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A Novel Global Prototype-Based Node Embedding Technique
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DOI：10.1109/ACCESS.2022.3225413.png)
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Abstract
En 中文
Node embedding refers to learning or generating low-dimensional representations for nodes in a given graph. In the era of big data and large graphs, there has been a growing interest in node embedding across a wide range of applications, ranging from social media to healthcare. Numerous research efforts have been invested in searching for node embeddings that maximally preserve the associated graph properties. However, each embedding technique has its own limitations. This paper presents a method for generating deep neural node embeddings that encode dissimilarity scores between pairs of nodes with the help of prototype nodes spread throughout the target graph. The proposed technique is adaptable to various notions of dissimilarity and yields efficient embeddings capable of estimating the dissimilarity between any two pairs of nodes in a graph. We compare our technique against relevant state-of-the-art similar embedding techniques. Superior results have been demonstrated in a number of experiments using several benchmark datasets.
Keywords:
Prototypes
Training data
Neurons
Task analysis
Feature extraction
Biological neural networks
Indexes
Deep neural networks
link prediction
node embedding
unsupervised node classification
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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