Distance Entropy Cartography Characterises Centrality in Complex Networks

We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.

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Modern Diffusion of Products with Complex Network Models

E-Activity and Intelligent Web Construction - Advances in Web Technologies and Engineering ◽

10.4018/978-1-61520-871-5.ch011 ◽

2011 ◽

pp. 119-133

Author(s):

Atsushi Tanaka

Keyword(s):

Complex Networks ◽

Marketing Strategy ◽

Complex Network ◽

Network Models ◽

Information Propagation ◽

Multiagent Simulation ◽

Product Marketing ◽

Winner Takes All ◽

Complex Network Models

In this chapter, some important matters of complex networks and their models are reviewed shortly, and then the modern diffusion of products under the information propagation using multiagent simulation is discussed. The remarkable phenomena like “Winner-Takes-All” and “Chasm” can be observed, and one product marketing strategy is also proposed.

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Trade-offs between driving nodes and time-to-control in complex networks

Scientific Reports ◽

10.1038/srep39978 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 13

Author(s):

Sérgio Pequito ◽

Victor M. Preciado ◽

Albert-László Barabási ◽

George J. Pappas

Keyword(s):

Complex Networks ◽

Time Window ◽

Empirical Studies ◽

Practical Importance ◽

Network Models ◽

Small Time ◽

Synthetic Network ◽

Trade Offs ◽

Minimum Number ◽

Time Required

Abstract Recent advances in control theory provide us with efficient tools to determine the minimum number of driving (or driven) nodes to steer a complex network towards a desired state. Furthermore, we often need to do it within a given time window, so it is of practical importance to understand the trade-offs between the minimum number of driving/driven nodes and the minimum time required to reach a desired state. Therefore, we introduce the notion of actuation spectrum to capture such trade-offs, which we used to find that in many complex networks only a small fraction of driving (or driven) nodes is required to steer the network to a desired state within a relatively small time window. Furthermore, our empirical studies reveal that, even though synthetic network models are designed to present structural properties similar to those observed in real networks, their actuation spectra can be dramatically different. Thus, it supports the need to develop new synthetic network models able to replicate controllability properties of real-world networks.

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The Impact of Complex Network Models to EDAs

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.3290 ◽

2014 ◽

Vol 926-930 ◽

pp. 3290-3293

Author(s):

Cai Chang Ding ◽

Wen Xiu Peng ◽

Wei Ming Wang

Keyword(s):

Complex Networks ◽

Complex Network ◽

Optimization Problem ◽

Wide Spectrum ◽

Network Models ◽

Problem Structure ◽

Topological Characteristics ◽

Complex Network Models ◽

The Impact

The study conducted in this paper is mainly driven by the topological characteristics of the structures that the interactions among the variables of the problems provide. Taking as reference the emergent field of complex networks, we generate a wide spectrum of networks that will serve as problem structures. Then, the impact that the topological characteristics of those networks have, both in the hardness of the optimization problem and in the behavior of the EDA, is analyzed. This reveals a relationship among the topology of the problem structure, the difficulty of the problems and the dependences that the algorithm needs to learn in order to solve the problems.

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Study of Cascading Failure in Multisubnet Composite Complex Networks

Symmetry ◽

10.3390/sym13030523 ◽

2021 ◽

Vol 13 (3) ◽

pp. 523

Author(s):

Gengxin Sun ◽

Chih-Cheng Chen ◽

Sheng Bin

Keyword(s):

Complex Networks ◽

Complex Network ◽

Influencing Factors ◽

Network Models ◽

Network Robustness ◽

Cascading Failure ◽

Network System ◽

Multiple Relationships ◽

Hierarchical Network ◽

Coupling Relationship

Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affect the process and scale of the network cascading failure. In this paper, a composite network is constructed based on the multisubnet composite complex network model, and its cascading failure is proposed combined with multiple relationships. The effect of intranetwork relationships and coupling relationships on network robustness under different influencing factors is studied. It is shown that cascading failure in composite networks is different from coupling networks, and increasing the strength of the coupling relationship can significantly improve the robustness of the network.

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Eigenvalue-based entropy in directed complex networks

PLoS ONE ◽

10.1371/journal.pone.0251993 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0251993

Author(s):

Yan Sun ◽

Haixing Zhao ◽

Jing Liang ◽

Xiujuan Ma

Keyword(s):

Complex Networks ◽

Complex Network ◽

Network Models ◽

Small World ◽

Laplacian Matrix ◽

Directed Network ◽

Small World Network ◽

Scale Free Network ◽

Scale Free ◽

Free Network

Entropy is an important index for describing the structure, function, and evolution of network. The existing research on entropy is primarily applied to undirected networks. Compared with an undirected network, a directed network involves a special asymmetric transfer. The research on the entropy of directed networks is very significant to effectively quantify the structural information of the whole network. Typical complex network models include nearest-neighbour coupling network, small-world network, scale-free network, and random network. These network models are abstracted as undirected graphs without considering the direction of node connection. For complex networks, modeling through the direction of network nodes is extremely challenging. In this paper, based on these typical models of complex network, a directed network model considering node connection in-direction is proposed, and the eigenvalue entropies of three matrices in the directed network is defined and studied, where the three matrices are adjacency matrix, in-degree Laplacian matrix and in-degree signless Laplacian matrix. The eigenvalue-based entropies of three matrices are calculated in directed nearest-neighbor coupling, directed small world, directed scale-free and directed random networks. Through the simulation experiment on the real directed network, the result shows that the eigenvalue entropy of the real directed network is between the eigenvalue entropy of directed scale-free network and directed small-world network.

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A New Evaluation Method of Node Importance in Directed Weighted Complex Networks

Journal of Systems Science and Information ◽

10.21078/jssi-2017-367-09 ◽

2017 ◽

Vol 5 (4) ◽

pp. 367-375 ◽

Cited By ~ 1

Author(s):

Yu Wang ◽

Jinli Guo ◽

Han Liu

Keyword(s):

Complex Networks ◽

Complex Network ◽

Real World ◽

Evaluation Method ◽

Network Models ◽

Node Importance ◽

Weighted Complex Network ◽

Complex Network Models ◽

Evaluation Matrix ◽

Key Nodes

AbstractCurrent researches on node importance evaluation mainly focus on undirected and unweighted networks, which fail to reflect the real world in a comprehensive and objective way. Based on directed weighted complex network models, the paper introduces the concept of in-weight intensity of nodes and thereby presents a new method to identify key nodes by using an importance evaluation matrix. The method not only considers the direction and weight of edges, but also takes into account the position importance of nodes and the importance contributions of adjacent nodes. Finally, the paper applies the algorithm to a microblog-forwarding network composed of 34 users, then compares the evaluation results with traditional methods. The experiment shows that the method proposed can effectively evaluate the node importance in directed weighted networks.

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Recommended method study based on incorporating complex network ripple net

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213951070 ◽

2021 ◽

Vol 39 (5) ◽

pp. 1070-1076

Author(s):

Yutai LUO ◽

Tao XU ◽

Zhangbo XU

Keyword(s):

Complex Networks ◽

Complex Network ◽

Present Method ◽

Network Models ◽

User Preferences ◽

Experimental Results ◽

Knowledge Graph ◽

Knowledge Maps ◽

Network Nodes

The RippleNet network models user preferences and is well applied in the recommended system. But Ripplenet didn't take into account the weight of entities in the knowledge graph, resulting in the inaccurate recommendation results. A RippleNet model incorporating the influence of the complex network nodes is proposed. After constructing the complex networks based on the knowledge maps, the maximum subnet model is extracted, the influence of the nodes in the map network is calculated, and the weight of the nodes is added to the RippleNet model as an entity. The experimental results showed that the present method increased the AUC and ACC values of RippleNet to 92.0% and 84.6%, made up for the problem that no entity influence was considered in the RippleNet network, and made the recommended results more in line with users' expectations.

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Information on evolutionary age in redundancy of complex network

Modern Physics Letters B ◽

10.1142/s0217984919503317 ◽

2019 ◽

Vol 33 (27) ◽

pp. 1950331

Author(s):

Shiguo Deng ◽

Henggang Ren ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Principal Component Analysis ◽

Complex Networks ◽

Metabolic Network ◽

Complex Network ◽

Cellular Networks ◽

Topological Structure ◽

Quantitative Measure ◽

Principal Component ◽

Evolutionary Information ◽

Local Patterns

Evolutionary processes of many complex networks in reality are dominated by duplication and divergence. This mechanism leads to redundant structures, i.e. some nodes share most of their neighbors and some local patterns are similar, called redundancy of network. An interesting reverse problem is to discover evolutionary information from the present topological structure. We propose a quantitative measure of redundancy of network from the perspective of principal component analysis. The redundancy of a community in the empirical human metabolic network is negatively and closely related with its evolutionary age, which is consistent with that for the communities in the modeling protein–protein network. This behavior can be used to find the evolutionary difference stored in cellular networks.

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Modelling community structure and temporal spreading on complex networks

Computational Social Networks ◽

10.1186/s40649-021-00094-z ◽

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.

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