DeepAI AI Chat
Log In Sign Up

A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications

09/22/2017
by   Hongyun Cai, et al.
University of Illinois at Urbana-Champaign
0

Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.

READ FULL TEXT
11/30/2020

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

Heterogeneous graphs (HGs) also known as heterogeneous information netwo...
12/15/2020

Understanding graph embedding methods and their applications

Graph analytics can lead to better quantitative understanding and contro...
01/17/2021

A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

With the rapid development of biomedical software and hardware, a large ...
07/20/2018

Attention Models in Graphs: A Survey

Graph-structured data arise naturally in many different application doma...
10/13/2021

Scalable Graph Embedding LearningOn A Single GPU

Graph embedding techniques have attracted growing interest since they co...
10/02/2022

Metric Distribution to Vector: Constructing Data Representation via Broad-Scale Discrepancies

Graph embedding provides a feasible methodology to conduct pattern class...
08/28/2018

EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection

Constructing latent vector representation for nodes in a network through...