Learning Graph Embedding With Adversarial Training Methods

Pan, Shirui; Hu, Ruiqi; Fung, Sai-fu; Long, Guodong; Jiang, Jing; Zhang, Chengqi

doi:10.1109/tcyb.2019.2932096

Cited by 257 publications

(111 citation statements)

References 59 publications

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“…Thus, it is challenging to handle this general form of charge prediction. 3We will explore graph embedding with adversarial training methods to investigate the effectiveness of multi-charge prediction [41] [42]. 4We will explore how to incorporate task-sensitive features to improve the performance of multi-charge prediction [43] [44].…”

Section: Resultsmentioning

confidence: 99%

Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

Zhu

Huang

et al. 2020

IEEE Access

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The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high accuracy for the single-charge prediction, but their accuracy for the multi-charge prediction is low. To solve this problem, in this paper we introduce a novel hierarchical nested attention structure model with relevant law article information to predict the multi-charge classification of legal judgment documents. By considering the correlation between different charges, the accuracy of multi-charge prediction is greatly improved. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements over other state-of-the-art baselines.

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Section: Resultsmentioning

confidence: 99%

Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

Zhu

Huang

et al. 2020

IEEE Access

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“…It uses the graph as an input, integrates the neighborhood node feature and structure information of the graph nodes, and represents them as a vector. Graph convolutional networks have been successfully applied towards the prediction of multidrug side effects, social networks, recommendation system and prediction of drug-target interactions [42,43,44,45]. Here, the graph convolutional network was used to predict lncRNA-disease associations.…”

Section: Methodsmentioning

confidence: 99%

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Xuan

Pan

Zhang

et al. 2019

Cells

132

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Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and diseases integrate their pertinent heterogeneous data. However, they failed to deeply integrate topological information of heterogeneous network comprising lncRNAs, diseases, and miRNAs. We proposed a novel method based on the graph convolutional network and convolutional neural network, referred to as GCNLDA, to infer disease-related lncRNA candidates. The heterogeneous network containing the lncRNA, disease, and miRNA nodes, is constructed firstly. The embedding matrix of a lncRNA-disease node pair was constructed according to various biological premises about lncRNAs, diseases, and miRNAs. A new framework based on a graph convolutional network and a convolutional neural network was developed to learn network and local representations of the lncRNA-disease pair. On the left side of the framework, the autoencoder based on graph convolution deeply integrated topological information within the heterogeneous lncRNA-disease-miRNA network. Moreover, as different node features have discriminative contributions to the association prediction, an attention mechanism at node feature level is constructed. The left side learnt the network representation of the lncRNA-disease pair. The convolutional neural networks on the right side of the framework learnt the local representation of the lncRNA-disease pair by focusing on the similarities, associations, and interactions that are only related to the pair. Compared to several state-of-the-art prediction methods, GCNLDA had superior performance. Case studies on stomach cancer, osteosarcoma, and lung cancer confirmed that GCNLDA effectively discovers the potential lncRNA-disease associations.

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“…However, the abovementioned research work has designed the broadcast algorithm through interference avoidance scheduling technology [26]. at is, when there is a signal interference between broadcast links, the transmission of information between these nodes is distributed to different time slices to avoid mutual interference.…”

Section: Introductionmentioning

confidence: 99%

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

Yang

Luo

2020

Complexity

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In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

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Learning Graph Embedding With Adversarial Training Methods

Cited by 257 publications

References 59 publications

Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

Graph Convolutional Network and Convolutional Neural Network Based Method for Predicting lncRNA-Disease Associations

Effective Data Transmission and Control Based on Social Communication in Social Opportunistic Complex Networks

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