A New Method for Extracting the Hierarchical Organization of Networks

2017 ◽  
Vol 16 (05) ◽  
pp. 1359-1385 ◽  
Author(s):  
Weihua Zhan ◽  
Jihong Guan ◽  
Zhongzhi Zhang
Keyword(s):  
Complex Networks ◽  
Networked Systems ◽  
Hierarchical Organization ◽  
New Method ◽  
Network Data ◽  
Large Complex

Extracting the hierarchical organization of networks is currently a pressing task for understanding complex networked systems. The hierarchy of a network is essentially defined by the heterogeneity of link densities of communities at different scales. Here, we define a top-level partition (TLP) as a bipartition of the network (or a sub-network) such that no top-level community (TLC) runs across the two parts. It has been found that a TLP generally has a higher modularity than a non-top-level (TLP) partition when their TLCs have similar sizes and when the link densities of neighboring levels are well separated from each other. A spectral TLP procedure is proposed here to search for TLPs of a network (or sub-network). To extract the hierarchical organization of large complex networks, an algorithm called TLPA has been developed based on the TLP. Experiments have shown that the method developed in this research extract hierarchy accurately from network data.

scholarly journals A new method to reduce overestimation of thresholds with observational network data

2017 ◽  
Author(s):  
George Berry ◽  
Christopher John Cameron
Keyword(s):  
Measurement Error ◽  
Complex Networks ◽  
Observational Data ◽  
New Method ◽  
Cascading Failure ◽  
Network Data ◽  
Product Adoption ◽  
Activation Threshold ◽  
Bandwagon Effects ◽  
Ecosystem Collapse

Networks of interdependent nodes support phenomena such as epidemics, product adoption, cascading failure, ecosystem collapse, congestion, and bandwagon effects. We consider the problem of using observational data to estimate the sensitivity of individual nodes to the activation of their network neighbors. We prove that—in the case of binary activation decisions—activation thresholds are impossible to correctly measure for some nodes in virtually all contagion processes on complex networks. This result holds even when each step of the process is observed. Measurement error always produces an overestimate of a node's true activation threshold. We develop a condition for determining which node thresholds are correctly measured and demonstrate that modeling activation thresholds as a function of node-level factors reduces the error compared to existing approaches.

scholarly journals p-adic numbers encode complex networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Hua ◽  
Ludger Hovestadt
Keyword(s):  
Complex Networks ◽  
Random Graph ◽  
Genetic Interaction ◽  
Hierarchical Structures ◽  
Number System ◽  
Hierarchical Organization ◽  
Natural Representation ◽  
Multimodal Distributions ◽  
Number Sequences ◽  
Er Model

AbstractThe Erdős-Rényi (ER) random graph G(n, p) analytically characterizes the behaviors in complex networks. However, attempts to fit real-world observations need more sophisticated structures (e.g., multilayer networks), rules (e.g., Achlioptas processes), and projections onto geometric, social, or geographic spaces. The p-adic number system offers a natural representation of hierarchical organization of complex networks. The p-adic random graph interprets n as the cardinality of a set of p-adic numbers. Constructing a vast space of hierarchical structures is equivalent for combining number sequences. Although the giant component is vital in dynamic evolution of networks, the structure of multiple big components is also essential. Fitting the sizes of the few largest components to empirical data was rarely demonstrated. The p-adic ultrametric enables the ER model to simulate multiple big components from the observations of genetic interaction networks, social networks, and epidemics. Community structures lead to multimodal distributions of the big component sizes in networks, which have important implications in intervention of spreading processes.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization

2013 ◽  
Vol 380-384 ◽  
pp. 2695-2698
Author(s):  
Cai Tian Zhang ◽  
Yi Bo Zhang
Keyword(s):  
Signal Detection ◽  
Expectation Maximization ◽  
Detection Method ◽  
Expectation Maximization Algorithm ◽  
Gaussian Mixture ◽  
Chaotic Synchronization ◽  
New Method ◽  
Network Data ◽  
Network Intrusion ◽  
Better Than

For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.

Sign in / Sign up

Export Citation Format

Share Document