Jul 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 49 references
Computer Science
TL;DR
SpectraDyN is introduced, a unified framework integrating spectral-temporal modeling that incorporates a wavelet transform layer to achieve multi-resolution decomposition, enabling the distinct capture of both long-term community evolution and short-term event-driven interactions.
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
A novel theoretical model, namely the multiway autoregressive (MARS) model, which characterizes multiple evolutionary paths by capturing dependencies within and across two core factors underlying diverse evolutionary mechanisms is proposed, which develops a general DGNN framework, a multiway autoregressive network (MAN...
Ping He, Xiao-hua Xu· IEEE Transactions on Neural...· 0 citations
A novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies and robustly captures the nonlinear interplay between mobility and sociality.
Liang Chen, Xiang Li, Gui-Yuan Jiang et al.· Proceedings of the Thirty-Fi...· 0 citations
The proposed method, TRicci, extends classical Forman-Ricci curvature to directed weighted temporal graphs by capturing structural support, temporal recency, and local interaction competition and suggests that temporal curvature can serve as a principled basis for scalable temporal graph learning by preserving predicti...
Poupak Azad, C. Akcora, Kiarash Shamsi· 0 citations
The framework gives a nonparametric baseline for dynamic network analysis with explicit convergence guarantees and establishes nonparametric convergence rates in both block-model and Holder-smooth regimes.
A novel fractal–causal temporal graph embedding (FCTGE) framework is proposed, which unifies multiscale fractal temporal dynamics, time-lagged causal interactions, and variable-level statistical characteristics within a unified graph-based representation learning paradigm.
Ying Zhang, Rong-Cong Wang, Yu-Rong Liu et al.· Physica Scripta· 0 citations
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