Approximate Deep Network Embedding for Mining Large-Scale Graphs

2019 ◽  
Author(s):  
Yang Zhou ◽  
Ling Liu
Keyword(s):  
Large Scale ◽  
Network Embedding ◽  
Deep Network

scholarly journals Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Bo-Ya Ji ◽  
Zhu-Hong You ◽  
Han-Jing Jiang ◽  
Zhen-Hao Guo ◽  
Kai Zheng
Keyword(s):  
Computational Methods ◽  
Information Integration ◽  
Drug Target ◽  
Large Scale ◽  
New Drugs ◽  
Single Type ◽  
Information Network ◽  
Potential Drug Target ◽  
Potential Drug ◽  
Network Embedding

Abstract Background The prediction of potential drug-target interactions (DTIs) not only provides a better comprehension of biological processes but also is critical for identifying new drugs. However, due to the disadvantages of expensive and high time-consuming traditional experiments, only a small section of interactions between drugs and targets in the database were verified experimentally. Therefore, it is meaningful and important to develop new computational methods with good performance for DTIs prediction. At present, many existing computational methods only utilize the single type of interactions between drugs and proteins without paying attention to the associations and influences with other types of molecules. Methods In this work, we developed a novel network embedding-based heterogeneous information integration model to predict potential drug-target interactions. Firstly, a heterogeneous multi-molecuar information network is built by combining the known associations among protein, drug, lncRNA, disease, and miRNA. Secondly, the Large-scale Information Network Embedding (LINE) model is used to learn behavior information (associations with other nodes) of drugs and proteins in the network. Hence, the known drug-protein interaction pairs can be represented as a combination of attribute information (e.g. protein sequences information and drug molecular fingerprints) and behavior information of themselves. Thirdly, the Random Forest classifier is used for training and prediction. Results In the results, under the five-fold cross validation, our method obtained 85.83% prediction accuracy with 80.47% sensitivity at the AUC of 92.33%. Moreover, in the case studies of three common drugs, the top 10 candidate targets have 8 (Caffeine), 7 (Clozapine) and 6 (Pioglitazone) are respectively verified to be associated with corresponding drugs. Conclusions In short, these results indicate that our method can be a powerful tool for predicting potential drug-target interactions and finding unknown targets for certain drugs or unknown drugs for certain targets.

scholarly journals ProNE: Fast and Scalable Network Representation Learning

2019 ◽  
Author(s):  
Jie Zhang ◽  
Yuxiao Dong ◽  
Yan Wang ◽  
Jie Tang ◽  
Ming Ding
Keyword(s):  
Large Scale ◽  
Sparse Matrix ◽  
Representation Learning ◽  
Second Step ◽  
Network Embedding ◽  
Network Mining ◽  
Large Scale Networks ◽  
Scalable Network ◽  
Relative Gains ◽  
Sparse Matrix Factorization

Recent advances in network embedding has revolutionized the field of graph and network mining. However, (pre-)training embeddings for very large-scale networks is computationally challenging for most existing methods. In this work, we present ProNE---a fast, scalable, and effective model, whose single-thread version is 10--400x faster than efficient network embedding benchmarks with 20 threads, including LINE, DeepWalk, node2vec, GraRep, and HOPE. As a concrete example, the single-version ProNE requires only 29 hours to embed a network of hundreds of millions of nodes while it takes LINE weeks and DeepWalk months by using 20 threads. To achieve this, ProNE first initializes network embeddings efficiently by formulating the task as sparse matrix factorization. The second step of ProNE is to enhance the embeddings by propagating them in the spectrally modulated space. Extensive experiments on networks of various scales and types demonstrate that ProNE achieves both effectiveness and significant efficiency superiority when compared to the aforementioned baselines. In addition, ProNE's embedding enhancement step can be also generalized for improving other models at speed, e.g., offering >10% relative gains for the used baselines. 

scholarly journals Susceptible-infected-spreading-based network embedding in static and temporal networks

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Xiu-Xiu Zhan ◽  
Ziyu Li ◽  
Naoki Masuda ◽  
Petter Holme ◽  
Huijuan Wang
Keyword(s):  
Random Walk ◽  
Link Prediction ◽  
Large Scale ◽  
Language Model ◽  
Network Evolution ◽  
Temporal Networks ◽  
Network Embedding ◽  
Comparison Task ◽  
Missing Link ◽  
Sample Paths

Abstract Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two susceptible-infected-spreading-based algorithms, i.e., Susceptible-Infected Network Embedding (SINE) on static networks and Temporal Susceptible-Infected Network Embedding (TSINE) on temporal networks. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks.

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.

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