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The paper offers an algorithm to generate k-hopped topological and feature information in graphs. It highlights the need for in-depth analysis of graph embedding techniques' performance in capturing hopped neighborhood information for link prediction. The proposed framework evaluates six prominent graph embeddings (ARGE, ARVGE, Node2vec, Attri2Vec, GraphSage, and GCN) using $k$-hopped link prediction on diverse graph datasets. The results reveal a notable increase in link prediction performance for $1$-hopped graph information, followed by a continuous decline beyond $1$-hop. This underscores the significance of embedding performance assessment using $k$-hopped link prediction.

Due to the lack of more variate, native and continuous datasets, sign languages are low-resources languages that can benefit from multilingualism in machine translation. In order to analyze the benefits of approaches like multilingualism, finding the …

In recent years, researchers have started looking into data transformations in quantum computation. They want to see how quantum computing affects the robustness and performance of machine learning methods. Quantum mechanics succeed in explaining …