Malicious users' circle detection in social network based on spatio-temporal co-occurrence

2011 ◽  
Author(s):  
Zahid Halim ◽  
Mian Maqsood Gul ◽  
Najam ul Hassan ◽  
Rauf Baig ◽  
Shafiq Ur Rehman ◽  
...  
Keyword(s):  
Social Network ◽  
Circle Detection ◽  
Spatio Temporal

A Novel Spatio-Temporal Access Control Model for Online Social Networks and Visual Verification

2021 ◽  
Vol 11 (2) ◽  
pp. 17-31
Author(s):  
Lanfang Zhang ◽  
Zhiyong Zhang ◽  
Ting Zhao
Keyword(s):  
Social Network ◽  
Access Control ◽  
Online Social Networks ◽  
Online Social Network ◽  
Rapid Development ◽  
Real Life ◽  
Control Model ◽  
Mobile Internet ◽  
Access Control Model ◽  
Spatio Temporal

With the rapid development of mobile internet, a large number of online social networking platforms and tools have been widely applied. As a classic method for protecting the privacy and information security of social users, access control technology is evolving with the spatio-temporal change of social application requirements and scenarios. However, nowadays there is a lack of effective theoretical model of social spatio-temporal access control as a guide. This paper proposed a novel spatio-temporal access control model for online social network (STAC) and its visual verification, combined with the advantages of discretionary access control, using formal language to describe the access control rules based on spatio-temporal, and real-life scenarios for access control policy description, realizes a more fine-grained access control mechanism for social network. By using the access control verification tool ACPT developed by NIST to visually verify the proposed model, the security and effectiveness of the STAC model are proved.

scholarly journals Social Network Discovery by Mining Spatio-Temporal Events

2005 ◽  
Vol 11 (2) ◽  
pp. 97-118 ◽  
Author(s):  
Hady W. Lauw ◽  
Ee-Peng Lim ◽  
HweeHwa Pang ◽  
Teck-Tim Tan
Keyword(s):  
Social Network ◽  
Network Discovery ◽  
Spatio Temporal ◽  
Temporal Events

scholarly journals Inferring social network structure in ecological systems from spatio-temporal data streams

2012 ◽  
Vol 9 (76) ◽  
pp. 3055-3066 ◽  
Author(s):  
Ioannis Psorakis ◽  
Stephen J. Roberts ◽  
Iead Rezek ◽  
Ben C. Sheldon
Keyword(s):  
Social Network ◽  
Network Structure ◽  
Temporal Dynamics ◽  
Rapid Development ◽  
Statistical Significance ◽  
Connectivity Pattern ◽  
Interacting Agents ◽  
Pair Bonds ◽  
Social Network Structure ◽  
Spatio Temporal

We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these ‘gathering events’. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix ) is not given a priori . We perform experiments on two large-scale datasets (greater than 10 6 points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents.

Recommendations based on a heterogeneous spatio-temporal social network

2017 ◽  
Vol 21 (2) ◽  
pp. 345-371 ◽  
Author(s):  
Pavlos Kefalas ◽  
Panagiotis Symeonidis ◽  
Yannis Manolopoulos
Keyword(s):  
Social Network ◽  
Spatio Temporal

scholarly journals A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

2020 ◽  
Vol 9 (12) ◽  
pp. 733
Author(s):  
Naimat Ullah Khan ◽  
Wanggen Wan ◽  
Shui Yu ◽  
A. A. M. Muzahid ◽  
Sajid Khan ◽  
...  
Keyword(s):  
Social Network ◽  
Human Behavior ◽  
Geographical Information Systems ◽  
Activity Patterns ◽  
Geographical Information ◽  
Time Frequency ◽  
City Dynamics ◽  
Location Based Social Network ◽  
Spatio Temporal ◽  
The City

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.

Sign in / Sign up

Export Citation Format

Share Document