scholarly journals Multimodal Deep Network Embedding With Integrated Structure and Attribute Information

2020 ◽  
Vol 31 (5) ◽  
pp. 1437-1449 ◽  
Author(s):  
Conghui Zheng ◽  
Li Pan ◽  
Peng Wu
Keyword(s):  
Network Embedding ◽  
Deep Network ◽  
Attribute Information

scholarly journals A structural deep network embedding model for predicting associations between miRNA and disease based on molecular association network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao-Yuan Li ◽  
Hai-Yan Chen ◽  
Lei Wang ◽  
Shen-Jian Song ◽  
Zhu-Hong You ◽  
...  
Keyword(s):  
Feature Descriptor ◽  
Network Embedding ◽  
Deep Network ◽  
Physiological Processes ◽  
Molecular Associations ◽  
Proposed Model ◽  
Validation Experiment ◽  
Attribute Information ◽  
Disease Associations ◽  
And Behavior

AbstractPrevious studies indicated that miRNA plays an important role in human biological processes especially in the field of diseases. However, constrained by biotechnology, only a small part of the miRNA-disease associations has been verified by biological experiment. This impel that more and more researchers pay attention to develop efficient and high-precision computational methods for predicting the potential miRNA-disease associations. Based on the assumption that molecules are related to each other in human physiological processes, we developed a novel structural deep network embedding model (SDNE-MDA) for predicting miRNA-disease association using molecular associations network. Specifically, the SDNE-MDA model first integrating miRNA attribute information by Chao Game Representation (CGR) algorithm and disease attribute information by disease semantic similarity. Secondly, we extract feature by structural deep network embedding from the heterogeneous molecular associations network. Then, a comprehensive feature descriptor is constructed by combining attribute information and behavior information. Finally, Convolutional Neural Network (CNN) is adopted to train and classify these feature descriptors. In the five-fold cross validation experiment, SDNE-MDA achieved AUC of 0.9447 with the prediction accuracy of 87.38% on the HMDD v3.0 dataset. To further verify the performance of SDNE-MDA, we contrasted it with different feature extraction models and classifier models. Moreover, the case studies with three important human diseases, including Breast Neoplasms, Kidney Neoplasms, Lymphoma were implemented by the proposed model. As a result, 47, 46 and 46 out of top-50 predicted disease-related miRNAs have been confirmed by independent databases. These results anticipate that SDNE-MDA would be a reliable computational tool for predicting potential miRNA-disease associations.

FLGAI: a unified network embedding framework integrating multi-scale network structures and node attribute information

2020 ◽  
Vol 50 (11) ◽  
pp. 3976-3989
Author(s):  
Yu Pan ◽  
Guyu Hu ◽  
Junyang Qiu ◽  
Yanyan Zhang ◽  
Shuaihui Wang ◽  
...  
Keyword(s):  
Network Structures ◽  
Network Embedding ◽  
Multi Scale ◽  
Node Attribute ◽  
Attribute Information ◽  
Scale Network

scholarly journals Adversarial Deep Network Embedding for Cross-Network Node Classification

2020 ◽  
Vol 34 (03) ◽  
pp. 2991-2999 ◽  
Author(s):  
Xiao Shen ◽  
Quanyu Dai ◽  
Fu-lai Chung ◽  
Wei Lu ◽  
Kup-Sze Choi
Keyword(s):  
Domain Adaptation ◽  
Structural Information ◽  
Classification Problem ◽  
Network Node ◽  
Network Embedding ◽  
Deep Network ◽  
Network Applications ◽  
Target Network ◽  
Node Classification ◽  
Learn Network

In this paper, the task of cross-network node classification, which leverages the abundant labeled nodes from a source network to help classify unlabeled nodes in a target network, is studied. The existing domain adaptation algorithms generally fail to model the network structural information, and the current network embedding models mainly focus on single-network applications. Thus, both of them cannot be directly applied to solve the cross-network node classification problem. This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information. In ACDNE, the deep network embedding module utilizes two feature extractors to jointly preserve attributed affinity and topological proximities between nodes. In addition, a node classifier is incorporated to make node representations label-discriminative. Moreover, an adversarial domain adaptation technique is employed to make node representations network-invariant. Extensive experimental results demonstrate that the proposed ACDNE model achieves the state-of-the-art performance in cross-network node classification.

scholarly journals An Unsupervised Learning Method for Attributed Network Based on Non-Euclidean Geometry

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Wei Wu ◽  
Guangmin Hu ◽  
Fucai Yu
Keyword(s):  
Ricci Curvature ◽  
Hyperbolic Geometry ◽  
Euclidean Geometry ◽  
Structural Information ◽  
Information Aggregation ◽  
Network Embedding ◽  
Heterogeneous Information ◽  
Non Euclidean Geometry ◽  
Attributed Network ◽  
Attribute Information

Many real-world networks can be modeled as attributed networks, where nodes are affiliated with attributes. When we implement attributed network embedding, we need to face two types of heterogeneous information, namely, structural information and attribute information. The structural information of undirected networks is usually expressed as a symmetric adjacency matrix. Network embedding learning is to utilize the above information to learn the vector representations of nodes in the network. How to integrate these two types of heterogeneous information to improve the performance of network embedding is a challenge. Most of the current approaches embed the networks in Euclidean spaces, but the networks themselves are non-Euclidean. As a consequence, the geometric differences between the embedded space and the underlying space of the network will affect the performance of the network embedding. According to the non-Euclidean geometry of networks, this paper proposes an attributed network embedding framework based on hyperbolic geometry and the Ricci curvature, namely, RHAE. Our method consists of two modules: (1) the first module is an autoencoder module in which each layer is provided with a network information aggregation layer based on the Ricci curvature and an embedding layer based on hyperbolic geometry; (2) the second module is a skip-gram module in which the random walk is based on the Ricci curvature. These two modules are based on non-Euclidean geometry, but they fuse the topology information and attribute information in the network from different angles. Experimental results on some benchmark datasets show that our approach outperforms the baselines.

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