Passive network evolution promotes group welfare in complex networks

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.

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Growth and Optimality in Network Evolution

Artificial Life ◽

10.1162/artl_a_00039 ◽

2011 ◽

Vol 17 (4) ◽

pp. 281-291 ◽

Cited By ~ 5

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.

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Research on Threat Information Network Based on Link Prediction

International Journal of Digital Crime and Forensics ◽

10.4018/ijdcf.2021030106 ◽

2021 ◽

Vol 13 (2) ◽

pp. 94-102

Author(s):

Jin Du ◽

Feng Yuan ◽

Liping Ding ◽

Guangxuan Chen ◽

Xuehua Liu

Keyword(s):

Complex Networks ◽

Link Prediction ◽

Situational Awareness ◽

Prediction Method ◽

Interdisciplinary Approach ◽

Network Evolution ◽

Information Network ◽

Research Perspective ◽

Intrinsic Factors ◽

Node Similarity

The study of complex networks is to discover the characteristics of these connections and to discover the nature of the system between them. Link prediction method is a classic in the study of complex networks. It ca not only reflect the relationship between the node similarity. More can be estimated through the edge, which reveals the intrinsic factors of network evolution, namely the network evolution mechanism. Threat information network is the evolution and development of the network. The introduction of such a complex network of interdisciplinary approach is an innovative research perspective to observe that the threat intelligence occurs. The characteristics of the network show, at the same time, also can predict what will happen. The evolution of the network for network security situational awareness of the research provides a new approach.

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Deciphering the generating rules and functionalities of complex networks

Scientific Reports ◽

10.1038/s41598-021-02203-4 ◽

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.

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