Fractal Operator Convergence by Analysis of Influence Graph

Author(s):  
Władysław Skarbek
2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Na Yu ◽  
Qi Han

Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.


2021 ◽  
Author(s):  
Shuyuan Liu ◽  
Xinyan Zhou ◽  
Feiyan Duan ◽  
Hansen Yang
Keyword(s):  

Author(s):  
Zhengzheng Xing ◽  
Jian Pei

Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.


2020 ◽  
Vol 26 (10) ◽  
pp. 2944-2960 ◽  
Author(s):  
Yucheng Huang ◽  
Lei Shi ◽  
Yue Su ◽  
Yifan Hu ◽  
Hanghang Tong ◽  
...  

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 685
Author(s):  
Qiu Jin ◽  
Lingqiang Li ◽  
Jing Jiang

Fischer diagonal condition plays an important role in convergence space since it precisely ensures a convergence space to be a topological space. Generally, Fischer diagonal condition can be represented equivalently both by Kowalsky compression operator and Gähler compression operator. ⊤-convergence spaces are fundamental fuzzy extensions of convergence spaces. Quite recently, by extending Gähler compression operator to fuzzy case, Fang and Yue proposed a fuzzy counterpart of Fischer diagonal condition, and proved that ⊤-convergence space with their Fischer diagonal condition just characterizes strong L-topology—a type of fuzzy topology. In this paper, by extending the Kowalsky compression operator, we present a fuzzy counterpart of Fischer diagonal condition, and verify that a ⊤-convergence space with our Fischer diagonal condition precisely characterizes topological generated L-topology—a type of fuzzy topology. Hence, although the crisp Fischer diagonal conditions based on the Kowalsky compression operator and the on Gähler compression operator are equivalent, their fuzzy counterparts are not equivalent since they describe different types of fuzzy topologies. This indicates that the fuzzy topology (convergence) is more complex and varied than the crisp topology (convergence).


2015 ◽  
Vol 27 (12) ◽  
pp. 3417-3431 ◽  
Author(s):  
Lei Shi ◽  
Hanghang Tong ◽  
Jie Tang ◽  
Chuang Lin

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