The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

Dong, Liyun; Li, Qinpeng; Ru, Yan; Zhang, Jun

doi:10.1109/access.2020.3044367

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…By calculating the similarity between different targets, the predicted score is calculated [ 37 ]. Compared with the calculating similarity by relying on neighbouring nodes, RWR can capture the global structural information of the network more comprehensively [ 38 , 39 ]. Starting from a certain node in the target set, RWR selects adjacent nodes with probability c (0 < c < 1) at random, or return to the previous node with probability (1− c ).…”

Section: Resultsmentioning

confidence: 99%

PPDTS: Predicting potential drug–target interactions based on network similarity

Wang

Liu

et al. 2021

IET Systems Biology

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Identification of drug–target interactions (DTIs) has great practical importance in the drug discovery process for known diseases. However, only a small proportion of DTIs in these databases has been verified experimentally, and the computational methods for predicting the interactions remain challenging. As a result, some effective computational models have become increasingly popular for predicting DTIs. In this work, the authors predict potential DTIs from the local structure of drug–target associations' network, which is different from the traditional global network similarity methods based on structure and ligand. A novel method called PPDTS is proposed to predict DTIs. First, according to the DTIs’ network local structure, the known DTIs are converted into a binary network. Second, the Resource Allocation algorithm is used to obtain a drug–drug similarity network and a target–target similarity network. Third, a Collaborative Filtering algorithm is used with the known drug–target topology information to obtain similarity scores. Fourth, the linear combination of drug–target similarity model and the target–drug similarity model are innovatively proposed to obtain the final prediction results. Finally, the experimental performance of PPDTS has proved to be higher than that of the previously mentioned four popular network‐based similarity methods, which is validated in different experimental datasets. Some of the predicted results can be supported in UniProt and DrugBank databases.

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Section: Resultsmentioning

confidence: 99%

PPDTS: Predicting potential drug–target interactions based on network similarity

Wang

Liu

et al. 2021

IET Systems Biology

Self Cite

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NODDLE: Node2vec Based Deep Learning Model for Link Prediction

Khanam

Singhal

Mago

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Community detection in social networks using machine learning: a systematic mapping study

Nooribakhsh,

Fernández-Diego,

González-Ladrón-De-Guevara

et al. 2024

Knowl Inf Syst

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One of the important issues in social networks is the social communities which are formed by interactions between its members. Three types of community including overlapping, non-overlapping, and hidden are detected by different approaches. Regarding the importance of community detection in social networks, this paper provides a systematic mapping of machine learning-based community detection approaches. The study aimed to show the type of communities in social networks along with the algorithms of machine learning that have been used for community detection. After carrying out the steps of mapping and removing useless references, 246 papers were selected to answer the questions of this research. The results of the research indicated that unsupervised machine learning-based algorithms with 41.46% (such as k means) are the most used categories to detect communities in social networks due to their low processing overheads. On the other hand, there has been a significant increase in the use of deep learning since 2020 which has sufficient performance for community detection in large-volume data. With regard to the ability of NMI to measure the correlation or similarity between communities, with 53.25%, it is the most frequently used metric to evaluate the performance of community identifications. Furthermore, considering availability, low in size, and lack of multiple edge and loops, dataset Zachary’s Karate Club with 26.42% is the most used dataset for community detection research in social networks.

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The Network Representation Learning Algorithm Based on Semi-Supervised Random Walk

Cited by 5 publications

References 27 publications

PPDTS: Predicting potential drug–target interactions based on network similarity

PPDTS: Predicting potential drug–target interactions based on network similarity

NODDLE: Node2vec Based Deep Learning Model for Link Prediction

Community detection in social networks using machine learning: a systematic mapping study

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