A Comprehensive Survey on Deep Graph Representation Learning

arXiv:2304.05055v3 Announce Type: replace
Abstract: Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is …

abstract arxiv basic cs.ai cs.ir cs.lg data data mining embedding encode fields graph graph representation low machine machine learning mining representation representation learning structured data survey type vectors