scholarly journals BANANA: when Behavior ANAlysis meets social Network Alignment

2020 ◽  
Author(s):  
Fuxin Ren ◽  
Zhongbao Zhang ◽  
Jiawei Zhang ◽  
Sen Su ◽  
Li Sun ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
User Behavior ◽  
Network Alignment ◽  
Learning Performance ◽  
User Behavior Analysis ◽  
The Social ◽  
End To End ◽  
Real World Datasets

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

scholarly journals A User Identification Algorithm Based on User Behavior Analysis in Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47114-47123 ◽  
Author(s):  
Kaikai Deng ◽  
Ling Xing ◽  
Longshui Zheng ◽  
Honghai Wu ◽  
Ping Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Behavior Analysis ◽  
User Behavior ◽  
Identification Algorithm ◽  
User Identification ◽  
User Behavior Analysis

Social network based microblog user behavior analysis

2013 ◽  
Vol 392 (7) ◽  
pp. 1712-1723 ◽  
Author(s):  
Qiang Yan ◽  
Lianren Wu ◽  
Lan Zheng
Keyword(s):  
Social Network ◽  
Behavior Analysis ◽  
User Behavior ◽  
User Behavior Analysis

scholarly journals A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Rongmei Zhao ◽  
Xi Xiong ◽  
Xia Zu ◽  
Shenggen Ju ◽  
Zhongzhi Li ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation System ◽  
Information Overload ◽  
User Behavior ◽  
Recommendation Systems ◽  
Social Domain ◽  
Network Information ◽  
Cross Domain ◽  
Real World Datasets

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.

Emotive Trending And Tracking of Tweets

2017 ◽  
Vol 7 (11) ◽  
pp. 25
Author(s):  
Sanjay Chhataru Gupta
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
The Social ◽  
The World ◽  
Complex Events

Popularity of the social media and the amount of importance given by an individual to social media has significantly increased in last few years. As more and more people become part of the social networks like Twitter, Facebook, information which flows through the social network, can potentially give us good understanding about what is happening around in our locality, state, nation or even in the world. The conceptual motive behind the project is to develop a system which analyses about a topic searched on Twitter. It is designed to assist Information Analysts in understanding and exploring complex events as they unfold in the world. The system tracks changes in emotions over events, signalling possible flashpoints or abatement. For each trending topic, the system also shows a sentiment graph showing how positive and negative sentiments are trending as the topic is getting trended.

Sign in / Sign up

Export Citation Format

Share Document