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

Abstract

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.

A 20-page comprehensive survey of graph/network embedding for over 150+ papers till year 2018. It provides systematic categorization of problems, techniques and applications. Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE). Comments and suggestions are welcomed for continuously improving this survey

References in corpus (4)

• Deep Convolutional Networks on Graph-Structured Data
• struc2vec: Learning Node Representations from Structural Identity
• ProjE: Embedding Projection for Knowledge Graph Completion
• Graph-based Isometry Invariant Representation Learning

Cited by in corpus (18)

• GraphSAINT: Graph Sampling Based Inductive Learning Method
• Convolutional Neural Network Architectures for Signals Supported on Graphs
• Multi-Label Text Classification using Attention-based Graph Neural Network
• A Broader Picture of Random-walk Based Graph Embedding
• Neural ranking models for document retrieval
• Build2Vec: Building Representation in Vector Space
• ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
• Initialization for Network Embedding: A Graph Partition Approach
• PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
• The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
• Cultivating Online: Question Routing in a Question and Answering Community for Agriculture
• Leveraging Knowledge Graph Embedding Techniques for Industry 4.0 Use Cases
• A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
• Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
• Mercem: Method Name Recommendation Based on Call Graph Embedding
• GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning
• Can x2vec Save Lives? Integrating Graph and Language Embeddings for Automatic Mental Health Classification
• Constructing Graph Node Embeddings via Discrimination of Similarity Distributions