A network model of the interbank market

This work introduces a network model of an interbank market based on interbank credit lending relationships. It generates some network features identified through empirical analysis. The critical issue to construct an interbank network is to decide the edges among banks, which is realized in this paper based on the interbank’s degree of trust. Through simulation analysis of the interbank network model, some typical structural features are identified in our interbank network, which are also proved to exist in real interbank networks. They are namely, a low clustering coefficient and a relatively short average path length, community structures, and a two-power-law distribution of out-degree and in-degree.     

The topology of the federal funds market

We explore the network topology of the federal funds market. This market is important for distributing liquidity throughout the financial system and for the implementation of monetary policy. The recent turmoil in global financial markets underscores its importance. We find that the network is sparse, exhibits the small-world phenomenon, and is disassortative. Centrality measures are useful predictors of the interest rate of a loan.     

The coevolutionary ultimatum game on different network topologies

In this paper, a model of ultimatum game is discussed from the coevolutionary perspective, where strategy dynamics and structure dynamics coexist. The interplay between structure dynamics and strategy dynamics leads to overwhelmingly interesting evolved topology and fairness behaviors. It is found that fair division emerges for specific ratios of structure updating probability to strategy updating probability. Furthermore, it is shown that the initial structures have no essentially different effect on the coevolutionary results. In particular, the results for strategy are almost similar whenever the initial structure is set to be the nearest-neighbor coupled network, the ER random network or the scale-free network. Besides, the effects of other spatial factors are also investigated, e.g. the population size has a positive influence on the offer, while the average degree has a negative effect. In addition, one extrinsic factor, the background payoff, is also of great importance in promoting fair divisions. Apart from above, we study the properties of the evolved networks, which have the small-world effect and positive assortative behaviors.     

The role of banks in the Brazilian interbank market: Does bank type matter?

This paper analyzes the Brazilian interbank network structure using a complex network-based approach. Results suggest a weak evidence of community structure, high heterogeneity of the network and that this market is characterized by money centers having exposures to many banks. Furthermore, we go beyond the structure of the network using information about the characteristics of the nodes and a non-parametric test in order to understand the role of the banks in the interbanking market.     

The effects of degree correlations on network topologies and robustness

Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of complex systems. In the last few years, many network metrics and models have been proposed to investigate the network topology, dynamics and evolution. Since these network metrics and models are derived from a wide range of studies, a systematic study is required to investigate the correlations among them. The present paper explores the effect of degree correlation on the other network metrics through studying an ensemble of graphs where the degree sequence (set of degrees) is fixed. We show that to some extent, the characteristic path length, clustering coefficient, modular extent and robustness of networks are directly influenced by the degree correlation.     

Congestion and cascades in payment systems

We develop a parsimonious model of the interbank payment system. The model incorporates an endogenous instruction arrival process, a scale-free topology of payments between banks, a fixed total liquidity which limits banks’ capacity to process arriving instructions, and a global market that distributes liquidity. We find that at low liquidity the system becomes congested and payment settlement loses correlation with payment instruction arrival, becoming coupled across the network. The onset of congestion is evidently related to the relative values of three characteristic times: the time for banks’ net position to return to 0, the time for a bank to exhaust its liquidity endowment, and the liquidity market relaxation time. In the congested regime settlement takes place in cascades having a characteristic length scale. A global liquidity market substantially attenuates congestion, requiring only a small fraction of the payment-induced liquidity flow to achieve strong beneficial effects.     

Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform

We analyze the correlations in patterns of trading for members of the Italian interbank trading platform e-MID. The trading strategy of a particular member institution is defined as the sequence of (intra-) daily net trading volumes within a certain semester. Based on this definition, we show that there are significant and persistent bilateral correlations between institutions’ trading strategies. In most semesters we find two clusters, with positively (negatively) correlated trading strategies within (between) clusters. We show that the two clusters mostly contain continuous net buyers and net sellers of money, respectively, and that cluster memberships of individual banks are highly persistent. Additionally, we highlight some problems related to our definition of trading strategies. Our findings add further evidence on the fact that preferential lending relationships on the micro-level lead to community structure on the macro-level.     

A dynamic model for traffic network flow

Concerning the link properties in traffic networks, we introduce a dynamic equation of road flow into each link, and thereby propose a dynamic model for network flow. Using this model, we investigate the evolutions of inflow, outflow and flow on each link caused by a small perturbation of the network inflow under different route choice rules. Numerical results show that the dynamic model can reasonably capture the basic characteristics of network flow.     

Integrating network structure and dynamic information for better routing strategy on scale-free networks

We study information packet routing processes on scale-free networks by mimicking the Internet traffic delivery strategies. We incorporate both the global network structure information and local queuing information in the dynamic processes. We propose several new routing strategies to guide the packet routing. The performance of the routing strategies is measured by the average transit time of the packets as well as their dependence on the traffic amount. We find that the routing strategies which integrate both global network structure information and local dynamic information perform much better than the traditional shortest-path routing protocol which takes into account only the global topological information. Moreover, from comparative studies of these routing strategies, we observe that some of our proposed methods can decrease the average transit time of packets but the performance is closely dependent on the total amount of traffic while some other proposed methods can have good performance independent of the total amount of traffic with hyper-excellent average transit time of packets. Also, numerical results show that our proposed methods integrating network structure information and local dynamic information can work much better than the methods recently proposed in [S. Sreenivasan, R. Cohen, E. López, Z. Toroczkai, H.E. Stanley, Phys. Rev. E 75 (2007) 036105, Zhi-Xi Wu, Gang Peng, Eric W.M. Wong, Kai-Hau Yeung, J. Stat. Mech. (2008) P11002.], which only considered network structure information.     

Method of analyzing the influence of network structure on information diffusion

Social phenomena are affected by the structure of networks consisting of personal relationships. In the present paper, the diffusion of information among people is examined. In particular, the relationship between the network structure and the dynamics is studied. First, several networks are generated using the proposed network model and other network models, such as the WS model and the KE model. By changing the parameters of the network models, networks with different structures are generated. The parameters of the network models determine the topology of the networks and the statistical indicators.     

Distinguishing manipulated stocks via trading network analysis

Manipulation is an important issue for both developed and emerging stock markets. For the study of manipulation, it is critical to analyze investor behavior in the stock market. In this paper, an analysis of the full transaction records of over a hundred stocks in a one-year period is conducted. For each stock, a trading network is constructed to characterize the relations among its investors. In trading networks, nodes represent investors and a directed link connects a stock seller to a buyer with the total trade size as the weight of the link, and the node strength is the sum of all edge weights of a node. For all these trading networks, we find that the node degree and node strength both have tails following a power-law distribution. Compared with non-manipulated stocks, manipulated stocks have a high lower bound of the power-law tail, a high average degree of the trading network and a low correlation between the price return and the seller-buyer ratio. These findings may help us to detect manipulated stocks.     

Disease dynamics in a dynamic social network

We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.     

Performance analysis of integer linear programming optical monitor placement for crosstalk attack detection in different network topologies

In this paper, an optimum optical power monitor placement is formulated in integer linear programming for crosstalk attack detection by exploiting monitoring information from test connections. The experimental results showed that equipping 6 out of 14, 8 out of 15 and 8 out of 16 nodes with optical power monitoring devices in NSF, MESH and GRID topologies are enough to detect the nodes that are traversed by the crosstalk attack in the networks.     

A model for the evaluation of systemic risk in stock markets

Systemic risk refers to the possibility of a collapse of an entire financial system or market, differing from the risk associated with any particular individual or a group pertaining to the system, which may include banks, government, brokers, and creditors. After the 2008 financial crisis, a significant amount of effort has been directed to the study of systemic risk and its consequences around the world. Although it is very difficult to predict when people begin to lose confidence in a financial system, it is possible to model the relationships among the stock markets of different countries and perform a Monte Carlo-type analysis to study the contagion effect. Because some larger and stronger markets influence smaller ones, a model inspired by a catalytic chemical model is proposed. In chemical reactions, reagents with higher concentrations tend to favor their conversion to products. In order to modulate the conversion process, catalyzers may be used. In this work, a mathematical modeling is proposed with bases on the catalytic chemical reaction model. More specifically, the Hang Seng and Dow Jones indices are assumed to dominate Ibovespa (the Brazilian Stock Market index), such that the indices of strong markets are taken as being analogous to the concentrations of the reagents and the indices of smaller markets as concentrations of products. The role of the catalyst is to model the degree of influence of one index on another. The actual data used to fit the model parameter consisted of the Hang Seng index, Dow Jones index, and Ibovespa, since 1993. “What if” analyses were carried out considering some intervention policies.     

Topic tracking with Bayesian belief network

The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (C_det)_norm by 5.5% comparing to VSM, BDTM-II decreases (C_det)_norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.     

Influence of a network structure on the network effect in the communication service market

In this study, we analyze the network effect in a model of a personal communication market, by using a multi-agent based simulation approach. We introduce into the simulation model complex network structures as the interaction patterns of agents. With complex network models, we investigate the dynamics of a market in which two providers are competing. We also examine the structure of networks that affect the complex behavior of the market. By a series of simulations, we show that the structural properties of complex networks, such as the clustering coefficient and degree correlation, have a major influence on the dynamics of the market. We find that the network effect is increased if the interaction pattern of agents is characterized by a high clustering coefficient, or a positive degree correlation. We also discuss a suitable model of the interaction pattern for reproducing market dynamics in the real world, by performing simulations using real data of a social network.     

Optimal dynamic bandwidth allocation for complex networks

Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.     

Analysis of queuing delay in optical space network on LEO satellite constellations

Low earth orbit (LEO) satellite constellations using laser inter-satellite links (ISLs) are recognized as a promising technology to provide global broadband network services. In this paper, the queuing delay model of an optical space network built on LEO satellite constellations is established. It is assumed that the optical space network employs wavelength division multiplexing ISLs with wavelength routing technology to communication satellites and makes routing decisions. With consideration of the network task characterizations such as distribution of task arrival time and task holding duration, simulation experiment results are analyzed and the expression of optical space network queuing delay is given. Both theoretical analysis and simulation results show that features of queuing delay vary with distribution characterizations of the network tasks. It is hoped that the study can be helpful to evaluate the design of constellation networking.     

An anti-attack model based on complex network theory in P2P networks

Complex network theory is a useful way to study many real systems. In this paper, an anti-attack model based on complex network theory is introduced. The mechanism of this model is based on a dynamic compensation process and a reverse percolation process in P2P networks. The main purpose of the paper is: (i) a dynamic compensation process can turn an attacked P2P network into a power-law (PL) network with exponential cutoff; (ii) a local healing process can restore the maximum degree of peers in an attacked P2P network to a normal level; (iii) a restoring process based on reverse percolation theory connects the fragmentary peers of an attacked P2P network together into a giant connected component. In this way, the model based on complex network theory can be effectively utilized for anti-attack and protection purposes in P2P networks.