Sequential seeding for spreading in complex networks: Influence of the network topology

2018 ◽  
Vol 508 ◽  
pp. 10-17 ◽  
Author(s):  
Qipeng Liu ◽  
Tao Hong
Keyword(s):  
Complex Networks ◽  
Network Topology

scholarly journals PROBING COMPLEX NETWORKS FROM MEASURED TIME SERIES

2012 ◽  
Vol 22 (10) ◽  
pp. 1250236 ◽  
Author(s):  
LIANG HUANG ◽  
YING-CHENG LAI ◽  
MARY ANN F. HARRISON
Keyword(s):  
Time Series ◽  
Complex Networks ◽  
Network Topology ◽  
Efficient Method ◽  
Relative Importance ◽  
Computationally Efficient ◽  
Heuristic Argument ◽  
Principal Eigenvector ◽  
Measured Time ◽  
Potential Applications

We propose a method to detect nodes of relative importance, e.g. hubs, in an unknown network based on a set of measured time series. The idea is to construct a matrix characterizing the synchronization probabilities between various pairs of time series and examine the components of the principal eigenvector. We provide a heuristic argument indicating the existence of an approximate one-to-one correspondence between the components and the degrees of the nodes from which measurements are obtained. The striking finding is that such a correspondence appears to be quite robust, which holds regardless of the detailed node dynamics and of the network topology. Our computationally efficient method thus provides a general means to address the important problem of network detection, with potential applications in a number of fields.

Application of Complex Network Theory in Computer Network Topology Optimization Research

2014 ◽  
Vol 989-994 ◽  
pp. 4237-4240
Author(s):  
Zhi Kun Wang
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Complex Network ◽  
Network Topology ◽  
Computer Network ◽  
Transportation Network ◽  
Power Grids ◽  
Research Network ◽  
Complex Network Theory ◽  
New Research

If we apply the system internal elements as nodes, and the relationship between the elements as connection, then the system form a network. If we put emphasis on the structure of the system and analyze the function of the system from the angle of structure, we’ll find that real network topology properties differ from previous research network, and has numerous nodes, which is called complex networks. In the real word, many complex systems can be basically described by the network, while the reality is that complex systems can be called as “complex network”, such as social network, transportation network, power grids and internet etc. In recent years, many articles about the complex networks are released in the international first-class publications such as Nature, PRL, PNAS, which reflects that the complex networks has become a new research focus.

scholarly journals Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Fanrong Meng ◽  
Feng Zhang ◽  
Mu Zhu ◽  
Yan Xing ◽  
Zhixiao Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Topology ◽  
Clustering Algorithm ◽  
Dynamic Networks ◽  
Experimental Results ◽  
Extended Version ◽  
High Quality ◽  
Detection Algorithms

Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

scholarly journals Modeling Complex Networks Based on Deep Reinforcement Learning

2022 ◽  
Vol 9 ◽  
Author(s):  
Wenbo Song ◽  
Wei Sheng ◽  
Dong Li ◽  
Chong Wu ◽  
Jun Ma
Keyword(s):  
Reinforcement Learning ◽  
Complex Networks ◽  
Network Topology ◽  
Network Structure ◽  
Dynamic Change ◽  
Intelligent Agent ◽  
Small World ◽  
Network Nodes ◽  
Scale Free ◽  
Internal Mechanism

The network topology of complex networks evolves dynamically with time. How to model the internal mechanism driving the dynamic change of network structure is the key problem in the field of complex networks. The models represented by WS, NW, BA usually assume that the evolution of network structure is driven by nodes’ passive behaviors based on some restrictive rules. However, in fact, network nodes are intelligent individuals, which actively update their relations based on experience and environment. To overcome this limitation, we attempt to construct a network model based on deep reinforcement learning, named as NMDRL. In the new model, each node in complex networks is regarded as an intelligent agent, which reacts with the agents around it for refreshing its relationships at every moment. Extensive experiments show that our model not only can generate networks owing the properties of scale-free and small-world, but also reveal how community structures emerge and evolve. The proposed NMDRL model is helpful to study propagation, game, and cooperation behaviors in networks.

scholarly journals Exact probabilities for the indeterminacy of complex networks as perceived through press perturbations

2016 ◽  
Author(s):  
David Koslicki ◽  
Mark Novak
Keyword(s):  
Complex Networks ◽  
Network Topology ◽  
Network Dynamics ◽  
Ecological Networks ◽  
Primary Result ◽  
Qualitative Modeling ◽  
Expected Number ◽  
Recent Advances ◽  
Modeling Techniques

AbstractWe consider the goal of predicting how complex networks respond to chronic (press) perturbations when characterizations of their network topology and interaction strengths are associated with uncertainty. Our primary result is the derivation of exact formulas for the expected number and probability of qualitatively incorrect predictions about a system’s responses under uncertainties drawn form arbitrary distributions of error. These formulas obviate the current use of simulations, algorithms, and qualitative modeling techniques. Additional indices provide new tools for identifying which links in a network are most qualitatively and quantitatively sensitive to error, and for determining the volume of errors within which predictions will remain qualitatively determinate (i.e. sign insensitive). Together with recent advances in the empirical characterization of uncertainty in ecological networks, these tools bridge a way towards probabilistic predictions of network dynamics.

scholarly journals Detecting different topologies immanent in scale-free networks with the same degree distribution

2019 ◽  
Vol 116 (14) ◽  
pp. 6701-6706 ◽  
Author(s):  
Dimitrios Tsiotas
Keyword(s):  
Complex Networks ◽  
Power Law ◽  
Network Topology ◽  
Degree Distribution ◽  
Growth Process ◽  
Preferential Attachment ◽  
Self Similarity ◽  
Scale Free ◽  
Scale Free Networks ◽  
Definition Of

The scale-free (SF) property is a major concept in complex networks, and it is based on the definition that an SF network has a degree distribution that follows a power-law (PL) pattern. This paper highlights that not all networks with a PL degree distribution arise through a Barabási−Albert (BA) preferential attachment growth process, a fact that, although evident from the literature, is often overlooked by many researchers. For this purpose, it is demonstrated, with simulations, that established measures of network topology do not suffice to distinguish between BA networks and other (random-like and lattice-like) SF networks with the same degree distribution. Additionally, it is examined whether an existing self-similarity metric proposed for the definition of the SF property is also capable of distinguishing different SF topologies with the same degree distribution. To contribute to this discrimination, this paper introduces a spectral metric, which is shown to be more capable of distinguishing between different SF topologies with the same degree distribution, in comparison with the existing metrics.

scholarly journals Mapping the topology of the air transport network in Turkey

2019 ◽  
Vol 52 (1) ◽  
pp. 6-9
Author(s):  
Umut Erdem ◽  
K. Mert Cubukcu ◽  
Dimitrios Tsiotas
Keyword(s):  
Economic Growth ◽  
Complex Networks ◽  
Network Topology ◽  
Transport Network ◽  
Air Transport ◽  
Uneven Distribution ◽  
Population Concentration ◽  
Network Topologies ◽  
Equal Population

Recent technological and philosophical research has revealed that almost everything around us is heavily dependent on network-based complexities. Airport network topologies are complex networks and their analyses are crucial regarding the fact that the evolution of airport network topology influences the economic growth of regions and countries. An equal population cartogram is derived displaying the distortion of the air transport network of Turkey in accordance with the uneven distribution of passengers. The regions between İstanbul, Ankara, İzmir, and Antalya are shrunk by the force of higher population concentration. The shrinkage across the eastern regions is less than that in the western regions; still, the distortion of the regions is dominated by particular regional hubs.

scholarly journals Expected Emergent Algorithmic Creativity and Integration in Dynamic Complex Networks

2018 ◽  
Author(s):  
Felipe S. Abrahão ◽  
Klaus Wehmuth ◽  
Artur Ziviani
Keyword(s):  
Phase Transition ◽  
Complex Networks ◽  
Theoretical Investigation ◽  
Network Topology ◽  
Communication Protocol ◽  
Network Size ◽  
Turing Machines ◽  
Emergence Of Complexity ◽  
Unlimited Amount ◽  
Over Time

We present a theoretical investigation of the emergence of complexity or irreducible information in networked computable systems when the network topology may change over time. For this purpose, we build a network model in which nodes are randomly generated Turing machines that obey a communication protocol of imitation of the fittest neighbor. Then, we show that there are topological conditions that trigger a phase transition in which eventually these networked computable systems begin to produce an unlimited amount of bits of expected emergent algorithmic complexity, creativity and integration as the network size goes to infinity.

scholarly journals Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks

2020 ◽  
Vol 117 (31) ◽  
pp. 18332-18340 ◽  
Author(s):  
Paolo Moretti ◽  
Marc-Thorsten Hütt
Keyword(s):  
Complex Networks ◽  
Network Topology ◽  
Numerical Results ◽  
Topological Characterization ◽  
Rich Club ◽  
Self Organized ◽  
Local Patterns ◽  
Directional Preference ◽  
Average Orientation ◽  
The Difference

In models of excitable dynamics on graphs, excitations can travel in both directions of an undirected link. However, as a striking interplay of dynamics and network topology, excitations often establish a directional preference. Some of these cases of “link-usage asymmetry” are local in nature and can be mechanistically understood, for instance, from the degree gradient of a link (i.e., the difference in node degrees at both ends of the link). Other contributions to the link-usage asymmetry are instead, as we show, self-organized in nature, and strictly nonlocal. This is the case for excitation waves, where the preferential propagation of excitations along a link depends on its orientation with respect to a hub acting as a source, even if the link in question is several steps away from the hub itself. Here, we identify and quantify the contribution of such self-organized patterns to link-usage asymmetry and show that they extend to ranges significantly longer than those ascribed to local patterns. We introduce a topological characterization, the hub-set-orientation prevalence of a link, which indicates its average orientation with respect to the hubs of a graph. Our numerical results show that the hub-set-orientation prevalence of a link strongly correlates with the preferential usage of the link in the direction of propagation away from the hub core of the graph. Our methodology is embedding-agnostic and allows for the measurement of wave signals and the sizes of the cores from which they originate.

THE MODELLING OF WEIGHTED COMPLEX NETWORKS

2007 ◽  
Vol 21 (16) ◽  
pp. 2813-2820
Author(s):  
XUTAO WANG ◽  
HONGTAO LU ◽  
GUANRONG CHEN
Keyword(s):  
Complex Networks ◽  
Network Topology ◽  
New Model ◽  
The Real ◽  
Scale Free ◽  
Free Behavior ◽  
Weighted Complex Networks

In order to further explore the mechanism responsible for weighted complex networks, we introduce a new model that incorporates the network topology and the weights' dynamical evolutions. Our model can capture the details of weight dynamics caused not only by the addition of a new node with new links and new links between old nodes, but also the deletion of old links. We calculate analytically the distributions of both degree and strength and found that all these distributions show scale-free behavior, as confirmed in many real networks. Thus our model characterizes the real weighted complex networks more precisely.

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