Related papers: Deep Learning for Learning Graph Representations

Deep Learning for Learning Graph Representations

URL: http://arxiv.org/abs/2001.00293v1
Date: Thu, 2 Jan 2020 02:13:28 GMT
Title: Deep Learning for Learning Graph Representations
Authors: Wenwu Zhu, Xin Wang, Peng Cui
Abstract summary: Mining graph data has become a popular research topic in computer science. The huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space.
Score: 58.649784596090385
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Mining graph data has become a popular research topic in computer science and has been widely studied in both academia and industry given the increasing amount of network data in the recent years. However, the huge amount of network data has posed great challenges for efficient analysis. This motivates the advent of graph representation which maps the graph into a low-dimension vector space, keeping original graph structure and supporting graph inference. The investigation on efficient representation of a graph has profound theoretical significance and important realistic meaning, we therefore introduce some basic ideas in graph representation/network embedding as well as some representative models in this chapter.

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