A Tensor Factorization Based User Influence Analysis Method with Clustering and Temporal Constraint

2018 ◽  
pp. 877-886 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Lin Gui ◽  
Kam-Fai Wong ◽  
Guolong Chen
Keyword(s):  
Tensor Factorization ◽  
Analysis Method ◽  
Temporal Constraint ◽  
Influence Analysis ◽  
User Influence

User Influence Analysis Based on Blogs

2016 ◽  
Author(s):  
Xiang LIU ◽  
Yan JIA ◽  
Rong JIANG ◽  
Yong QUAN
Keyword(s):  
Influence Analysis ◽  
User Influence

scholarly journals Recommending Mobile Microblog Users via a Tensor Factorization Based on User Cluster Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangwen Liao ◽  
Lingying Zhang ◽  
Jingjing Wei ◽  
Dingda Yang ◽  
Guolong Chen
Keyword(s):  
Network Centrality ◽  
Network Clustering ◽  
Tensor Factorization ◽  
Latent Factors ◽  
Cp Decomposition ◽  
Temporal Features ◽  
User Influence ◽  
Proposed Model ◽  
The Mean ◽  
Content Similarity

User influence is a very important factor for microblog user recommendation in mobile social network. However, most existing user influence analysis works ignore user’s temporal features and fail to filter the marketing users with low influence, which limits the performance of recommendation methods. In this paper, a Tensor Factorization based User Cluster (TFUC) model is proposed. We firstly identify latent influential users by neural network clustering. Then, we construct a features tensor according to latent influential user’s opinion, activity, and network centrality information. Furthermore, user influences are predicted by the latent factors resulting from the temporal restrained CP decomposition. Finally, we recommend microblog users considering both user influence and content similarity. Our experimental results show that the proposed model significantly improves recommendation performance. Meanwhile, the mean average precision of TFUC outperforms the baselines with 3.4% at least.

scholarly journals Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Yufei Liu ◽  
Dechang Pi ◽  
Lin Cui
Keyword(s):  
Social Network ◽  
Real World ◽  
Social Trust ◽  
Community Level ◽  
Analysis Model ◽  
Sina Weibo ◽  
Influence Analysis ◽  
Community Influence ◽  
User Influence ◽  
The Individual

Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

Research Notes: User Influence in Microblog Based on Interest Graph

2018 ◽  
Vol 28 (02) ◽  
pp. 207-217 ◽  
Author(s):  
Lidong Zhai ◽  
Jingya Wang ◽  
Chaojian Hu ◽  
Jun Li
Keyword(s):  
Social Relationship ◽  
Great Influence ◽  
User Interaction ◽  
Label Propagation ◽  
User Interest ◽  
Influence Analysis ◽  
Pagerank Algorithm ◽  
User Influence ◽  
The Social ◽  
Relationship Of

Microblog is currently the largest social networking platform in China. In recent years, as a social media, the influence of microblog continues to expand. The users who have large influence play a guiding role in the spread of microblog, and even guide the trends of public opinion. Therefore, we propose an influence analysis method to find microblog users who are with great influence, which is of great significance for the research and mining of microblog. User influence analysis in microblog has great difficulties due to the limited amount of microblog information, quick updates and nonstandard microblog language. First, we use the label propagation algorithm combined with LDA algorithm to divide users by the user interest graph, according to the social relationship of microblog users and the content they generate. Then, depending on different interest areas, an improved PageRank algorithm based on user interaction behavior is proposed to calculate the user’s influence. Experiments on the real datasets show that the proposed method outperforms the traditional algorithms.

Analysis of User Influence Using User Behavior and Random Walk

2014 ◽  
Vol 571-572 ◽  
pp. 1163-1167
Author(s):  
Meng Wang ◽  
Gang Zhou ◽  
Jun Yu Chen
Keyword(s):  
Random Walk ◽  
Network Topology ◽  
User Behavior ◽  
New Method ◽  
Influence Analysis ◽  
Pagerank Algorithm ◽  
Individual Attribute ◽  
Random Walk Method ◽  
Interactive Behavior ◽  
User Influence

To improve the effect of user influence predicting in microblog, this paper proposed a new method of user influence analysis named UBRWR, based on user interactive behavior and the random walk method. UBRWR firstly used the behaviors between users to reconstruct the network topology, and then an improved PageRank algorithm was applied to predict the user influence, with quantized individual attribute features. The experimental in Weiba show that the UBRWR algorithm outperforms the PageRank algorithm and method using fans count in terms of ranking accuracy.

scholarly journals An Influence Analysis Method of Wake Turbulence in V-open Runway Operation Mode

2019 ◽  
Author(s):  
Tian-xu LIU ◽  
Run-qing ZHANG ◽  
Yang MIN ◽  
Le XU ◽  
Xin-yi ZHANG ◽  
...  
Keyword(s):  
Operation Mode ◽  
Analysis Method ◽  
Influence Analysis ◽  
Wake Turbulence
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