Influence Maximization From Cascade Information Traces in Complex Networks in the Absence of Network Structure

2019 ◽  
Vol 6 (6) ◽  
pp. 1147-1155
Author(s):  
Naimisha Kolli ◽  
Balakrishnan Narayanaswamy
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Influence Maximization

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

An Introduction to Complex Networks

2013 ◽  
Author(s):  
Jordi Bascompte ◽  
Pedro Jordano
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Network Structure ◽  
Ecological Networks ◽  
Broad Context ◽  
Network Approach ◽  
Mutualistic Networks ◽  
Plant Animal Interactions

Mutualisms can involve dozens, even hundreds, of species and this complexity has precluded a serious community-wide approach to plant–animal interactions. The most straightforward way to describe such an interacting community is with a network of interactions. In this approach, species are represented as nodes of two types: plants and animals. This chapter provides the tools and concepts for characterizing mutualistic networks and placing them into a broad context. This serves as a background with which to understand the structure of mutualistic networks. The discussions cover a network approach to complex systems, measures of network structure, models of network buildup, and ecological networks.

Optimizing complex networks controllability by local structure information

2016 ◽  
Vol 27 (10) ◽  
pp. 1650115
Author(s):  
Houyi Yan ◽  
Lvlin Hou ◽  
Yunxiang Ling ◽  
Guohua Wu
Keyword(s):  
Numerical Simulation ◽  
Complex Networks ◽  
Network Structure ◽  
Local Structure ◽  
Global Structure ◽  
Directed Network ◽  
Structure Information ◽  
Different Types ◽  
Extensive Numerical Simulation ◽  
Network Controllability

Research in network controllability has mostly been focused on the effects of the network structure on its controllability, and some methods have been proposed to optimize the network controllability. However, they are all based on global structure information of networks. We propose two different types of methods to optimize controllability of a directed network by local structure information. Extensive numerical simulation on many modeled networks demonstrates that this method is effective. Since the whole topologies of many real networks are not visible and we only get some local structure information, this strategy is potentially more practical.

scholarly journals Non-Markovianity over Ensemble Averages in Quantum Complex Networks

2017 ◽  
Vol 24 (04) ◽  
pp. 1740018 ◽  
Author(s):  
Johannes Nokkala ◽  
Sabrina Maniscalco ◽  
Jyrki Piilo
Keyword(s):  
Spectral Density ◽  
Harmonic Oscillator ◽  
Complex Networks ◽  
Transport Properties ◽  
Network Structure ◽  
Coupling Strength ◽  
Large Impact ◽  
Random Networks ◽  
Quantum Harmonic Oscillator ◽  
Different Types

We consider bosonic quantum complex networks as structured finite environments for a quantum harmonic oscillator and investigate the interplay between the network structure and its spectral density, excitation transport properties and non-Markovianity. After a review of the formalism used, we demonstrate how even small changes to the network structure can have a large impact on the transport of excitations. We then consider the non-Markovianity over ensemble averages of several different types of random networks of identical oscillators and uniform coupling strength. Our results show that increasing the number of interactions in the network tends to suppress the average non-Markovianity. This suggests that tree networks are the random networks optimizing this quantity.

THE GINI COEFFICIENT'S APPLICATION TO GENERAL COMPLEX NETWORKS

2005 ◽  
Vol 08 (01) ◽  
pp. 159-167 ◽  
Author(s):  
HAI-BO HU ◽  
LIN WANG
Keyword(s):  
Income Inequality ◽  
Complex Networks ◽  
Network Structure ◽  
Gini Coefficient ◽  
Scale Free ◽  
The Gini Coefficient ◽  
Scale Free Networks ◽  
General Complex

The Gini coefficient, which was originally used in microeconomics to describe income inequality, is introduced into the research of general complex networks as a metric on the heterogeneity of network structure. Some parameters such as degree exponent and degree-rank exponent were already defined in the case of scale-free networks also as a metric on the heterogeneity. In scale-free networks, the Gini coefficient is proved to be equivalent to the parameters mentioned above, and moreover, a classification of infinite scale-free networks is given according to the value of the Gini coefficient.

scholarly journals Complex Networks: Traffic Dynamics, Network Performance, and Network Structure

2013 ◽  
Vol 03 (01) ◽  
pp. 187-195 ◽  
Author(s):  
Ziping Hu ◽  
Krishnaiyan Thulasiraman ◽  
Pramode K. Verma
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Network Performance ◽  
Traffic Dynamics

scholarly journals Network Analysis as a Powerful Approach to Analyse the Operational Environment

2020 ◽  
Vol 25 (3) ◽  
pp. 245-252
Author(s):  
Martin Hristov
Keyword(s):  
Graph Theory ◽  
Network Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Operational Environment ◽  
Powerful Approach ◽  
Alternative Approaches

AbstractIn times of information flooding us from everywhere and in an environment of increasingly complex and difficult to understand systems that our forces face in the operational environment, there is a need for alternative approaches to analyzing the environment and its participants. This approach meets key requirements and provides new and in-depth knowledge of systems. Network analysis is an approach based on graph theory that is applicable to the analysis of any system or process with a network structure. The network analysis toolkit is suitable for highlighting “important” players or elements in complex networks invisible at first glance, according to various criteria set in the term “important”.

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