GANE: A Generative Adversarial Network Embedding

Hong, Huiting; Li, Xin; Wang, Mingzhong

doi:10.1109/tnnls.2019.2921841

Cited by 26 publications

(19 citation statements)

References 30 publications

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“…Traditional Gan often encounters instability problems such as gradient disappearance and mode collapse during training. Most of the improved methods introduced for these problems have good performance [38][39][40]. Although GAN is still in the early stages of medical imaging, it has already demonstrated its advantages in the medical field.…”

Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

162

104

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In recent years, the development of artificial intelligence (AI) and the gradual beginning of AI's research in the medical field have allowed people to see the excellent prospects of the integration of AI and healthcare. Among them, the hot deep learning field has shown greater potential in applications such as disease prediction and drug response prediction. From the initial logistic regression model to the machine learning model, and then to the deep learning model today, the accuracy of medical disease prediction has been continuously improved, and the performance in all aspects has also been significantly improved. This article introduces some basic deep learning frameworks and some common diseases, and summarizes the deep learning prediction methods corresponding to different diseases. Point out a series of problems in the current disease prediction, and make a prospect for the future development. It aims to clarify the effectiveness of deep learning in disease prediction, and demonstrates the high correlation between deep learning and the medical field in future development. The unique feature extraction methods of deep learning methods can still play an important role in future medical research.

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Section: Generative Adversarial Network (Gan)mentioning

confidence: 99%

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie¹,

Yu²,

Lv³

2021

Computer Modeling in Engineering &Amp; Sciences

162

104

View full text Add to dashboard Cite

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“…Some works apply perturbations to network structures [9,49], others on the node or edge attributes [23,46]. It is worth mentioning that Adversarial training based network embedding methods are different from Generative Adversarial Network (GAN) based ones [17,22,44]. GAN based network embedding methods utilize a mini-max game between connectivity generators and corresponding discriminators to obtain representations.…”

Section: Related Workmentioning

confidence: 99%

Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

Chen

Gao

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, both the noisy information behind the realworld networks and the overfitting problem negatively impact the quality of embedding vectors. To tackle these problems, researchers utilize Adversarial Perturbations on Parameters (APP) and achieve state-of-the-art performance. Unlike the mainstream methods introducing perturbations on the network structure or the data feature, Adversarial Training for Network Embedding (AdvTNE) adopts APP to directly perturb the model parameters, thus providing a new chance to understand the mechanism behind it. In this paper, we explain APP theoretically from an optimization perspective. Considering the Power-law property of networks and the optimization objective, we analyze the reason for its remarkable results on network embedding. Based on the above analysis and the Sigmoid saturation region problem, we propose a new Sine-base activation to enhance the performance of AdvTNE. We conduct extensive experiments on four real networks to validate the effectiveness of our method in node classification and link prediction. The results demonstrate that our method is competitive with state-of-the-art methods. CCS CONCEPTS• Networks → Topology analysis and generation; Physical topologies; • Information systems → Network data models; • Mathematics of computing → Mathematical optimization.

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“…In Graph-GAN (Wang et al 2017a), generative methods and discriminative methods are unified via adversarial training in a minimax game. GANE (Hong, Li, and Wang 2018), ANE (Dai et al 2017) and ARVGA (Pan et al 2018) also explore GANs on network representation by preserving network structure with adversarial learning.…”

Section: Related Workmentioning

confidence: 99%

DGE: Deep Generative Network Embedding Based on Commonality and Individuality

Zhou

Wang

et al. 2020

AAAI

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Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes and have edges connecting them (commonality). On the other hand, each information source may maintain individual differences as well (individuality). Simultaneously capturing commonality and individuality is very challenging due to their exclusive nature and existing work fail to do so. In this paper, we propose a deep generative embedding (DGE) framework which simultaneously captures commonality and individuality between network topology and node attributes in a generative process. Stochastic gradient variational Bayesian (SGVB) optimization is employed to infer model parameters as well as the node embeddings. Extensive experiments on four real-world datasets show the superiority of our proposed DGE framework in various tasks including node classification and link prediction.

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GANE: A Generative Adversarial Network Embedding

Cited by 26 publications

References 30 publications

Multi-Disease Prediction Based on Deep Learning: A Survey

Multi-Disease Prediction Based on Deep Learning: A Survey

Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective

DGE: Deep Generative Network Embedding Based on Commonality and Individuality

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