Distributed Subgraph Matching Privacy Preserving Method for Dynamic Social Network

2019 ◽  
pp. 159-173
Author(s):  
Xiao-Lin Zhang ◽  
Hao-chen Yuan ◽  
Zhuo-lin Li ◽  
Huan-xiang Zhang ◽  
Jian Li
Keyword(s):  
Social Network ◽  
Privacy Preserving ◽  
Subgraph Matching ◽  
Dynamic Social Network

Finding Communities and Their Evolutions in Dynamic Social Network

2012 ◽  
Author(s):  
Tianbao Yang ◽  
Shenghuo Zhu ◽  
Yun Chi ◽  
Yihong Gong
Keyword(s):  
Social Network ◽  
Dynamic Social Network

Evolution pattern mining on dynamic social network

2021 ◽  
Author(s):  
Guan-Yi Jheng ◽  
Yi-Cheng Chen ◽  
Hung-Ming Liang
Keyword(s):  
Social Network ◽  
Pattern Mining ◽  
Dynamic Social Network ◽  
Evolution Pattern

Limitation of degree information for analyzing the interaction evolution in online social networks

2014 ◽  
Vol 25 (10) ◽  
pp. 1450056 ◽  
Author(s):  
Ke-Ke Shang ◽  
Wei-Sheng Yan ◽  
Xiao-Ke Xu
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Services ◽  
Online Social Networks ◽  
Dynamic Network ◽  
Interaction Network ◽  
Strength Distribution ◽  
Customer Churn ◽  
Dynamic Social Network ◽  
Temporal Interactions

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

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