Multiple Structure-View Learning for Graph Classification

Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph sett...

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Vydané v:	IEEE transaction on neural networks and learning systems Ročník 29; číslo 7; s. 3236 - 3251
Hlavní autori:	Wu, Jia, Pan, Shirui, Zhu, Xingquan, Zhang, Chengqi, Yu, Philip S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Chemical compounds Classification Correlation Graph graph classification Graphical representations Graphs Image color analysis Labels Learning Learning systems Machine learning multiview learning Periodic structures Proteins Regularization subgraph mining Visualization
ISSN:	2162-237X, 2162-2388, 2162-2388
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Abstract	Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.
AbstractList	Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches. Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.Many applications involve objects containing structure and rich content information, each describing different feature aspects of the object. Graph learning and classification is a common tool for handling such objects. To date, existing graph classification has been limited to the single-graph setting with each object being represented as one graph from a single structure-view. This inherently limits its use to the classification of complicated objects containing complex structures and uncertain labels. In this paper, we advance graph classification to handle multigraph learning for complicated objects from multiple structure views, where each object is represented as a bag containing several graphs and the label is only available for each graph bag but not individual graphs inside the bag. To learn such graph classification models, we propose a multistructure-view bag constrained learning (MSVBL) algorithm, which aims to explore substructure features across multiple structure views for learning. By enabling joint regularization across multiple structure views and enforcing labeling constraints at the bag and graph levels, MSVBL is able to discover the most effective substructure features across all structure views. Experiments and comparisons on real-world data sets validate and demonstrate the superior performance of MSVBL in representing complicated objects as multigraph for classification, e.g., MSVBL outperforms the state-of-the-art multiview graph classification and multiview multi-instance learning approaches.
Author	Wu, Jia Pan, Shirui Zhang, Chengqi Yu, Philip S. Zhu, Xingquan
Author_xml	– sequence: 1 givenname: Jia orcidid: 0000-0002-1371-5801 surname: Wu fullname: Wu, Jia email: jia.wu@mq.edu.au organization: Department of Computing, Faculty of Science and Engineering, Macquarie University, Sydney, NSW, Australia – sequence: 2 givenname: Shirui surname: Pan fullname: Pan, Shirui email: shirui.pan@uts.edu.au organization: Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia – sequence: 3 givenname: Xingquan surname: Zhu fullname: Zhu, Xingquan email: xzhu3@fau.edu organization: Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA – sequence: 4 givenname: Chengqi orcidid: 0000-0001-5715-7154 surname: Zhang fullname: Zhang, Chengqi email: chengqi.zhang@uts.edu.au organization: Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia – sequence: 5 givenname: Philip S. orcidid: 0000-0002-3491-5968 surname: Yu fullname: Yu, Philip S. email: psyu@cs.uic.edu organization: Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
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SubjectTerms	Chemical compounds Classification Correlation Graph graph classification Graphical representations Graphs Image color analysis Labels Learning Learning systems Machine learning multiview learning Periodic structures Proteins Regularization subgraph mining Visualization
Title	Multiple Structure-View Learning for Graph Classification
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