Abstract
A graph is one of important mathematical tools to describe ubiquitous relations. In the classical graph theory and some applications, graphs are generally provided in advance, or can at least be defined clearly. Thus, the main focus is to measure/analyze the graphs for mining informative patterns. However, for many real-world scenarios, the graph is often uncertain due to the fact that data associated with the vertices in the graph are high-dimensional, noisy, differently distributed, and even no clear definition. As a result, one needs to design or learn graphs from data prior to any analysis, which in turn affects the subsequent tasks. Therefore, constructing a high-quality graph has become an increasingly hot research problem, which inspired many graph construction methods being proposed in the past years. Since there has been no systematic summary on this topic, in this paper, we review the main-stream graph construction/learning methods involved in both general machine learning algorithms (including semi-supervised learning, clustering, manifold learning, and spectral kernel learning, etc.) and some specific applications (especially, the modeling and analysis of functional brain connectivity). Additionally, we introduce a matrix-regularized graph learning framework that can benefit to unify some existing graph construction models and develop new graph learning algorithms. Finally, we discuss several related topics and some promising research directions in this field.
| Original language | English |
|---|---|
| Journal | Neurocomputing |
| DOIs | |
| Publication status | Accepted/In press - 2018 Jan 1 |
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Keywords
- Brain network
- Graph construction
- Low-rank representation
- Machine learning
- Multi-graph learning
- Sparse representation
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence
Cite this
Data-driven graph construction and graph learning : A review. / Qiao, Lishan; Zhang, Limei; Chen, Songcan; Shen, Dinggang.
In: Neurocomputing, 01.01.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Data-driven graph construction and graph learning
T2 - A review
AU - Qiao, Lishan
AU - Zhang, Limei
AU - Chen, Songcan
AU - Shen, Dinggang
PY - 2018/1/1
Y1 - 2018/1/1
N2 - A graph is one of important mathematical tools to describe ubiquitous relations. In the classical graph theory and some applications, graphs are generally provided in advance, or can at least be defined clearly. Thus, the main focus is to measure/analyze the graphs for mining informative patterns. However, for many real-world scenarios, the graph is often uncertain due to the fact that data associated with the vertices in the graph are high-dimensional, noisy, differently distributed, and even no clear definition. As a result, one needs to design or learn graphs from data prior to any analysis, which in turn affects the subsequent tasks. Therefore, constructing a high-quality graph has become an increasingly hot research problem, which inspired many graph construction methods being proposed in the past years. Since there has been no systematic summary on this topic, in this paper, we review the main-stream graph construction/learning methods involved in both general machine learning algorithms (including semi-supervised learning, clustering, manifold learning, and spectral kernel learning, etc.) and some specific applications (especially, the modeling and analysis of functional brain connectivity). Additionally, we introduce a matrix-regularized graph learning framework that can benefit to unify some existing graph construction models and develop new graph learning algorithms. Finally, we discuss several related topics and some promising research directions in this field.
AB - A graph is one of important mathematical tools to describe ubiquitous relations. In the classical graph theory and some applications, graphs are generally provided in advance, or can at least be defined clearly. Thus, the main focus is to measure/analyze the graphs for mining informative patterns. However, for many real-world scenarios, the graph is often uncertain due to the fact that data associated with the vertices in the graph are high-dimensional, noisy, differently distributed, and even no clear definition. As a result, one needs to design or learn graphs from data prior to any analysis, which in turn affects the subsequent tasks. Therefore, constructing a high-quality graph has become an increasingly hot research problem, which inspired many graph construction methods being proposed in the past years. Since there has been no systematic summary on this topic, in this paper, we review the main-stream graph construction/learning methods involved in both general machine learning algorithms (including semi-supervised learning, clustering, manifold learning, and spectral kernel learning, etc.) and some specific applications (especially, the modeling and analysis of functional brain connectivity). Additionally, we introduce a matrix-regularized graph learning framework that can benefit to unify some existing graph construction models and develop new graph learning algorithms. Finally, we discuss several related topics and some promising research directions in this field.
KW - Brain network
KW - Graph construction
KW - Low-rank representation
KW - Machine learning
KW - Multi-graph learning
KW - Sparse representation
UR - http://www.scopus.com/inward/record.url?scp=85048586477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048586477&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2018.05.084
DO - 10.1016/j.neucom.2018.05.084
M3 - Article
AN - SCOPUS:85048586477
JO - Neurocomputing
JF - Neurocomputing
SN - 0925-2312
ER -