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pro vyhledávání: '"dynamic link"'
Modern communication networks feature local fast failover mechanisms in the data plane, to swiftly respond to link failures with pre-installed rerouting rules. This paper explores resilient routing meant to tolerate $\leq k$ simultaneous link failure
Externí odkaz:
http://arxiv.org/abs/2410.02021
Evolving networks are complex data structures that emerge in a wide range of systems in science and engineering. Learning expressive representations for such networks that encode their structural connectivity and temporal evolution is essential for d
Externí odkaz:
http://arxiv.org/abs/2408.12753
Publikováno v:
Complex Networks & Their Applications XIII: Proceedings of the 13th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2024)
The prediction of both the existence and weight of network links at future time points is essential as complex networks evolve over time. Traditional methods, such as vector autoregression and factor models, have been applied to small, dense networks
Externí odkaz:
http://arxiv.org/abs/2409.08718
Structure encoding has proven to be the key feature to distinguishing links in a graph. However, Structure encoding in the temporal graph keeps changing as the graph evolves, repeatedly computing such features can be time-consuming due to the high-or
Externí odkaz:
http://arxiv.org/abs/2407.20871
Publikováno v:
Appl. Sci. 2024, 14(8), 3516
Dynamic Link Prediction (DLP) addresses the prediction of future links in evolving networks. However, accurately portraying the performance of DLP algorithms poses challenges that might impede progress in the field. Importantly, common evaluation pip
Externí odkaz:
http://arxiv.org/abs/2405.17182
Autor:
Osuma, Godswill1 (AUTHOR) gosuma@uj.ac.za, Nzimande, Ntokozo Patrick1 (AUTHOR)
Publikováno v:
Economies. Nov2024, Vol. 12 Issue 11, p283. 18p.
Akademický článek
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Autor:
Çolak, Olcay1 olcay.colak@usak.edu.tr, Bölükbaşi, Ömer Faruk2
Publikováno v:
Economic Horizons / Ekonomski Horizonti. Sep-Dec2024, Vol. 26 Issue 3, p237-252. 16p.
This work proposes DyExpert, a dynamic graph model for cross-domain link prediction. It can explicitly model historical evolving processes to learn the evolution pattern of a specific downstream graph and subsequently make pattern-specific link predi
Externí odkaz:
http://arxiv.org/abs/2402.02168
Modelling temporal networks for dynamic link prediction of new nodes has many real-world applications, such as providing relevant item recommendations to new customers in recommender systems and suggesting appropriate posts to new users on social pla
Externí odkaz:
http://arxiv.org/abs/2310.09787