Growing network models: the Barabási–Albert model and its variants

2013 ◽  
pp. 51-62
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Network Models ◽  
Growing Network

Algorithms for Maximum Leaf Spanning Tress of Edge-Growing Network Models in Information System

2015 ◽  
Vol 775 ◽  
pp. 431-435
Author(s):  
Xiao Min Wang ◽  
Xi Yang Zhao ◽  
Bing Yao ◽  
Ming Yao ◽  
Xiang En Chen
Keyword(s):  
Information System ◽  
Spanning Trees ◽  
System Modeling ◽  
Network Models ◽  
Scale Free ◽  
Modeling Simulation ◽  
Simulation Techniques ◽  
Initial Network ◽  
Free Behavior ◽  
Growing Network

For system modeling, simulation techniques and sensors networks the edge-growing network models with any connected initial network model are introduced in this paper. We design some algorithms for finding Maximum Leaf Spanning Trees (MLS-trees) of the models. Our algorithms can find the MLS-trees having scale-free behavior and short diameters. These algorithms are designed by techniques including big-degree-first level-searching and first-first level-searching in order to scan the desired spanning trees.

scholarly journals On Evolution Properties of E-bound Growing Network Models

2015 ◽  
Author(s):  
Xia Liu ◽  
Bing Yao ◽  
Xiaomin Wang ◽  
Xiyang Zhao ◽  
Mingjun Zhang
Keyword(s):  
Network Models ◽  
Growing Network

Research on Edge-Growing Models Related with Scale-Free Small-World Networks

2014 ◽  
Vol 513-517 ◽  
pp. 2444-2448 ◽  
Author(s):  
Bing Yao ◽  
Ming Yao ◽  
Xiang En Chen ◽  
Xia Liu ◽  
Wan Jia Zhang
Keyword(s):  
Complex Networks ◽  
Topological Structure ◽  
Spanning Trees ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Scale Free ◽  
Scale Free Networks ◽  
Growing Network

Understanding the topological structure of scale-free networks or small world networks is required and useful for investigation of complex networks. We will build up a class of edge-growing network models and provide an algorithm for finding spanning trees of edge-growing network models in this article.

The Structure of Single Networks

2018 ◽  
Author(s):  
Ginestra Bianconi
Keyword(s):  
Community Detection ◽  
Random Graphs ◽  
Network Structure ◽  
Reference Point ◽  
Network Models ◽  
Relevant Information ◽  
Detection Methods ◽  
Centrality Measures ◽  
Network Ensembles ◽  
Growing Network

Chapters 2–3 constitute Part II of the book, ‘Single Networks’, and provide a reference point for the rest of the book devoted exclusively to Multilayer Networks, making the book self-contained. This chapter provides the relevant background on the network structure of complex networks formed by just one layer (single networks). Here the basic definitions of network structure are given, the major network universalities are presented and methods to extract relevant information from network structure including centrality measures and community detection methods are discussed. Finally, modelling frameworks are introduced including random graphs, growing network models (including notably the Barabasi–Albert Model) and network ensembles.

Topological Structure and Spanning Trees of Rectangular Growing Network Models

2014 ◽  
Vol 644-650 ◽  
pp. 1805-1808 ◽  
Author(s):  
Xia Liu ◽  
Bing Yao ◽  
Wan Jia Zhang ◽  
Xiang En Chen ◽  
Xiao Min Zhang
Keyword(s):  
Topological Structure ◽  
Spanning Trees ◽  
Network Models ◽  
Scale Free ◽  
Statistical Sense ◽  
Growing Network

Fractals in building network models are objects which appear similar (at least in some statistical sense) at every lengthscale. We focus on produce the rectangular growing network models by constructive operations based on fractals, and show the scale-free behaviors of our models. We, also, try to find those nodes like “hubs” in the models by spanning trees having maximal leaves.

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