Graph Learning: A Survey
- URL: http://arxiv.org/abs/2105.00696v1
- Date: Mon, 3 May 2021 09:06:01 GMT
- Title: Graph Learning: A Survey
- Authors: Feng Xia, Ke Sun, Shuo Yu, Abdul Aziz, Liangtian Wan, Shirui Pan, Huan
Liu
- Abstract summary: We present a comprehensive overview on the state-of-the-art of graph learning.
Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning.
We examine graph learning applications in areas such as text, images, science, knowledge graphs, and optimization.
- Score: 38.245120261668816
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Graphs are widely used as a popular representation of the network structure
of connected data. Graph data can be found in a broad spectrum of application
domains such as social systems, ecosystems, biological networks, knowledge
graphs, and information systems. With the continuous penetration of artificial
intelligence technologies, graph learning (i.e., machine learning on graphs) is
gaining attention from both researchers and practitioners. Graph learning
proves effective for many tasks, such as classification, link prediction, and
matching. Generally, graph learning methods extract relevant features of graphs
by taking advantage of machine learning algorithms. In this survey, we present
a comprehensive overview on the state-of-the-art of graph learning. Special
attention is paid to four categories of existing graph learning methods,
including graph signal processing, matrix factorization, random walk, and deep
learning. Major models and algorithms under these categories are reviewed
respectively. We examine graph learning applications in areas such as text,
images, science, knowledge graphs, and combinatorial optimization. In addition,
we discuss several promising research directions in this field.
Related papers
- Curriculum Graph Machine Learning: A Survey [51.89783017927647]
curriculum graph machine learning (Graph CL) integrates the strength of graph machine learning and curriculum learning.
This paper comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction.
arXiv Detail & Related papers (2023-02-06T16:59:25Z) - State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
Graph-level learning has been applied to many tasks including comparison, regression, classification, and more.
Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures.
Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
arXiv Detail & Related papers (2023-01-14T09:15:49Z) - Graph Learning and Its Advancements on Large Language Models: A Holistic Survey [37.01696685233113]
This survey focuses on the most recent advancements in integrating graph learning with pre-trained language models.
We provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
arXiv Detail & Related papers (2022-12-17T22:05:07Z) - CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph
Similarity Learning [65.1042892570989]
We propose a contrastive graph matching network (CGMN) for self-supervised graph similarity learning.
We employ two strategies, namely cross-view interaction and cross-graph interaction, for effective node representation learning.
We transform node representations into graph-level representations via pooling operations for graph similarity computation.
arXiv Detail & Related papers (2022-05-30T13:20:26Z) - Graph Pooling for Graph Neural Networks: Progress, Challenges, and
Opportunities [128.55790219377315]
Graph neural networks have emerged as a leading architecture for many graph-level tasks.
graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
arXiv Detail & Related papers (2022-04-15T04:02:06Z) - Graph Lifelong Learning: A Survey [6.545297572977323]
This paper focuses on the motivations, potentials, state-of-the-art approaches, and open issues of graph lifelong learning.
We expect extensive research and development interest in this emerging field.
arXiv Detail & Related papers (2022-02-22T06:14:07Z) - Learning Graph Representations [0.0]
Graph Neural Networks (GNNs) are efficient ways to get insight into large dynamic graph datasets.
In this paper, we discuss the graph convolutional neural networks graph autoencoders and Social-temporal graph neural networks.
arXiv Detail & Related papers (2021-02-03T12:07:55Z) - GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism.
The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain.
GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
arXiv Detail & Related papers (2020-07-07T16:51:39Z) - GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training [62.73470368851127]
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
We design Graph Contrastive Coding -- a self-supervised graph neural network pre-training framework.
We conduct experiments on three graph learning tasks and ten graph datasets.
arXiv Detail & Related papers (2020-06-17T16:18:35Z) - Machine Learning on Graphs: A Model and Comprehensive Taxonomy [22.73365477040205]
We bridge the gap between graph neural networks, network embedding and graph regularization models.
Specifically, we propose a Graph Decoder Model (GRAPHEDM), which generalizes popular algorithms for semi-supervised learning on graphs.
arXiv Detail & Related papers (2020-05-07T18:00:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.