5 citations · 12 across the 9 of their papers we have counts for
11 papers
DualClusterNet: Personalized Pattern-Space Composition for Next-POI Recommendation
Ziyang Su, Guanjie Zheng, Jilin Hu +1
Next point-of-interest (POI) recommendation supports personalized place discovery on location-based Web platforms. Mobility routines can recur across users while being realized at…
CoGSeq-LTV: Scalable Missing-Aware Graph-Sequence Modeling for Multi-Business Transaction Analytics
Ziyang Su, Yao Zhou, Kelin Liu +2
Large-scale super-apps generate heterogeneous transaction logs in which users interact with multiple businesses, item identifiers are only partially available, and future value tar…
How Much Can Time-related Features Enhance Time Series Forecasting?
Chaolv Zeng, Yuan Tian, Guanjie Zheng +1
Recent advancements in long-term time series forecasting (LTSF) have primarily focused on capturing cross-time and cross-variate (channel) dependencies within historical data. Howe…
UMGAD: Unsupervised Multiplex Graph Anomaly Detection
Xiang Li, Jianpeng Qi, Zhongying Zhao +4
Graph anomaly detection (GAD) is a critical task in graph machine learning, with the primary objective of identifying anomalous nodes that deviate significantly from the majority.…
Frequency Enhanced Pre-training for Cross-city Few-shot Traffic Forecasting
Zhanyu Liu, Jianrong Ding, Guanjie Zheng
The field of Intelligent Transportation Systems (ITS) relies on accurate traffic forecasting to enable various downstream applications. However, developing cities often face challe…
MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data
Jianping Zhou, Bin Lu, Zhanyu Liu +6
Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missin…
CMamba: Channel Correlation Enhanced State Space Models for Multivariate Time Series Forecasting
Chaolv Zeng, Zhanyu Liu, Guanjie Zheng +1
Recent advancements in multivariate time series forecasting have been propelled by Linear-based, Transformer-based, and Convolution-based models, with Transformer-based architectur…
CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
Jianrong Ding, Zhanyu Liu, Guanjie Zheng +2
Dataset condensation is a newborn technique that generates a small dataset that can be used in training deep neural networks to lower training costs. The objective of dataset conde…
Graph Data Condensation via Self-expressive Graph Structure Reconstruction
Zhanyu Liu, Chaolv Zeng, Guanjie Zheng
With the increasing demands of training graph neural networks (GNNs) on large-scale graphs, graph data condensation has emerged as a critical technique to relieve the storage and t…
Dataset Condensation for Time Series Classification via Dual Domain Matching
Zhanyu Liu, Ke Hao, Guanjie Zheng +1
Time series data has been demonstrated to be crucial in various research fields. The management of large quantities of time series data presents challenges in terms of deep learnin…
Multi-scale Traffic Pattern Bank for Cross-city Few-shot Traffic Forecasting
Zhanyu Liu, Guanjie Zheng, Yanwei Yu
Traffic forecasting is crucial for intelligent transportation systems (ITS), aiding in efficient resource allocation and effective traffic control. However, its effectiveness often…