In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very importan...In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.展开更多
Graph neural networks(GNNs)have achieved significant success in graph representation learning.Nevertheless,the recent work indicates that current GNNs are vulnerable to adversarial perturbations,in particular structur...Graph neural networks(GNNs)have achieved significant success in graph representation learning.Nevertheless,the recent work indicates that current GNNs are vulnerable to adversarial perturbations,in particular structural perturbations.This,therefore,narrows the application of GNN models in real-world scenarios.Such vulnerability can be attributed to the model’s excessive reliance on incomplete data views(e.g.,graph convolutional networks(GCNs)heavily rely on graph structures to make predictions).By integrating the information from multiple perspectives,this problem can be effectively addressed,and typical views of graphs include the node feature view and the graph structure view.In this paper,we propose C^(2)oG,which combines these two typical views to train sub-models and fuses their knowledge through co-training.Due to the orthogonality of the views,sub-models in the feature view tend to be robust against the perturbations targeted at sub-models in the structure view.C^(2)oG allows sub-models to correct one another mutually and thus enhance the robustness of their ensembles.In our evaluations,C^(2)oG significantly improves the robustness of graph models against adversarial attacks without sacrificing their performance on clean datasets.展开更多
基金supported in part by the National Natural Science Foundation of China (No. 61572407)
文摘In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering.
基金This work was partially supported by the National University of Defense Technology Foundation under Grant Nos.ZK20-09 and ZK21-17,and the Natural Science Foundation of Hunan Province of China under Grant No.2021JJ40692.
文摘Graph neural networks(GNNs)have achieved significant success in graph representation learning.Nevertheless,the recent work indicates that current GNNs are vulnerable to adversarial perturbations,in particular structural perturbations.This,therefore,narrows the application of GNN models in real-world scenarios.Such vulnerability can be attributed to the model’s excessive reliance on incomplete data views(e.g.,graph convolutional networks(GCNs)heavily rely on graph structures to make predictions).By integrating the information from multiple perspectives,this problem can be effectively addressed,and typical views of graphs include the node feature view and the graph structure view.In this paper,we propose C^(2)oG,which combines these two typical views to train sub-models and fuses their knowledge through co-training.Due to the orthogonality of the views,sub-models in the feature view tend to be robust against the perturbations targeted at sub-models in the structure view.C^(2)oG allows sub-models to correct one another mutually and thus enhance the robustness of their ensembles.In our evaluations,C^(2)oG significantly improves the robustness of graph models against adversarial attacks without sacrificing their performance on clean datasets.
