The Construction and Analysis of Music Influence Graph

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

scholarly journals Context-Aware Community Construction in Proximity-Based Mobile Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Na Yu ◽  
Qi Han
Keyword(s):  
Social Networking ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Context Aware ◽  
Two Phase ◽  
Space And Time ◽  
Shared Information ◽  
Influence Graph ◽  
Social Applications ◽  
Share Information

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.

Exploring Disease Association from the NHANES Data

2011 ◽  
pp. 157-173
Author(s):  
Zhengzheng Xing ◽  
Jian Pei
Keyword(s):  
Pattern Mining ◽  
Disease Association ◽  
Medical Data ◽  
Mining Method ◽  
Medical Data Mining ◽  
Data Set ◽  
Hierarchical Tree ◽  
Disease Associations ◽  
Disease Pattern ◽  
Influence Graph

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.

Eiffel: Evolutionary Flow Map for Influence Graph Visualization

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

VEGAS: Visual influEnce GrAph Summarization on Citation Networks

2015 ◽  
Vol 27 (12) ◽  
pp. 3417-3431 ◽  
Author(s):  
Lei Shi ◽  
Hanghang Tong ◽  
Jie Tang ◽  
Chuang Lin
Keyword(s):  
Citation Networks ◽  
Influence Graph ◽  
Visual Influence ◽  
Graph Summarization

scholarly journals A Markovian Influence Graph Formed From Utility Line Outage Data to Mitigate Large Cascades

2020 ◽  
Vol 35 (4) ◽  
pp. 3224-3235 ◽  
Author(s):  
Kai Zhou ◽  
Ian Dobson ◽  
Zhaoyu Wang ◽  
Alexander Roitershtein ◽  
Arka P. Ghosh
Keyword(s):  
Influence Graph

On the size of a random sphere of influence graph

1999 ◽  
Vol 31 (3) ◽  
pp. 596-609 ◽  
Author(s):  
T. K. Chalker ◽  
A. P. Godbole ◽  
P. Hitczenko ◽  
J. Radcliff ◽  
O. G. Ruehr
Keyword(s):  
Upper And Lower Bounds ◽  
Exponential Inequality ◽  
Nearest Neighbour ◽  
Expected Number ◽  
Spheres Of Influence ◽  
Influence Graph ◽  
Influence Graphs ◽  
Sphere Of Influence ◽  
Proximity Graph ◽  
Sharp Concentration

We approach sphere of influence graphs (SIGs) from a probabilistic perspective. Ordinary SIGs were first introduced by Toussaint as a type of proximity graph for use in pattern recognition, computer vision and other low-level vision tasks. A random sphere of influence graph (RSIG) is constructed as follows. Consider n points uniformly and independently distributed within the unit square in d dimensions. Around each point, Xi, draw an open ball (‘sphere of influence’) with radius equal to the distance to Xi's nearest neighbour. Finally, draw an edge between two points if their spheres of influence intersect. Asymptotically exact values for the expected number of edges in a RSIG are determined for all values of d; previously, just upper and lower bounds were known for this quantity. A modification of the Azuma-Hoeffding exponential inequality is employed to exhibit the sharp concentration of the number of edges around its expected value.

REMARKS ON THE SPHERE OF INFLUENCE GRAPH

1985 ◽  
Vol 440 (1 Discrete Geom) ◽  
pp. 323-327 ◽  
Author(s):  
David Avis ◽  
Joe Horton
Keyword(s):  
Influence Graph ◽  
Sphere Of Influence
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