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.

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

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.

scholarly journals A Comprehensive Survey of Time Series Anomaly Detection in Online Social Network Data

2017 ◽  
Vol 180 (3) ◽  
pp. 13-22 ◽  
Author(s):  
Md Rafiqul ◽  
Naznin Sultana ◽  
Mohammad Ali ◽  
Prohollad Chandra ◽  
Bushra Rahman
Keyword(s):  
Time Series ◽  
Social Network ◽  
Anomaly Detection ◽  
Online Social Network ◽  
Network Data ◽  
Social Network Data ◽  
Comprehensive Survey

Detecting changes in context using time series analysis of social network

2015 ◽  
Author(s):  
Kajal Nusratullah ◽  
Shoab Ahmad Khan ◽  
Asadullah Shah ◽  
Wasi Haider Butt
Keyword(s):  
Time Series ◽  
Social Network ◽  
Time Series Analysis ◽  
Series Analysis

scholarly journals Evolutionary Prediction of Nonstationary Event Popularity Dynamics of Weibo Social Network Using Time-Series Characteristics

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Xiaoliang Chen ◽  
Xiang Lan ◽  
Jihong Wan ◽  
Peng Lu ◽  
Ming Yang
Keyword(s):  
Time Series ◽  
Social Network ◽  
Prediction Model ◽  
Linear Models ◽  
Personalized Recommendation ◽  
Popularity Dynamics ◽  
Social Media Platforms ◽  
Popularity Prediction ◽  
Prediction Approach ◽  
Prediction Problems

A growing number of web users around the world have started to post their opinions on social media platforms and offer them for share. Building a highly scalable evolution prediction model by means of evolution trend volatility plays a significant role in the operations of enterprise marketing, public opinion supervision, personalized recommendation, and so forth. However, the historical patterns cannot cover the systematical time-series dynamic and volatility features in the prediction problems of a social network. This paper aims to investigate the popularity prediction problem from a time-series perspective utilizing dynamic linear models. First, the stationary and nonstationary time series of Weibo hot events are detected and transformed into time-dependent variables. Second, a systematic general popularity prediction model N- SEP 2 M is proposed to recognize and predict the nonstationary event propagation of a hot event on the Weibo social network. Third, the explanatory compensation variable social intensity (SI) is introduced to optimize the model N- SEP 2 M. Experiments on three Weibo hot events with different subject classifications show that our prediction approach is effective for the propagation of hot events with burst traffic.

scholarly journals Time Series Predictive Models for Social Networking Media Usage Data: The Pragmatics and Projections

2020 ◽  
pp. 37-54
Author(s):  
M. A. Jayaram ◽  
Gayitri Jayatheertha ◽  
Ritu Rajpurohit
Keyword(s):  
Time Series ◽  
Social Network ◽  
Social Networking ◽  
Predictive Models ◽  
Visual Analytics ◽  
Computer Applications ◽  
Media Usage ◽  
Error Metrics ◽  
Linear Polynomial ◽  
Network Media

Aims: We have set forth three main objectives in the work presented in this paper, they are namely, to study how social networking media usage is surging over the time for three social media networks viz., Facebook, Twitter and LinkedIn, ii.to develop best fitting time series predictive models for predicting future usage of three network media  and, iii. to make a comparative analysis to herald the ups and downs noticed in the usage across three network media considered. Study Design: Application of time series techniques for the analysis of social network user’s data. The main research question addressed by this work is to see how time series models augurs for time dependent data such as the one chosen in this research. Place and Duration of Study: Research Center, Department of Master of Computer Applications, Siddaganga Institute of Technology, Tumakuru, Karnataka, India, between January 2020- April 2020. Methodology: The work delved on collection three social network users (Facebook, LinkedIn, and Twitter) data for a span of nine years i.e., for the tenure 2011-2019. One dimensional, two dimensional and three dimensional visual analytics is made prior to time series analysis. Time series predictive analytics involved development of best fits for prediction. To select the best fits among linear, polynomial, exponential, power function and logarithmic models, mean absolute error and root mean square error metrics were used. Results: Linear, polynomial function trend lines proved to be the best for Facebook, LinkedIn and Twitter respectively with low values of MAE and RMSE and high values of regression coefficients as compared with other kinds of models. Apart from the error metrics, the Theil’s U-statistic values of 0.928, 1.008 and 1.21 for Facebook, Twitter and LinkedIn also heralded the fact that these functions are superior models when compared with other naïve models. It is also projected that by 2025, Facebook will see 10,000 billion, followed by LinkedIn at 1500 billion while Twitter would see 750 billion people if same kind of surge trend prevails in user numbers across three networks considered in this research. Conclusion: This paper presented a unique work which is supposedly deemed to be the first of its kind to the best of the knowledge of authors. The models come with a limitation that, they can provide accurate projection if the same trend prevails in the pattern of upheavals in usage.

Predicting Homophily and Social Network Connectivity From Dyadic Behavioral Similarity Trajectory Clusters

2020 ◽  
pp. 089443932092312
Author(s):  
Brandon Sepulvado ◽  
Michael Lee Wood ◽  
Ethan Fridmanski ◽  
Cheng Wang ◽  
Matthew J. Chandler ◽  
...  
Keyword(s):  
Physical Activity ◽  
Communication Network ◽  
Time Series ◽  
Social Network ◽  
Network Connectivity ◽  
Fine Grained ◽  
Behavioral Similarity ◽  
Ongoing Work ◽  
Gender Based ◽  
Shape Clustering

The similarity between pairs of people is often measured on relatively static traits and at a given point in time. Moving beyond this approach, a burgeoning line of research is investigating temporal dyadic similarity on traits and behaviors, such as health activities. Our study contributes to this line of inquiry by using fine-grained longitudinal data obtained from sensors, mobile devices, and surveys to examine whether we can observe distinct types of dyadic similarity trajectories based on physical activity, and if so, what dyad-level properties predict membership in each trajectory class. Treating daily differences in the steps for dyads as time series, we use k-shape clustering to identify classes of similarity trajectories and logistic regression to examine the link between trajectory class and key dyad-level factors. We identify 21 dyadic trajectory clusters and find that trajectory membership predicts dyadic connectivity in the communication network, as well as racial and religious—but not gender-based—similarity. We conclude by noting how research on dyadic similarity trajectories can be further integrated with ongoing work in social network analysis.

scholarly journals Cooperation and motivation in organizational networks: Analysis of structural configuration of social network in time series

2014 ◽  
Vol 25 (2) ◽  
pp. 186
Author(s):  
June Alisson Westarb Cruz ◽  
Carlos Olavo Quandt ◽  
Heitor Takashi Kato ◽  
Tomas Sparano Martins
Keyword(s):  
Time Series ◽  
Social Network ◽  
Organizational Networks ◽  
Structural Configuration ◽  
Networks Analysis
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