A comparative analysis of the statistical properties of large mobile phone calling networks

Li, Ming-Xia; Jiang, Zhi‐Qiang; Xie, Wen-Jie; Miccichè, Salvatore; Tumminello, Michele; Zhou, Wei‐Xing; Mantegna, Rosario N.

doi:10.1038/srep05132

Cited by 42 publications

(37 citation statements)

References 43 publications

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“…For each link in the network, we perform a statistical test to check whether two banks preferentially traded in a given three-maintenance period. Our test is done by using a recently proposed method (Li et al (2014)) that is a directional variant of the method presented in Tumminello et al (2011). The statistical test is implemented as follows.…”

Section: Statistically Validated Networkmentioning

confidence: 99%

Networked relationships in the e-MID interbank market: A trading model with memory

Iori¹,

Mantegna²,

Marotta³

et al. 2015

Journal of Economic Dynamics and Control

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Interbank markets are fundamental for bank liquidity management. In this paper, we introduce a model of interbank trading with memory. Our model reproduces features of preferential trading patterns in the e-MID market recently empirically observed through the method of statistically validated networks. The memory mechanism is used to introduce a proxy of trust in the model. The key idea is that a lender, having lent many times to a borrower in the past, is more likely to lend to that borrower again in the future than to other borrowers, with which the lender has never (or has infrequently) interacted. The core of the model depends on only one parameter representing the initial attractiveness of all the banks as borrowers. Model outcomes and real data are compared through a variety of measures that describe the structure and properties of trading networks, including number of statistically validated links, bidirectional links, and 3-motifs. Refinements of the pairing method are also proposed, in order to capture finite memory and reciprocity in the model. The model is implemented within the Mason framework in Java.$ The authors acknowledge support from the FP7 research project CRISIS "Complexity Research Initiative for Systemic InstabilitieS". G.I. acknowledge support from the FP7 research project FOC "Forecasting Financial Crises ".

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Section: Statistically Validated Networkmentioning

confidence: 99%

Networked relationships in the e-MID interbank market: A trading model with memory

Iori¹,

Mantegna²,

Marotta³

et al. 2015

Journal of Economic Dynamics and Control

Self Cite

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“…While some empirical networks consist of a single connected component such as transportation networks and brain networks [47], other networks consist of many isolated components of various sizes such as adoption of innovations or products networks [48] and mobile phone calling networks [49]. The distribution of sizes of these components has been studied in the context of subcritical networks and provides a useful characterization of the network structure [24].…”

Section: Discussionmentioning

confidence: 99%

Generating random networks that consist of a single connected component with a given degree distribution

et al. 2019

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We present a method for the construction of ensembles of random networks that consist of a single connected component with a given degree distribution. This approach extends the construction toolbox of random networks beyond the configuration model framework, in which one controls the degree distribution but not the number of components and their sizes. Unlike configuration model networks, which are completely uncorrelated, the resulting single-component networks exhibit degree-degree correlations. Moreover, they are found to be disassortative, namely high-degree nodes tend to connect to low-degree nodes and vice versa. We demonstrate the method for singlecomponent networks with ternary, exponential and power-law degree distributions. PACS numbers: 64.60.aq,89.75.Da 1 I. INTRODUCTIONNetwork models provide a useful description of a broad range of phenomena in the natural sciences and engineering as well as in the economic and social sciences. This realization has stimulated increasing interest in the structure of complex networks, and in the dynamical processes that take place on them [1-9]. One of the central lines of inquiry has been concerned with the existence of a giant connected component that is extensive in the network size. In the case of Erdős-Rényi (ER) networks, the critical parameters for the emergence of a giant component in the thermodynamic limit were identified and the fraction of nodes that reside in the giant component was determined [10][11][12][13]. These studies were later extended to the broader class of configuration model networks [14,15]. The configuration model framework enables one to construct an ensemble of random networks whose degree sequences are drawn from a desired degree distribution, with no degree-degree correlations. The resulting network ensemble is a maximum entropy ensemble under the condition of the given degree distribution. A simple example of a configuration model network is the random regular graph, in which all the nodes are of the same degree, k = c. For random regular graphs with c ≥ 3 the giant component encompasses the whole network [16]. However, in general, configuration model networks often exhibit a coexistence between a giant component, which is extensive in the network size, and many finite components, which are non-extensive trees. This can be exemplified by the case of ER networks, which exhibit a Poisson degree distribution of the formwhere c = K is the mean degree. ER networks with 0 < c < 1 consist of finite tree components. At c = 1 there is a percolation transition, above which the network exhibits a coexistence between the giant component and the finite components. In the asymptotic limit, the size of the giant component is N 1 = gN, where N is the size of the whole network and the parameter g = g(c), which vanishes for c ≤ 1, increases monotonically for c > 1. At c = ln N there is a second transition, above which the giant component encompasses the entire network [16]. In the range of 1 < c < ln N, where the giant and finite components coexist, the...

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“…Mobile social networks (MSNs) [1][2][3][4][5][6][7] are networks with device mobility and social communication. With phones or tablets, people who share common interests can create a profile, multimedia posts, instant messaging and play social gaming.…”

Section: Introductionmentioning

confidence: 99%

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Gao¹,

Xu²,

Wang³

et al. 2020

Preprint

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Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

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A comparative analysis of the statistical properties of large mobile phone calling networks

Cited by 42 publications

References 43 publications

Networked relationships in the e-MID interbank market: A trading model with memory

Networked relationships in the e-MID interbank market: A trading model with memory

Generating random networks that consist of a single connected component with a given degree distribution

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

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