Research on the Scale-Free Structure in Network Growth and Collapse(Strategic Soft Computing 2,Session: MP1-C)

Author(s):  
Yuumi KAWACHI ◽  
Shinichiro YOSHII ◽  
Yukinori KAKAZU
2021 ◽  
Author(s):  
Yuhu Qiu ◽  
Tianyang Lyu ◽  
Xizhe Zhang ◽  
Ruozhou Wang

Network decrease caused by the removal of nodes is an important evolution process that is paralleled with network growth. However, many complex network models usually lacked a sound decrease mechanism. Thus, they failed to capture how to cope with decreases in real life. The paper proposed decrease mechanisms for three typical types of networks, including the ER networks, the WS small-world networks and the BA scale-free networks. The proposed mechanisms maintained their key features in continuous and independent decrease processes, such as the random connections of ER networks, the long-range connections based on nearest-coupled network of WS networks and the tendency connections and the scale-free feature of BA networks. Experimental results showed that these mechanisms also maintained other topology characteristics including the degree distribution, clustering coefficient, average length of shortest-paths and diameter during decreases. Our studies also showed that it was quite difficult to find an efficient decrease mechanism for BA networks to withstand the continuous attacks at the high-degree nodes, because of the unequal status of nodes.


2006 ◽  
Vol 96 (21) ◽  
Author(s):  
Santo Fortunato ◽  
Alessandro Flammini ◽  
Filippo Menczer

2018 ◽  
Vol 5 (12) ◽  
pp. 181286 ◽  
Author(s):  
Bernat Corominas-Murtra ◽  
Martí Sànchez Fibla ◽  
Sergi Valverde ◽  
Ricard Solé

The emergence of syntax during childhood is a remarkable example of how complex correlations unfold in nonlinear ways through development. In particular, rapid transitions seem to occur as children reach the age of two, which seems to separate a two-word, tree-like network of syntactic relations among words from the scale-free graphs associated with the adult, complex grammar. Here, we explore the evolution of syntax networks through language acquisition using thechromatic number, which captures the transition and provides a natural link to standard theories on syntactic structures. The data analysis is compared to a null model of network growth dynamics which is shown to display non-trivial and sensible differences. At a more general level, we observe that the chromatic classes define independent regions of the graph, and thus, can be interpreted as the footprints of incompatibility relations, somewhat as opposed to modularity considerations.


2005 ◽  
Vol 2 (5) ◽  
pp. 533-536 ◽  
Author(s):  
Marcel Salathé ◽  
Robert M May ◽  
Sebastian Bonhoeffer

The topology of large social, technical and biological networks such as the World Wide Web or protein interaction networks has caught considerable attention in the past few years (reviewed in Newman 2003 ), and analysis of the structure of such networks revealed that many of them can be classified as broad-tailed, scale-free-like networks, since their vertex connectivities follow approximately a power-law. Preferential attachment of new vertices to highly connected vertices is commonly seen as the main mechanism that can generate scale-free connectivity in growing networks ( Watts 2004 ). Here, we propose a new model that can generate broad-tailed networks even in the absence of network growth, by not only adding vertices, but also selectively eliminating vertices with a probability that is inversely related to the sum of their first- and second order connectivity.


Author(s):  
Antonis Sidiropoulos ◽  
Dimitrios Katsaros ◽  
Yannis Manolopoulos

The World Wide Web, or simply Web, is a characteristic example of a social network (Newman, 2003; Wasserman & Faust, 1994). Other examples of social networks include the food web network, scientific collaboration networks, sexual relationships networks, metabolic networks, and air transportation networks. Socials networks are usually abstracted as graphs, comprised by vertices, edges (directed or not), and in some cases, with weights on these edges. Social network theory is concerned with properties related to connectivity (degree, structure, centrality), distances (diameter, shortest paths), “resilience” (geodesic edges or vertices, articulation vertices) of these graphs, models of network growth. Social networks have been studied long before the conception of the Web. Pioneering works for the characterization of the Web as a social network and for the study of its basic properties are due to the work of Barabasi and its colleagues (Albert, Jeong & Barabasi, 1999). Later, several studies investigated other aspects like its growth (Bianconi & Barabasi, 2001; Menczer, 2004; Pennock, Flake, Lawrence, Glover, & Giles, 2002; Watts & Strogatz, 1998), its “small-world” nature in that pages can reach other pages with only a small number of links, and its scale-free nature (Adamic & Huberman, 2000; Barabasi & Albert, 1999; Barabasi & Bonabeau, 2003) (i.e., a feature implying that it is dominated by a relatively small number of Web pages that are connected to many others; these pages are called hubs and have a seemingly unlimited number of hyperlinks). Thus, the distribution of Web page linkages follows a power law in that most nodes have just a few hyperlinks and some have a tremendous number of links In that sense, the system has no “scale” (see Figure 1).


2012 ◽  
Vol 22 (07) ◽  
pp. 1250159 ◽  
Author(s):  
PAU EROLA ◽  
JAVIER BORGE-HOLTHOEFER ◽  
SERGIO GOMEZ ◽  
ALEX ARENAS

Singular Value Decomposition (SVD) is a technique based on linear projection theory, which has been frequently used for data analysis. It constitutes an optimal (in the sense of least squares) decomposition of a matrix in the most relevant directions of the data variance. Usually, this information is used to reduce the dimensionality of the data set in a few principal projection directions, this is called Truncated Singular Value Decomposition (TSVD). In situations where the data is continuously changing, the projection might become obsolete. Since the change rate of data can be fast, it is an interesting question whether the TSVD projection of the initial data is reliable. In the case of complex networks, this scenario is particularly important when considering network growth. Here we study the reliability of the TSVD projection of growing scale-free networks, monitoring its evolution at global and local scales.


2008 ◽  
Vol 19 (04) ◽  
pp. 647-664 ◽  
Author(s):  
ANDRZEJ GECOW

We describe systems using Kauffman and similar networks. They are directed functioning networks consisting of finite number of nodes with finite number of discrete states evaluated in synchronous mode of discrete time. In this paper we introduce the notion and phenomenon of "structural tendencies". Along the way we expand Kauffman networks, which were a synonym of Boolean networks, to more than two signal variants and we find a phenomenon during network growth which we interpret as "complexity threshold". For simulation we define a simplified algorithm which allows us to omit the problem of periodic attractors. We estimate that living and human designed systems are chaotic (in Kauffman sense) which can be named — complex. Such systems grow in adaptive evolution. These two simple assumptions lead to certain statistical effects, i.e., structural tendencies observed in classic biology but still not explained and not investigated on theoretical way. For example, terminal modifications or terminal predominance of additions where terminal means: near system outputs. We introduce more than two equally probable variants of signal, therefore our networks generally are not Boolean networks. They grow randomly by additions and removals of nodes imposed on Darwinian elimination. Fitness is defined on external outputs of system. During growth of the system we observe a phase transition to chaos (threshold of complexity) in damage spreading. Above this threshold we identify mechanisms of structural tendencies which we investigate in simulation for a few different networks types, including scale-free BA networks.


2018 ◽  
Vol 29 (10) ◽  
pp. 1850099 ◽  
Author(s):  
Michelle T. Cirunay ◽  
Rene C. Batac

We present a statistical characterization of the morphological features emerging from the complex processes governing the growth of the road network, particularly in a mostly self-organized urban setting. Apart from the usual fractal analysis, the roads are quantified by their lengths and straightnesses, while the segmented blocks are characterized by their areas, perimeters and circularities. When applied to the Metro Manila conurbation, one of the megacities in Asia with the fastest growing populations, we observe dense space-filling and nontrivial statistical distributions of roads and blocks that can be attributed to the geographical constraints of the metropolis. The emergence of the scale-free regimes is explained using a simple rule-based model patterned after the assumed dynamical interplay between the local and global factors involved in individual street formation. By viewing road network growth from a quantitative complex systems perspective, we can gain insights into the underlying rules operating at the local scales that give rise to the global spatial patterns.


2021 ◽  
Vol 35 (24) ◽  
Author(s):  
Sen Qin ◽  
Sha Peng

Considering the retarding effect of natural resources, environmental conditions, and other factors on network growth, the capacity of network nodes to connect to new edges is generally limited. Inspired by this hindered growth of many real-world networks, two types of evolving network models are suggested with different logistic growth schemes. In the global and local logistic network, the total number of network edges and the number of edges added into the network at each step are in line with the Logistic growth, respectively. The most exciting feature of the Logistic growth network is that the growth rule of network edges is first fast, then slow and finally reaches the saturation value [Formula: see text]. Theoretical analysis and numerical simulation reveal that the node degrees of two new networks converge to the same results of the BA scale-free network, [Formula: see text], as the growth rate [Formula: see text] approaches to 0. The local logistic network follows a bilateral power-law degree distribution with a given value of [Formula: see text]. Meanwhile, for these two networks, it is found that the greater [Formula: see text] and [Formula: see text], the smaller the average shortest paths, the greater the clustering coefficients, and the weaker the disassortativity. Additionally, compared to the local logistic growth network, the clustering feature of the global logistic network is more obvious.


2015 ◽  
Vol 26 (09) ◽  
pp. 1550097 ◽  
Author(s):  
A. M. Dos Santos ◽  
M. L. De Almeida ◽  
G. A. Mendes ◽  
L. R. Da Silva

We propose a simple network growth process where the preferential attachment contains two essential parameters: homophily, namely, the tendency of sites to link with similar ones, and the number of attaching neighbors. It jointly generalizes the Barabási–Albert model and the scale-free homophilic model with a control parameter which tunes the importance of the homophily on preferential attachment process. Our results support a detailed discussion about different kinds of correlation, in special a fitness correlation introduced in this paper, and comparisons between BA model, scale-free homophilic model, and our present model considering its topological properties: degree distribution, time dependence of the connectivity and clustering coefficient.


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