Zobrazeno 1 - 10
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pro vyhledávání: '"Graph convolutional networks"'
Graph convolutional networks (GCNs) have emerged as powerful models for graph learning tasks, exhibiting promising performance in various domains. While their empirical success is evident, there is a growing need to understand their essential ability
Externí odkaz:
http://arxiv.org/abs/2410.08473
Autor:
Ancelotti, Amy, Liason, Claudia
This paper reviews the applications of Graph Neural Networks (GNNs), Graph Convolutional Networks (GCNs), and Convolutional Neural Networks (CNNs) in blockchain technology. As the complexity and adoption of blockchain networks continue to grow, tradi
Externí odkaz:
http://arxiv.org/abs/2410.00875
Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, in part, to their flexibility in specification of network propagation rules. These propagation rules are often constructed as polynomial f
Externí odkaz:
http://arxiv.org/abs/2409.04813
In this work we investigate an observation made by Kipf \& Welling, who suggested that untrained GCNs can generate meaningful node embeddings. In particular, we investigate the effect of training only a single layer of a GCN, while keeping the rest o
Externí odkaz:
http://arxiv.org/abs/2410.13416
Text classification is a quintessential and practical problem in natural language processing with applications in diverse domains such as sentiment analysis, fake news detection, medical diagnosis, and document classification. A sizable body of recen
Externí odkaz:
http://arxiv.org/abs/2410.09399
Autor:
Xing, Hao, Burschka, Darius
Understanding human activity is a crucial aspect of developing intelligent robots, particularly in the domain of human-robot collaboration. Nevertheless, existing systems encounter challenges such as over-segmentation, attributed to errors in the up-
Externí odkaz:
http://arxiv.org/abs/2410.07917
The utilisation of event cameras represents an important and swiftly evolving trend aimed at addressing the constraints of traditional video systems. Particularly within the automotive domain, these cameras find significant relevance for their integr
Externí odkaz:
http://arxiv.org/abs/2406.07318
Autor:
Fu, Zhongxiang, Cao, Buqing, Liu, Shanpeng, Peng, Qian, Peng, Zhenlian, Shi, Min, Liu, Shangli
Publikováno v:
International Journal of Web Information Systems, 2024, Vol. 20, Issue 5, pp. 520-536.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/IJWIS-06-2024-0178
To accelerate the training of graph convolutional networks (GCNs) on real-world large-scale sparse graphs, downsampling methods are commonly employed as a preprocessing step. However, the effects of graph sparsity and topological structure on the tra
Externí odkaz:
http://arxiv.org/abs/2408.17274
Dynamic graphs (DG) are often used to describe evolving interactions between nodes in real-world applications. Temporal patterns are a natural feature of DGs and are also key to representation learning. However, existing dynamic GCN models are mostly
Externí odkaz:
http://arxiv.org/abs/2408.02704