Random Walks: A Review of Algorithms and Applications

Xia, Feng; Liu, Jiaying; Nie, Hansong; Fu, Yu; Wan, Liangtian; Kong, Xiangjie

doi:10.1109/tetci.2019.2952908

Cited by 179 publications

(86 citation statements)

References 67 publications

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“…From their part, Guo, et al [71] proposed graph neural network method to identify spammer by jointly embedding the occasional relations and stable relations. The parametric random walk method [173] was used to extract the occasional relations, while a direct vectorized encoding method was used to model the stable relation. Graph deep learning was developed to model the features of interaction.…”

Section: ) Other Neural Network Methodsmentioning

confidence: 99%

Fake Reviews Detection: A Survey

et al. 2021

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In e-commerce, user reviews can play a significant role in determining the revenue of an organisation.Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies' reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions. INDEX TERMSFake review; Fake review detection; Feature engineering, Machine learning; Deep learning.

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Section: ) Other Neural Network Methodsmentioning

confidence: 99%

Fake Reviews Detection: A Survey

et al. 2021

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“…In addition, there are several NRL models based on Skip-Gram model [1], which is a powerful model in natural language processing. Moreover, the random walk approach [83] has been applied to capture graph structure. To understand these NRL algo- SAGPool [82] rithms deduced from Skip-Gram model, we start with a brief introduction of Word2vec model [1] in this subsection.…”

Section: B Edge-based Modeling Methodsmentioning

confidence: 99%

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Sun

Wang

et al. 2020

IEEE Access

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Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field.

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“…The local path (LP) [51] index has been designed based on the path information. The widely used metric based on the structural similarity in networks is local random walk [52]. Random walk can to quantify relevance between nodes, and it is usually implemented for link prediction and recommendation tasks.…”

Section: Similarity Metricsmentioning

confidence: 99%

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

Wan

Xia

Kong

et al. 2021

IEEE Trans. Netw. Sci. Eng.

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Recent years have witnessed remarkable information overload in online social networks, and social network based approaches for recommender systems have been widely studied. The trust information in social networks among users is an important factor for improving recommendation performance. Many successful recommendation tasks are treated as the matrix factorization problems. However, the prediction performance of matrix factorization based methods largely depends on the matrixes initialization of users and items. To address this challenge, we develop a novel trust-aware approach based on deep learning to alleviate the initialization dependence. First, we propose two deep matrix factorization (DMF) techniques, i.e., linear DMF and non-linear DMF to extract features from the user-item rating matrix for improving the initialization accuracy. The trust relationship is integrated into the DMF model according to the preference similarity and the derivations of users on items. Second, we exploit deep marginalized Denoising Autoencoder (Deep-MDAE) to extract the latent representation in the hidden layer from the trust relationship matrix to approximate the user factor matrix factorized from the user-item rating matrix. The community regularization is integrated in the joint optimization function to take neighbours' effects into consideration. The results of DMF are applied to initialize the updating variables of Deep-MDAE in order to further improve the recommendation performance. Finally, we validate that the proposed approach outperforms state-of-the-art baselines for recommendation, especially for the cold-start users.

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Random Walks: A Review of Algorithms and Applications

Cited by 179 publications

References 67 publications

Fake Reviews Detection: A Survey

Fake Reviews Detection: A Survey

Network Representation Learning: From Traditional Feature Learning to Deep Learning

Deep Matrix Factorization for Trust-Aware Recommendation in Social Networks

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