A Hybrid Time-Series Link Prediction Framework for Large Social Network

2012 ◽  
pp. 345-359 ◽  
Author(s):  
Jia Zhu ◽  
Qing Xie ◽  
Eun Jung Chin
Keyword(s):  
Time Series ◽  
Social Network ◽  
Link Prediction

scholarly journals Time+User Dual Attention Based Sentiment Prediction for Multiple Social Network Texts With Time Series

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 17644-17653 ◽  
Author(s):  
Lei Li ◽  
Yabin Wu ◽  
Yuwei Zhang ◽  
Tianyuan Zhao
Keyword(s):  
Time Series ◽  
Social Network

Efficient Time Series Clustering and Its Application to Social Network Mining

2014 ◽  
Vol 23 (2) ◽  
pp. 213-229 ◽  
Author(s):  
Cangqi Zhou ◽  
Qianchuan Zhao
Keyword(s):  
Time Series ◽  
Social Network ◽  
Information Diffusion ◽  
Time Series Data ◽  
Online Social Network ◽  
Dissimilarity Measure ◽  
Series Data ◽  
Formation Mechanisms ◽  
Network Analyses ◽  
Definition Of

AbstractMining time series data is of great significance in various areas. To efficiently find representative patterns in these data, this article focuses on the definition of a valid dissimilarity measure and the acceleration of partitioning clustering, a common group of techniques used to discover typical shapes of time series. Dissimilarity measure is a crucial component in clustering. It is required, by some particular applications, to be invariant to specific transformations. The rationale for using the angle between two time series to define a dissimilarity is analyzed. Moreover, our proposed measure satisfies the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. The experiments show that angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, the accelerated algorithm outperforms the standard one as redundancies are pruned. Our approach has been applied to discover typical patterns of information diffusion in an online social network. Analyses revealed the formation mechanisms of different patterns.

scholarly journals Group Relationship Mining of College Students Based on Predictive Social Network

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Huazhang Liu
Keyword(s):  
College Students ◽  
Social Networks ◽  
Social Network ◽  
Complex Network ◽  
Link Prediction ◽  
Rapid Development ◽  
Combination Method ◽  
Prediction Algorithm ◽  
The Social ◽  
Random Block

With the rapid development of the Internet, social networks have shown an unprecedented development trend among college students. Closer social activities among college students have led to the emergence of college students with new social characteristics. The traditional method of college students’ group classification can no longer meet the current demand. Therefore, this paper proposes a social network link prediction method-combination algorithm, which combines neighbor information and a random block. By mining the social networks of college students’ group relationships, the classification of college students’ groups can be realized. Firstly, on the basis of complex network theory, the essential relationship of college student groups under a complex network is analyzed. Secondly, a new combination algorithm is proposed by using the simplest linear combination method to combine the proximity link prediction based on neighbor information and the likelihood analysis link prediction based on a random block. Finally, the proposed combination algorithm is verified by using the social data of college students’ networks. Experimental results show that, compared with the traditional link prediction algorithm, the proposed combination algorithm can effectively dig out the group characteristics of social networks and improve the accuracy of college students’ association classification.

Time Series Geographic Social Network Dynamic Preference Group Query

2021 ◽  
Vol 13 (4) ◽  
pp. 18-39
Author(s):  
Yinglian Zhou ◽  
Jifeng Chen
Keyword(s):  
Time Series ◽  
Social Network ◽  
Social Impact ◽  
Network Models ◽  
User Preferences ◽  
Network Dynamic ◽  
Tree Index ◽  
Query Algorithm ◽  
Value Model ◽  
Dynamic Preferences

Driven by experience and social impact of the new life, user preferences continue to change over time. In order to make up for the shortcomings of existing geographic social network models that often cannot obtain user dynamic preferences, a time-series geographic social network model was constructed to detect user dynamic preferences, a dynamic preference value model was built for user dynamic preference evaluation, and a dynamic preferences group query (DPG) was proposed in this paper . In order to optimize the efficiency of the DPG query algorithm, the UTC-tree index user timing check-in record is designed. UTC-tree avoids traversing all user check-in records in the query, accelerating user dynamic preference evaluation. Finally, the DPG query algorithm is used to implement a well-interacted DPG query system. Through a large number of comparative experiments, the validity of UTC-tree and the scalability of DPG query are verified.

Link Prediction in Social Networks

2018 ◽  
pp. 164-172 ◽  
Author(s):  
Sovan Samanta ◽  
Madhumangal Pal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Link Prediction ◽  
Research Work ◽  
Dense Networks ◽  
Friend Recommendation ◽  
Neighborhood Method

Social network is a topic of current research. Relations are broken and new relations are increased. This chapter will discuss the scope or predictions of new links in social networks. Here different approaches for link predictions are described. Among them friend recommendation model is latest. There are some other methods like common neighborhood method which is also analyzed here. The comparison among them to predict links in social networks is described. The significance of this research work is to find strong dense networks in future.

An Experimental Evaluation of Link Prediction for Movie Suggestions Using Social Media Content

2018 ◽  
pp. 203-230
Author(s):  
Anu Taneja ◽  
Bhawna Gupta ◽  
Anuja Arora
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Social Networks ◽  
Experimental Evaluation ◽  
Link Prediction ◽  
Common Neighbor ◽  
Research Directions ◽  
Definition Of

The enormous growth and dynamic nature of online social networks have emerged to new research directions that examine the social network analysis mechanisms. In this chapter, the authors have explored a novel technique of recommendation for social media and used well known social network analysis (SNA) mechanisms-link prediction. The initial impetus of this chapter is to provide general description, formal definition of the problem, its applications, state-of-art of various link prediction approaches in social media networks. Further, an experimental evaluation has been made to inspect the role of link prediction in real environment by employing basic common neighbor link prediction approach on IMDb data. To improve performance, weighted common neighbor link prediction (WCNLP) approach has been proposed. This exploits the prediction features to predict new links among users of IMDb. The evaluation shows how the inclusion of weight among the nodes offers high link prediction performance and opens further research directions.

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