Transformation of Node to Knowledge Graph Embeddings for Faster Link Prediction in Social Networks. (arXiv:2111.09308v1 [cs.SI])

Recent advances in neural networks have solved common graph problems such as
link prediction, node classification, node clustering, node recommendation by
developing embeddings of entities and relations into vector spaces. Graph
embeddings encode the structural information present in a graph. The encoded
embeddings then can be used to predict the missing links in a graph. However,
obtaining the optimal embeddings for a graph can be a computationally
challenging task specially in an embedded system. Two techniques which we focus
on in this work are 1) node embeddings from random walk based methods and 2)
knowledge graph embeddings. Random walk based embeddings are computationally
inexpensive to obtain but are sub-optimal whereas knowledge graph embeddings
perform better but are computationally expensive. In this work, we investigate
a transformation model which converts node embeddings obtained from random walk
based methods to embeddings obtained from knowledge graph methods directly
without an increase in the computational cost. Extensive experimentation shows
that the proposed transformation model can be used for solving link prediction
in real-time.



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