Publications & Projects
Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
SUREL is a novel framework for scalable Subgraph Representation Learning by co-designing the learning algorithm and its system support. It adopts walk-based decomposition of subgraphs and reuses the walks to form subgraphs, which substantially reduces the redundancy of subgraph extraction and supports parallel computation.
Yin, H., Zhang, M., Wang, Y., Wang, J., & Li, P. (2022). Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning. To appear in Proceedings of the VLDB Endowment 15 (11). [PDF(arXiv), Code].
Revisiting Graph Neural Networks and Distance Encoding From a Practical View
Reviewed GNNs and Distance Encoding (DE) technique for graph representation learning: 1) categorize the labels for node classification tasks into two types: community type and structure type. 2) investigate how DE makes GNNs fit for tasks, such as node classification and link prediction. 3) design eight variants to identify the mechanism that GNNs adopt to predict two types of node labels under different graph settings.
Yin, H., Wang, Y., & Li, P. (2020). Revisiting Graph Neural Networks and Distance Encoding From a Practical View. In Proceedings of the 35th AAAI Conference on Artificial Intelligence DLG Workshop. [Link, PDF(arXiv), Code].
Graph-Structured Sequence Modeling through Spatio-Temporal U-Network
Designed a novel multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), for graph-structured time series modeling. In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) and the unpooling (ST-Unpool). To better localize the representation from the input, higher-level features retrieved from the pooling part are concatenated with the upsampled output. The final output of ST-UNet can be utilized for predicting node attributes or the entire graph in the next few time steps.
Yu, B.*, Yin, H.*, & Zhu, Z. (2019). ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling. arXiv preprint arXiv:1903.05631. [PDF].
Machine Learning Attacks to Location Privacy
Developed a model of adversary that uses machine learning to learn about the geographical data collected from users of location-based services and the corresponding privacy mechanism, and then performs its attack on users’ location privacy.
Project finished as a member of the international internship program at École Polytechnique & Inria.
Traffic Prediction with Deep Spatial Temporal Neural Nets
Designed a fully integrated convolutional neural network to precisely model the topology of the road network and then accurately forecast the future traffic condition (speed, flow or volume) of the network through space-time series in the mid-and-long term.
Yu, B.*, Yin, H.*, & Zhu, Z. (2018). Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (pp. 3634-3640). [Link, PDF (arXiv), Code], * indicates equal contribution.
Neural Artist - Style Transfer in Short Videos
Applied Conditional GANs and Fast Style Transfer to convert short videos into customized styles (e.g. Van Gogh, The Starry Night) through DNN-based texture abstraction and redesigned loss function to balance and minimize the flicker between rendering frames.
This project is awarded 'the Most Technical Difficulty Award' at Schlumberger HackPKU 2017.
Hotspot Prediction Based on Temporal Trajectory and Social Attributes
Proposed a location-vector embedding framework (Loc2vec) to predict the geographic hotspot in a certain area and explore its semantic meaning based on temporal trajectories, which are linked in a chronological order through users’ check-ins gathered from location-based social networks.