Titlebook: Complex Networks & Their Applications XII; Proceedings of The T Hocine Cherifi,Luis M. Rocha,Murat Donduran Conference proceedings 2024 The

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E-MIGAN: Tackling Cold-Start Challenges in Recommender Systems pairwise interactions, and iii) A Content-Based Embedding model, which overcomes the cold start issue. The empirical study on real-world datasets proves that E-MIGAN achieves state-of-the-art performance, demonstrating its effectiveness in capturing complex interactions in graph-structured data.

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A Framework for Empirically Evaluating Pretrained Link Prediction Models on. Moreover, we systematically assessed the relationship between topological similarity and performance difference of pretrained models and a model trained on the same data. We find that similar network pairs in terms of clustering coefficient, and to a lesser extent degree assortativity and gini

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Efficient Approach for Patient Monitoring: ML-Enabled Framework with Smart Connected Systemsg range detection techniques supported by Bluetooth master-slave communication. Computations are performed in the backend such that the alerts are notified based on the conditions assigned to the respective patients. In case of emergency, we can reliably predict the condition of a patient with impro

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https://doi.org/10.1007/978-3-658-16596-3s in data interpretation. This research lays the groundwork for integrating these methodologies, indicating vast potential for their wider application and opening up promising avenues for future exploration.

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https://doi.org/10.1007/978-3-658-12285-0 pairwise interactions, and iii) A Content-Based Embedding model, which overcomes the cold start issue. The empirical study on real-world datasets proves that E-MIGAN achieves state-of-the-art performance, demonstrating its effectiveness in capturing complex interactions in graph-structured data.

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