Aug 2026· Data mining and knowledge discovery· Vol 40· 0 citations· 39 references
Computer Science
TL;DR
A novel evolutionary incremental approach called Event Adaptive Incremental Learning Non-Negative Matrix Factorization (EA-iNMF) is proposed, which integrates event-adaptive learning with an incremental update mechanism to efficiently identify optimal time spans corresponding to peak user engagement.
This work formulate dynamic community detection over observed node-time instances, where each node-time instance in the temporal interaction stream is assigned a cluster label and proposes a diffusion-guided contrastive learning framework that uses a local temporal diffusion affinity matrix to construct positive and ne...
Pei-Jie Zhong, Raul J. Mondragón, Richard G. Clegg· 0 citations
Auto-ibDLM is proposed, a network-driven deep learning framework that represents events as dynamic interaction networks and predicts public event evolution through participant growth forecasting and consistently outperforms representative state-of-the-art methods in both forecasting accuracy and generalization capabili...
Jie Wei, Yue Liu, Xiaochuan Tang et al.· 0 citations
This work proposes a unified mathematical framework capable of capturing varying degrees of complexity across temporal graphs that is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics.
Mohammad Ostadmohammadi, S. Kazemi, H. R. Rabiee· arXiv.org· 0 citations
This paper proposes an attention-assisted detection method for abnormal events in social networks that constructs a social network temporal graph model that reveals hidden anomalous topics and constructs scenarios as events evolve, thereby improving the interpretability of anomalous events in social networks.
Qi Zhang, Jian-Xin Zhang, Shen-Tao Gao et al.· Journal of King Saud Univers...· 0 citations
THGNN-MRD, a temporal heterogeneous graph neural network for multimodal rumor detection, is proposed, suggesting that modeling rumors as evolving multimodal social events provides a principled and effective solution for trustworthy rumor detection.
