Hub-aware Random Walk Graph Embedding Methods for Classification

arXiv:2209.07603v3 Announce Type: replace
Abstract: In the last two decades we are witnessing a huge increase of valuable big data structured in the form of graphs or networks. To apply traditional machine learning and data analytic techniques to such data it is necessary to transform graphs into vector-based representations that preserve the most essential structural properties of graphs. For this purpose, a large number of graph embedding methods have been proposed in the literature. Most of them produce general-purpose embeddings …

abstract apply arxiv big big data classification cs.lg cs.si data embedding form graph graphs hub machine machine learning networks random traditional machine learning type vector