1 citations · 1 across the 4 of their papers we have counts for
5 papers
DISRL: Dual-Scale Incremental Semi-Supervised Reinforcement Learning for Dynamic Community Tracking
Tianming Zhang, Jinxu Wang, Yixuan Zhang +4
Dynamic local community detection tracks the evolving community of designated seed nodes across graph snapshots. However, it faces two critical challenges: temporal supervision unc…
MDTE: Minority-Aware Diffusion over Temporal Edge Events for Imbalanced Node Classification
Zhou Zelong, Zhang Tianming, Yang Zhengyi +4
Class-imbalanced node classification on temporal graphs is challenging because majority-dominated temporal propagation progressively assimilates minority representations, while con…
EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction
Jiaxing Wang, Kaitao Chen, Zhubin Han +4
Next activity prediction helps service-oriented processes anticipate upcoming steps before delays, exceptions, or service-level risks occur. Most existing methods assume classical…
RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes
Jiaxing Wang, Yifeng Yu, Jiahan Song +3
Next activity prediction represents a fundamental challenge for optimizing business processes in service-oriented architectures such as microservices environments, distributed ente…
CLGNN: A Contrastive Learning-based GNN Model for Betweenness Centrality Prediction on Temporal Graphs
Tianming Zhang, Renbo Zhang, Zhengyi Yang +3
Temporal Betweenness Centrality (TBC) measures how often a node appears on optimal temporal paths, reflecting its importance in temporal networks. However, exact computation is hig…