Detecting Hierarchical Organization in Complex Networks by Nearest Neighbor Correlation

2008 ◽  
pp. 487-494 ◽  
Author(s):  
Chao Long Wang ◽  
Ka Wai Au ◽  
Ching King Chan ◽  
Hon Wai Lau ◽  
K. Y. Szeto
Keyword(s):  
Complex Networks ◽  
Nearest Neighbor ◽  
Hierarchical Organization

Efficient detection of hacker community based on twitter data using complex networks and machine learning algorithm

2021 ◽  
pp. 1-17
Author(s):  
Ahmed Al-Tarawneh ◽  
Ja’afer Al-Saraireh
Keyword(s):  
Machine Learning ◽  
Complex Networks ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Efficient Detection ◽  
Suggested Keywords

Twitter is one of the most popular platforms used to share and post ideas. Hackers and anonymous attackers use these platforms maliciously, and their behavior can be used to predict the risk of future attacks, by gathering and classifying hackers’ tweets using machine-learning techniques. Previous approaches for detecting infected tweets are based on human efforts or text analysis, thus they are limited to capturing the hidden text between tweet lines. The main aim of this research paper is to enhance the efficiency of hacker detection for the Twitter platform using the complex networks technique with adapted machine learning algorithms. This work presents a methodology that collects a list of users with their followers who are sharing their posts that have similar interests from a hackers’ community on Twitter. The list is built based on a set of suggested keywords that are the commonly used terms by hackers in their tweets. After that, a complex network is generated for all users to find relations among them in terms of network centrality, closeness, and betweenness. After extracting these values, a dataset of the most influential users in the hacker community is assembled. Subsequently, tweets belonging to users in the extracted dataset are gathered and classified into positive and negative classes. The output of this process is utilized with a machine learning process by applying different algorithms. This research build and investigate an accurate dataset containing real users who belong to a hackers’ community. Correctly, classified instances were measured for accuracy using the average values of K-nearest neighbor, Naive Bayes, Random Tree, and the support vector machine techniques, demonstrating about 90% and 88% accuracy for cross-validation and percentage split respectively. Consequently, the proposed network cyber Twitter model is able to detect hackers, and determine if tweets pose a risk to future institutions and individuals to provide early warning of possible attacks.

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 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.

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.

HIERARCHICAL ORGANIZATION AND DISASSORTATIVE MIXING OF CORRELATION-BASED WEIGHTED FINANCIAL NETWORKS

2010 ◽  
Vol 21 (03) ◽  
pp. 433-441 ◽  
Author(s):  
SHI-MIN CAI ◽  
YAN-BO ZHOU ◽  
TAO ZHOU ◽  
PEI-LING ZHOU
Keyword(s):  
Financial Market ◽  
Span Tree ◽  
Nearest Neighbor ◽  
Underlying Mechanism ◽  
Hierarchical Organization ◽  
Cumulative Distribution ◽  
Financial Networks ◽  
Financial Network ◽  
Weighted Complex Network ◽  
The Relationship

Correlation-based weighted financial networks are analyzed to present cumulative distribution of strength with a power-law tail, which suggests that a small number of hub-like stocks have greater influence on the whole fluctuation of financial market than others. The relationship between clustering and connectivity of vertices emphasizes hierarchical organization, which has been depicted by minimal span tree in previous work. These results urge us to further study the mixing patter of financial network to understand the tendency for vertices to be connected to vertices that are like (or unlike) them in some way. The measurement of average nearest-neighbor degree running over classes of vertices with degree k shows a descending trend when k increases. This interesting result is first uncovered in our work, and suggests the disassortative mixing of financial network which refers to a bias in favor of connections between dissimilar vertices. All the results in weighted complex network aspect may provide some insights to deeper understand the underlying mechanism of financial market and model the evolution of financial market.

scholarly journals Quantifying the compressibility of complex networks

2021 ◽  
Vol 118 (32) ◽  
pp. e2023473118
Author(s):  
Christopher W. Lynn ◽  
Danielle S. Bassett
Keyword(s):  
Complex Networks ◽  
Resource Constraints ◽  
Rate Distortion ◽  
Hierarchical Organization ◽  
Human Cognition ◽  
Minimal Amount ◽  
Amount Of Information ◽  
Rate Distortion Theory ◽  
Network Properties ◽  
Biological Entities

Many complex networks depend upon biological entities for their preservation. Such entities, from human cognition to evolution, must first encode and then replicate those networks under marked resource constraints. Networks that survive are those that are amenable to constrained encoding—or, in other words, are compressible. But how compressible is a network? And what features make one network more compressible than another? Here, we answer these questions by modeling networks as information sources before compressing them using rate-distortion theory. Each network yields a unique rate-distortion curve, which specifies the minimal amount of information that remains at a given scale of description. A natural definition then emerges for the compressibility of a network: the amount of information that can be removed via compression, averaged across all scales. Analyzing an array of real and model networks, we demonstrate that compressibility increases with two common network properties: transitivity (or clustering) and degree heterogeneity. These results indicate that hierarchical organization—which is characterized by modular structure and heterogeneous degrees—facilitates compression in complex networks. Generally, our framework sheds light on the interplay between a network’s structure and its capacity to be compressed, enabling investigations into the role of compression in shaping real-world networks.

HYPERCHAOTIC SYNCHRONIZATION IN DETERMINISTIC SMALL-WORLD DYNAMICAL NETWORKS

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2674-2679 ◽  
Author(s):  
PIN-QUN JIANG ◽  
BING-HONG WANG ◽  
SHOU-LIANG BU ◽  
QING-HUA XIA ◽  
XIAO-SHU LUO
Keyword(s):  
Complex Networks ◽  
Continuous Time ◽  
Weak Coupling ◽  
Nearest Neighbor ◽  
Small World ◽  
Feedback System ◽  
Small World Network ◽  
Single Node ◽  
Hyperchaotic Synchronization ◽  
Real Complex

In this paper, hyperchaotic synchronization in a network of continuous-time dynamical systems with small-world connections is investigated. The small-world network is obtained by selecting a part of nodes to be hubs and then using a globally coupled network to interconnect these hubs in an originally nearest-neighbor coupled network. We show that, the deterministic small-world dynamical network will also synchronize when the maximal Lyapunov exponent of the self-feedback system of single node is equated to, even great than zero. This explains why many real-world complex networks exhibit strong tendency toward synchronization even with a relatively weak coupling. Our study may shed some new light on synchronization phenomena in real complex networks.

scholarly journals Evolutionary significance of metabolic network properties

2011 ◽  
Vol 9 (71) ◽  
pp. 1168-1176 ◽  
Author(s):  
Georg Basler ◽  
Sergio Grimbs ◽  
Oliver Ebenhöh ◽  
Joachim Selbig ◽  
Zoran Nikoloski
Keyword(s):  
Complex Networks ◽  
Structural Properties ◽  
Protein Interactions ◽  
Biological Networks ◽  
Metabolic Networks ◽  
Functional Organization ◽  
Hierarchical Organization ◽  
Clustering Coefficient ◽  
Evolutionary Significance ◽  
System Architectures

Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein–protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, such as small average path length, large clustering coefficient, heavy-tail degree distribution and hierarchical organization, viewed as requirements for efficient and robust system architectures. However, for biological networks, it is unclear to what extent these properties reflect the evolutionary history of the represented systems. Here, we show that the salient structural properties of six metabolic networks from all kingdoms of life may be inherently related to the evolution and functional organization of metabolism by employing network randomization under mass balance constraints. Contrary to the results from the common Markov-chain switching algorithm, our findings suggest the evolutionary importance of the small-world hypothesis as a fundamental design principle of complex networks. The approach may help us to determine the biologically meaningful properties that result from evolutionary pressure imposed on metabolism, such as the global impact of local reaction knockouts. Moreover, the approach can be applied to test to what extent novel structural properties can be used to draw biologically meaningful hypothesis or predictions from structure alone.

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