Network Representation Learning: A Survey

By Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. 2017

693 citations recorded by the source corpus.

With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

Adversarially Regularized Graph Autoencoder (2018, methodology): “This merit has motivated a number of studies in this area [Cai et al., 2017; Zhang et al., 2017a].”
DeepViT: Towards Deeper Vision Transformer (2021, background): “This is because a deeper CNN can learn richer and more complex representations for the input images and provide better performance on vision tasks [1, 45, 29].”
Evolutionary Architecture Search for Graph Neural Networks (2020, background): “NETWORK data and systems are ubiquitous [1], [2] in the real world including social network, document”
Uni-Mol: A Universal 3D Molecular Representation Learning Framework (2023, background): “Representation learning In recent years, representation learning [1, 2, 3] has received much attention and has been prevailing in many applications.”
Learning Graph Embedding With Adversarial Training Methods (2019, background): “Graph embedding, also known as network embedding [4] or network representation learning [10], transfers a graph into vectors.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Note that there are already many good surveys on static graphs [88, 251, 27, 48, 172, 227].”
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources (2020, neutral): “different from the existing surveys which mainly focus on homogeneous graph embedding [14], [27], [28], [29], [30], [31], we aim at exploring the works on heterogeneous graph”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, background): “Network Representation Learning draws a lot of attention in recent years [46].”
Representation Learning for Dynamic Graphs: A Survey (2019, background): “Note that there are already many good surveys on static graphs (see, e.g., Hamilton et al. (2017b); Zhang et al. (2018b); Cai et al. (2018); Cui et al. (2018); Nickel et al. (2016a); Shi et al. (2016); Wang et al. (2017a)).”
Graph Learning: A Survey (2021, methodology): “When complex networks are embedded into a latent, low dimensional space, the structural information and vertex attributes can be preserved [4].”
Machine Learning on Graphs: A Model and Comprehensive Taxonomy (2020, background): “First, there exist several surveys that cover shallow network embedding and auto-encoding techniques and we refer to [18, 24, 46, 51, 122] for a detailed overview of these methods.”