SSDBA: the stretch shrink distance based algorithm for link prediction in social networks

Yan, Ruidong; Li, Yang; Li, Deying; Wu, Weili; Wang, Yongcai

doi:10.1007/s11704-019-9083-3

Cited by 7 publications

(4 citation statements)

References 33 publications

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“…Feng et al 7 completed map matching in the latent space based on deep learning and enhanced the matching with the knowledge of mobile patterns. Yan et al 8 proposed an algorithm based on stretching and shrinking distance (SSDBA) for link prediction in social networks. In our previous work, we evaluated the usefulness of risky permissions for malapp detection with SVM, Decision Trees, as well as Random Forests.…”

Section: Related Workmentioning

confidence: 99%

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang

Zhu

et al. 2021

International Journal of Distributed Sensor Networks

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“Social sensors” refer to those who provide opinions through electronic communication channels such as social networks. There are two major issues in current models of sentiment analysis in social sensor networks. First, most existing models only analyzed the sentiment within the text but did not analyze the users, which led to the experimental results difficult to explain. Second, few studies extract the specific opinions of users. Only analyzing the emotional tendencies or aspect-level emotions of social users brings difficulties to the analysis of the opinion evolution in public emergencies. To resolve these issues, we propose an explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors. Our model extracts the specific opinions of the user groups on the topics and fully considers the impacts of their diverse features on sentiment analysis. We conduct experiments on 51,853 tweets about the “COVID-19” collected from 1 May 2020 to 9 July 2020. We build users’ portraits from three aspects: attribute features, interest features, and emotional features. Six machine learning algorithms are used to predict emotional tendency based on users’ portraits. We analyze the influence of users’ features on the sentiment. The prediction accuracy of our model is 64.88%.

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Section: Related Workmentioning

confidence: 99%

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

Wang

Zhu

et al. 2021

International Journal of Distributed Sensor Networks

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“…Therefore, predicting these links is still challenging and it is necessary to achieve a predictive method with acceptable precision. Although, the link prediction problem has been extensively studied and various researches have been presented to solve it [7][8][9]; However, the problem of how to optimally and effectively combine information to describe future communications remains largely unresolved. In [10], to link prediction in social networks, the analysis of user's demographic features has been used.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the link prediction problem has become popular on large networks. Researchers have proposed various methods to find missing links [7][8][9]. Most of these methods are calculated based on a similarity measure on neighboring nodes [11].…”

Section: Introductionmentioning

confidence: 99%

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

Yang

Peng

et al. 2022

Concurrency and Computation

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Online social networks are an integral element of modern societies and significantly influence the formation and consolidation of social relationships. In fact, these networks are multi-layered so that there may be multiple links between a user' on different social networks. In this paper, the link prediction problem for the same user in a two-layer social network is examined, where we consider Twitter and Foursquare networks. Here, information related to the two-layer communication is used to predict links in the Foursquare network. Link prediction aims to discover spurious links or predict the emergence of future links from the current network structure. There are many algorithms for link prediction in unweighted networks, however only a few have been developed for weighted networks. Based on the extraction of topological features from the network structure and the use of reliable paths between users, we developed a novel similarity measure for link prediction. Reliable paths have been proposed to develop unweight local similarity measures to weighted measures. Using these measures, both the existence of links and their weight can be predicted. Empirical analysis shows that the proposed similarity measure achieves superior performance to existing approaches and can more accurately predict future relationships. In addition, the proposed method has better results compared to single-layer networks. Experiments show that the proposed similarity measure has an advantage precision of 1.8% over the Katz and FriendLink measures.

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“…For this purpose, sequential pattern mining is one of the essential methods. It has a broad field in data science and applications in many sectors, such as shop analysis (Valle, Ruz, and Morriás 2018; Moodley et al 2019;Astrova, Koschel, and Lee 2020), web-based analysis (Danilowicz et al 2000;Nguyen 2000Nguyen , 2002Nguyen and Sobecki 2003;Hagen and Stein 2018; Prakash and Jaya 2020), prediction (Fabra, Alvarez, and Ezpeleta 2020;Soui et al 2020;Yan et al 2021), and bioinformatics analysis (Asite and Aleksejeva 2019; Aberra et al 2020;Shihab, Dawood, and Kashmar 2020). Its main goal is to identify recurrent patterns in any kind of data: images, transactions, sequences of figures.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Mining Closed Clickstream Patterns

Huynh

Nguyen

Huynh

et al. 2021

Cybernetics and Systems

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Closed sequential pattern (CSP) mining is an optimization technique in sequential pattern mining because they produce more compact representations. Additionally, the runtime and memory usage required for mining CSPs is much lower than the sequential pattern mining. This task has fascinated numerous researchers. In this study, we propose a novel approach for closed clickstream pattern mining using C-List (CCPC) data structure. Closed clickstream pattern mining is a more specific task of CSP mining that has been lacking in research investment; nevertheless, it has promising applications in various fields. CCPC consists of two key steps: It initially builds the SPPC-tree and the C-List for each frequent 1-pattern and then determines all frequently closed clickstream 1-patterns; next, it constructs the C-List for each frequent k-pattern and mines the remaining frequently closed k-patterns. The proposed method is optimized by modifying the SPPC-tree structure and a new property is added into each node element in both the SPPC-tree and C-Lists to quickly prune nonclosed clickstream. Experimental results conducted on several datasets show that the proposed method is better than the previous techniques and improves the runtime and memory usage in most cases, especially when using low minimum support thresholds on the huge databases.

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SSDBA: the stretch shrink distance based algorithm for link prediction in social networks

Cited by 7 publications

References 33 publications

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

An explainable sentiment prediction model based on the portraits of users sharing representative opinions in social sensors

A novel similarity measure of link prediction in multi‐layer social networks based on reliable paths

A Novel Approach for Mining Closed Clickstream Patterns

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