Community Detection Using Synthetic Coordinates and Flow Propagation

2016 ◽  
pp. 579-636
Author(s):  
Paraskevi Fragopoulou ◽  
Harris Papadakis ◽  
Costas Panagiotakis
Keyword(s):  
Community Detection ◽  
Flow Propagation

145 Color M-mode flow propagation velocity — a preload independent index of diastolic function

2003 ◽  
Vol 2 (1) ◽  
pp. 24-25
Author(s):  
A ILIESIU ◽  
T NANEA ◽  
D VINEREANU ◽  
C NICOLAE ◽  
A DEUTSCH
Keyword(s):  
Diastolic Function ◽  
Propagation Velocity ◽  
Flow Propagation Velocity ◽  
Flow Propagation

Fast Uncovering of Graph Communities on a Chip: Toward Scalable Community Detection on Multicore and Manycore Platforms

2016 ◽  
Author(s):  
Ananth Kalyanaraman ◽  
Mahantesh Halappanavar ◽  
Daniel Chavarría-Miranda ◽  
Hao Lu ◽  
Karthi Duraisamy
Keyword(s):  
Community Detection

Research on community detection in social network

2014 ◽  
Vol 31 (1) ◽  
pp. 35
Author(s):  
Na Wang ◽  
Xia Li ◽  
Hongying Xu
Keyword(s):  
Social Network ◽  
Community Detection

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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