scholarly journals Deciphering the generating rules and functionalities of complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiongye Xiao ◽  
Hanlong Chen ◽  
Paul Bogdan
Keyword(s):  
Fractal Dimension ◽  
Complex Networks ◽  
Network Theory ◽  
Multifractal Analysis ◽  
Network Evolution ◽  
Networked Systems ◽  
Technological Systems ◽  
Structural Interactions ◽  
Complex Topology ◽  
Multiscale Structures

AbstractNetwork theory helps us understand, analyze, model, and design various complex systems. Complex networks encode the complex topology and structural interactions of various systems in nature. To mine the multiscale coupling, heterogeneity, and complexity of natural and technological systems, we need expressive and rigorous mathematical tools that can help us understand the growth, topology, dynamics, multiscale structures, and functionalities of complex networks and their interrelationships. Towards this end, we construct the node-based fractal dimension (NFD) and the node-based multifractal analysis (NMFA) framework to reveal the generating rules and quantify the scale-dependent topology and multifractal features of a dynamic complex network. We propose novel indicators for measuring the degree of complexity, heterogeneity, and asymmetry of network structures, as well as the structure distance between networks. This formalism provides new insights on learning the energy and phase transitions in the networked systems and can help us understand the multiple generating mechanisms governing the network evolution.

scholarly journals The Role of Movement Patterns in Epidemic Models on Complex Networks

2021 ◽  
Vol 83 (10) ◽  
Author(s):  
Alfonso Ruiz-Herrera ◽  
Pedro J. Torres
Keyword(s):  
Complex Networks ◽  
Network Theory ◽  
Diffusion Rate ◽  
Critical Role ◽  
Movement Patterns ◽  
Epidemic Models ◽  
Simplifying Assumptions ◽  
The Common

AbstractIn this paper, we analyze the influence of the usual movement variables on the spread of an epidemic. Specifically, given two spatial topologies, we can deduce which topology produces less infected individuals. In particular, we determine the topology that minimizes the overall number of infected individuals. It is worth noting that we do not assume any of the common simplifying assumptions in network theory such as all the links have the same diffusion rate or the movement of the individuals is symmetric. Our main conclusion is that the degree of mobility of the population plays a critical role in the spread of a disease. Finally, we derive theoretical insights to management of epidemics.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

scholarly journals Box-covering algorithm for fractal dimension of complex networks

2012 ◽  
Vol 86 (1) ◽  
Author(s):  
Christian M. Schneider ◽  
Tobias A. Kesselring ◽  
José S. Andrade ◽  
Hans J. Herrmann
Keyword(s):  
Fractal Dimension ◽  
Complex Networks

scholarly journals Dynamics of social network emergence explain network evolution

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Caleb Pomeroy ◽  
Robert M. Bond ◽  
Peter J. Mucha ◽  
Skyler J. Cranmer
Keyword(s):  
Social Network ◽  
Scientific Collaboration ◽  
Behavioral Outcomes ◽  
Network Evolution ◽  
Networked Systems ◽  
Rapid Expansion ◽  
Survey Results ◽  
Simulation Results ◽  
Initial Process ◽  
Initial Cycle

AbstractNetworked systems emerge and subsequently evolve. Although several models describe the process of network evolution, researchers know far less about the initial process of network emergence. Here, we report temporal survey results of a real-world social network starting from its point of inception. We find that individuals’ ties undergo an initial cycle of rapid expansion and contraction. This process helps to explain the eventual interactions and working structure in the network (in this case, scientific collaboration). We propose a stylized concept and model of “churn” to describe the process of network emergence and stabilization. Our empirical and simulation results suggest that these network emergence dynamics may be instrumental for explaining network details, as well as behavioral outcomes at later time periods.

scholarly journals The evolution of political networks: Evidence from the Council of the European Union

2017 ◽  
Vol 19 (1) ◽  
pp. 25-51 ◽  
Author(s):  
Narisong Huhe ◽  
Daniel Naurin ◽  
Robert Thomson
Keyword(s):  
Decision Making ◽  
European Union ◽  
Network Theory ◽  
Network Evolution ◽  
The European Union ◽  
Unique Combination ◽  
Policy Positions ◽  
Political Actors ◽  
Political Networks ◽  
Over Time

We test two of the main explanations of the formation of political ties. The first states that political actors are more likely to form a relationship if they have similar policy preferences. The second explanation, from network theory, predicts that the likelihood of a tie between two actors depends on the presence of certain relationships with other actors. Our data consist of a unique combination of actors' policy positions and their network relations over time in the Council of the European Union. We find evidence that both types of explanations matter, although there seems to be variation in the extent to which preference similarity affects network evolution. We consider the implications of these findings for understanding the decision-making in the Council.

INFORMATION ASYMMETRY FLOWING IN COMPLEX NETWORKS

2012 ◽  
Vol 26 (31) ◽  
pp. 1250183
Author(s):  
CHEN-XI SHAO ◽  
HUI-LING DOU ◽  
BING-HONG WANG
Keyword(s):  
Symmetry Breaking ◽  
Complex Networks ◽  
Information Asymmetry ◽  
Complex Network ◽  
Information Flow ◽  
Weak Link ◽  
Network Evolution ◽  
Long Tail ◽  
Network Nodes ◽  
Concept Of Information

The concept of information asymmetry in complex networks is introduced on the basis of information asymmetry in economics and symmetry breaking. Information flowing between two nodes on a link is bidirectional, whose size is closely related to traffic dynamics on the network. Based on asymmetric information theory, we proposed information flow between network nodes is asymmetrical. We designed two methods to calculate the amount of information flow based on two mechanisms of complex network. Unequal flow of two opposite directions on the same link proved information asymmetry exists in the complex network. A complex network evolution model based on symmetry breaking is established, which is a truthful example for complex network mimicking nature. The evolution mechanism of symmetry breaking can best explain the phenomenon of the weak link and long tail theory in complex network.

scholarly journals Growth and Optimality in Network Evolution

2011 ◽  
Vol 17 (4) ◽  
pp. 281-291 ◽  
Author(s):  
Markus Brede
Keyword(s):  
Complex Networks ◽  
Time Scales ◽  
Power Law ◽  
Path Length ◽  
Network Evolution ◽  
Hierarchical Organization ◽  
Optimization Process ◽  
Assembly Process ◽  
Rich Variety ◽  
Mixing Patterns

We investigate networks whose evolution is governed by the interaction of a random assembly process and an optimization process. In the first process, new nodes are added one at a time and form connections to randomly selected old nodes. In between node additions, the network is rewired to minimize its path length. For time scales at which neither the assembly nor the optimization processes are dominant, we find a rich variety of complex networks with power law tails in the degree distributions. These networks also exhibit nontrivial clustering, a hierarchical organization, and interesting degree-mixing patterns.

scholarly journals Classification of texture using random box counting and binarization methods

2021 ◽  
Vol 10 (1) ◽  
pp. 533-540
Author(s):  
Wijdan Jaber AL-kubaisy ◽  
Maha Mahmood
Keyword(s):  
Image Processing ◽  
Fractal Dimension ◽  
Blood Cells ◽  
Multifractal Analysis ◽  
Vision System ◽  
White Blood Cells ◽  
Real Life ◽  
Self Similarity ◽  
Box Counting

The heterogeneous texture classifications with the complexity of structures provide variety of possibilities in image processing, as an example of the multifractal analysis features. The task of texture analysis is a highly significant field of study in the field of machine vision. Most of the real-life surfaces exhibit textures and an efficiently modelled vision system must have the ability to deal with this variety of surfaces. A considerable number of surfaces maintain a self-similarity quality as well as statistical roughness at different scales. Fractals could provide a great deal of advantages; also, they are popular in the process of modelling these properties in the tasks related to the field of image processing. With two distinct methods, this paper presents classification of texture using random box counting and binarization methods calculate the estimation measures of the fractal dimension BCM. There methods are the banalization and random selecting boxes. The classification of the white blood cells is presented in this paper based on the texture if it is normal or abnormal with the use of a number of various methods.

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