD，SIGMOD 2026）, 2026, 4(1): 1-26
Linglin Yang, Xunbin Su, Lei Zou, Xiangyang Gou and Yinnian Lin， CEMR: An Effective Subgraph Matching Algorithm with Redundant Extension Elimination，The International Conference on Very Large Data Bases （VLDB 2026）， To appear.
LiSheng Cao, Xiangyang Gou*, Lei Zou, Wenjie Zhang: MAVIS: Materialized View for Subgraph Matching. Proceedings of the ACM on Management of Data （PACMMOD，SIGMOD 2026）, 2025，3(6), 1-26.
Xinyi Ye, Xiangyang Gou*, Lei Zou, Wenjie Zhang: AJOSC: Adaptive join order selection for continuous queries on data streams. Proceedings of the ACM on Management of Data （PACMMOD，SIGMOD 2025）, 2025, 3(3): 1-27
苟向阳，邹磊，于旭：高精度滑动窗口模型下的图流三角形近似计数算法.软件学报,2025,36(9):4349-4372
Xiangyang Gou, Xinyi Ye, Lei Zou, Jeffrey Xu Yu: LM-SRPQ: Efficiently Answering Regular Path Query in Streaming Graphs. Proceedings of the VLDB Endowment (PVLDB，VLDB 2024), 2024, 17(5): 1047-1059.
Xiangyang Gou, Lei Zou: Sliding window-based approximate triangle counting with bounded memory usage." The VLDB Journal (VLDBJ), 2023, 32(5): 1087-1110.
Fan Zhang, Xiangyang Gou, Lei Zou: Top-k heavy weight triangles listing on graph stream. World Wide Web: Internet and Web Information Systems (WWW Journal), 2023, 26(4): 1827-1851.
Xiangyang Gou, Lei Zou, Chenxingyu Zhao, Tong Yang: Graph Stream Sketch: Summarizing Graph Streams with High Speed and Accuracy. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022, 35(6): 5901-5914.
Xiangyang Gou, Yinda Zhang, Zhoujing Hu, Long He, Ke Wang, Xilai Liu, Tong Yang, Yi Wang, Bin Cui: A Sketch Framework for Approximate Data Stream Processing in Sliding Windows. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022, 32(5), 4411-4424.
Xiangyang Gou, Lei Zou: Sliding Window-based Approximate Triangle Counting over Streaming Graphs with Duplicate Edges. The International Conference on Management of Data（SIGMOD） Xi’an, China, 2021-06-20 至 2021-06-25.
Xiangyang Gou, Long He, Yinda Zhang, Ke Wang, Xilai Liu, Tong Yang, Yi Wang, Bin Cui: Sliding sketches: A framework using time zones for data stream processing in sliding windows. The International Conference on Knowledge Discovery & Data Mining (SIGKDD), virtual conference, 2020-08-23 至 2020-08-27.
Fan Zhang, Lei Zou, Li Zeng, Xiangyang Gou: Dolha-an efficient and exact data structure for streaming graphs. World Wide Web: Internet and Web Information Systems (WWW Journal), 2020, 23(2): 873-903.
Xiangyang Gou, Lei Zou, Chenxingyu Zhao, Tong Yang: Fast and accurate graph stream summarization. The International Conference on Data Engineering (ICDE)，Macau SAR, China, 2019-04-08 至 2019-04-11.
Xiangyang Gou, Chenxingyu Zhao, Tong Yang, Lei Zou, Yang Zhou, Yibo Yan, Xiaoming Li, Bin Cui: Single hash: Use one hash function to build faster hash based data structures. The International Conference on Big Data and Smart Computing (BigComp). 2018-01-15 至 2018-01-17.
PC Member：ICDE 2025, 2027，PAKDD 2026，WISE 2021，2023，2024，2025
Reviewer: TKDE
Awards：
CCF Outstanding Doctoral Dissertation Award (2024)
CCF TCDB Outstanding Doctoral Dissertation Award (2024)
Lei Zou, Xiangyang Gou, and Yu Liu: Certified Applied Mathematics Implementation Achievement by CSIAM: Graph Pattern Matching Methods and Applications in Embedded Low-Resource Environments (2023)
