Structure Of Real Networks Research Articles

Conflict and convention in dynamic networks.

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.

Recovering Social Networks from Panel Data: Identification, Simulations and an Application

It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present methods to recover information on the entire structure of social networks from observational panel data that contains no information on social ties between individuals. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. We describe how high-dimensional estimation techniques can be used to estimate the model based on the Adaptive Elastic Net GMM method. We showcase our method in Monte Carlo simulations using two stylized and two real world network structures. Finally, we employ our method to study tax competition across US states. We find the identified network structure of tax competition differs markedly from the common assumption of tax competition between geographically neighboring states. We analyze the identified social interactions matrix to provide novel insights into the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our method shows how the analysis of social interactions can be usefully extended to economic realms where no network data exists.

Overlapping Community Discovery Algorithm Based on Hierarchical Agglomerative Clustering

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable.

Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows

Cascading failures are a critical vulnerability of complex information or infrastructure networks. Here we investigate the properties of load-based cascading failures in real and synthetic spatially-embedded network structures, and propose mitigation strategies to reduce the severity of damages caused by such failures. We introduce a stochastic method for optimal heterogeneous distribution of resources (node capacities) subject to a fixed total cost. Additionally, we design and compare the performance of networks with N-stable and (N-1)-stable network-capacity allocations by triggering cascades using various real-world node-attack and node-failure scenarios. We show that failure mitigation through increased node protection can be effectively achieved against single-node failures. However, mitigating against multiple node failures is much more difficult due to the combinatorial increase in possible sets of initially failing nodes. We analyze the robustness of the system with increasing protection, and find that a critical tolerance exists at which the system undergoes a phase transition, and above which the network almost completely survives an attack. Moreover, we show that cascade-size distributions measured in this region exhibit a power-law decay. Finally, we find a strong correlation between cascade sizes induced by individual nodes and sets of nodes. We also show that network topology alone is a weak predictor in determining the progression of cascading failures.

Product flow and price change in an agricultural distribution network

We use the structure of a real distribution network of agricultural product in Korea and investigate how the change in the supply may affect the price changes in agents across the distribution network. In particular, we focus on the real network structure of cabbage distribution composed of various types of agents, from farms to consumers, and apply a dynamic model to describe how each participant reacts upon the change of input and output flow of products through the adjustment of price. Our main result implies that the effect of fluctuation of production quantity in the supplying participant can be nontrivial and the consumer price responds to such changes. We believe that our results can be useful to predict what will happen if the agricultural production changes much in the future due to the climate changes.

SgnesR: An R package for simulating gene expression data from an underlying real gene network structure considering delay parameters

BackgroundsgnesR (Stochastic Gene Network Expression Simulator in R) is an R package that provides an interface to simulate gene expression data from a given gene network using the stochastic simulation algorithm (SSA). The package allows various options for delay parameters and can easily included in reactions for promoter delay, RNA delay and Protein delay. A user can tune these parameters to model various types of reactions within a cell. As examples, we present two network models to generate expression profiles. We also demonstrated the inference of networks and the evaluation of association measure of edge and non-edge components from the generated expression profiles.ResultsThe purpose of sgnesR is to enable an easy to use and a quick implementation for generating realistic gene expression data from biologically relevant networks that can be user selected.ConclusionssgnesR is freely available for academic use. The R package has been tested for R 3.2.0 under Linux, Windows and Mac OS X.

Erratum to: Characterizing the structure of large real networks to improve community detection

Erratum to: Characterizing the structure of large real networks to improve community detection

Detecting communities in social networks using label propagation with information entropy

Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks.

Locating the source of spreading in temporal networks

Abstract The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.

Asymmetries arising from the space-filling nature of vascular networks.

Cardiovascular networks span the body by branching across many generations of vessels. The resulting structure delivers blood over long distances to supply all cells with oxygen via the relatively short-range process of diffusion at the capillary level. The structural features of the network that accomplish this density and ubiquity of capillaries are often called space-filling. There are multiple strategies to fill a space, but some strategies do not lead to biologically adaptive structures by requiring too much construction material or space, delivering resources too slowly, or using too much power to move blood through the system. We empirically measure the structure of real networks (18 humans and 1 mouse) and compare these observations with predictions of model networks that are space-filling and constrained by a few guiding biological principles. We devise a numerical method that enables the investigation of space-filling strategies and determination of which biological principles influence network structure. Optimization for only a single principle creates unrealistic networks that represent an extreme limit of the possible structures that could be observed in nature. We first study these extreme limits for two competing principles, minimal total material and minimal path lengths. We combine these two principles and enforce various thresholds for balance in the network hierarchy, which provides a novel approach that highlights the tradeoffs faced by biological networks and yields predictions that better match our empirical data.

Characterizing the structure of large real networks to improve community detection

Community structure is a fundamental feature for many networks. The problem of discovering communities in those networks thus has been attracting a lot of research. However, due to the rapid increase of networks’ scale and the availability of real communities in many networks, the task of detecting communities in large real networks remains a challenging problem. In this paper, we study the structure of various large real networks and their ground-truth community structures and observe an interesting phenomenon: the difference of degrees (abbreviated as dod) of connected nodes follows a heavy-tailed distribution with an approximate power-law tail for large dod, in both original network and community structure but to different extents. With the aim to explore the effect of this observation on identifying communities in real large networks, we propose a weighting strategy and further embed it into two prominent community detection algorithms. Comparisons against the state of the arts demonstrate a very promising performance of the proposed weighting strategy.

Spatio-temporal propagation of cascading overload failures in spatially embedded networks.

Different from the direct contact in epidemics spread, overload failures propagate through hidden functional dependencies. Many studies focused on the critical conditions and catastrophic consequences of cascading failures. However, to understand the network vulnerability and mitigate the cascading overload failures, the knowledge of how the failures propagate in time and space is essential but still missing. Here we study the spatio-temporal propagation behaviour of cascading overload failures analytically and numerically on spatially embedded networks. The cascading overload failures are found to spread radially from the centre of the initial failure with an approximately constant velocity. The propagation velocity decreases with increasing tolerance, and can be well predicted by our theoretical framework with one single correction for all the tolerance values. This propagation velocity is found similar in various model networks and real network structures. Our findings may help to predict the dynamics of cascading overload failures in realistic systems.

Hierarchical organization of multiscale communities in brain networks is non-tree structured

In literature of network science, a group of nodes that are densely connected within the group and are less connected with nodes outside the group is referred to as a [1]. Community structure is a fundamental property of a variety of social, biological and engineering networks. Specifically, communities in brain networks are considered to be associated with functional modules of information processing in the brain [2]. To reveal information processing architecture of the brain, therefore, it is pivotal to know individual communities and their organization in brain networks. Community structure in brain networks is characterized by hierarchical organization, which reflects that functional modules at larger scales are built up from a set of functional modules at smaller scales [3]. A number of mathematical methods for detecting communities in networks have been developed so far [1], but unfortunately few of them can consistently deal with hierarchical organization of multiscale communities. Here we propose a reliable method for detecting hierarchical organization of multiscale communities. Then we examine community structure of real brain networks by use of this method. The proposed method is based on a novel Bayesian formulation of Markov chain. The method has only one parameter, , which comes from the precision of the prior distribution of a random process. The amplitude of controls the resolution of community detection; the smaller its amplitude, the finer the size of detected communities. Quasi-static increase in causes a series of discrete phase transitions; at each transition point a subset of smaller communities (children) agglomerate a larger community (parent), thus leading to a hierarchical organization of multiscale communities. Applying this method to the neuronal network of C. elegans [4] and the macaque cortical network [5], we have found that hierarchical organization of multiscale communities in these networks is non-tree structured: Some child communities have more than one parent community (Figure ​(Figure1).1). These findings suggest efficient architecture for integration of functional modules in brain information processing: The same functional modules at lower levels can be shared by distinct functional modules at higher levels. Figure 1 Hierarchical organization of communities in the neuronal network of C. elegans. Each layer of the hierarchy is shown by horizontal alignment of red squares. Each square indicates a community detected at each layer. The size of each square indicates the ...

A social network model driven by events and interests

A proper understanding of how complex networks grow is important to get insights into the network structure, make predictions of future growth, and enable simulation of large networks. In this paper, we focus on social networks and try to understand, capture and predict dynamic behaviors on social networks. How social networks evolve, i.e. how individuals create and deactivate social ties? It is interesting for several areas such as marketing, web search and recommendation.We propose an agent-based model in which agents represent individuals and social networks evolve driven by events and interests. The model, which we call EIM (Event-Interest Model), incorporates an intuitive idea: individuals begin to interact when participating in the same event, and social ties are formed or reinforced between two individuals if they have similar interests and would like to link to each other.Our model matches better to realistic network structure in terms of a number of statistical properties and critical patterns compared with some recent models for social networks, which suggests that both events and interests may play an important role in shaping the evolution of social networks.

Two Heuristics for Calculating a Shared Risk Link Group Disjoint Set of Paths of Min-Sum Cost

A shared risk link group (SRLG) is a set of links which share a common risk of failure. Routing protocols in Generalized MultiProtocol Label Switching, using distributed SRLG information, can calculate paths avoiding certain SRLGs. For single SRLG failure an end-to-end SRLG-disjoint path pair can be calculated, but to ensure connection in the event of multiple SRLG failures a set with more than two end-to-end SRLG-disjoint paths should be used. Two heuristic, the Conflicting SRLG-Exclusion Min Sum (CoSE-MS) and the Iterative Modified Suurballes's Heuristic (IMSH), for calculating node and SRLG-disjoint path pairs, which use the Modified Suurballes's Heuristic, are reviewed and new versions (CoSE-MScd and IMSHd) are proposed, which may improve the number of obtained optimal solutions. Moreover two new heuristics are proposed: kCoSE-MScd and kIMSHd, to calculate a set of $$k$$k node and SRLG-disjoint paths, seeking to minimize its total cost. To the best of our knowledge these heuristics are a first proposal for seeking a set of $$k\, (k>2)$$k(k>2) node and SRLG-disjoint paths of minimal additive cost. The performance of the proposed heuristics is evaluated using a real network structure, where SRLGs were randomly defined. The number of solutions found, the percentage of optimal solutions and the relative error of the sub-optimal solutions are presented. Also the CPU time for solving the problem in a path computation element is reported.

Optimization for design and operation of natural gas transmission networks

We consider the problem of designing a new natural gas transmission network or expanding an existing network while minimizing the total investment and operating costs. We develop an integrated large-scale mixed-integer nonlinear optimization model to determine pipelines in the network, compressor stations and their capacities, timings of these installations in a multi-period planning horizon, and natural gas purchase and steady-state flow decisions for each period in the network. The model is solvable using state-of-the-art solution methodologies available online.Employing our modeling and the solution methodology for its solution, we conduct computational studies on various test instances generated using realistic network structure and data based on the natural gas network in Turkey as well as data from literature. Our analysis provides insights into sensitivity of network configuration and operations to the number of periods within the planning horizon and cost parameters as well as into strategic decision making for design and expansion of natural gas networks.

Deciphering the global organization of clustering in real complex networks

We uncover the global organization of clustering in real complex networks. To this end, we ask whether triangles in real networks organize as in maximally random graphs with given degree and clustering distributions, or as in maximally ordered graph models where triangles are forced into modules. The answer comes by way of exploring m-core landscapes, where the m-core is defined, akin to the k-core, as the maximal subgraph with edges participating in at least m triangles. This property defines a set of nested subgraphs that, contrarily to k-cores, is able to distinguish between hierarchical and modular architectures. We find that the clustering organization in real networks is neither completely random nor ordered although, surprisingly, it is more random than modular. This supports the idea that the structure of real networks may in fact be the outcome of self-organized processes based on local optimization rules, in contrast to global optimization principles.

An evolving model of online bipartite networks

Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

Detectability of communities in heterogeneous networks

Communities are fundamental entities for the characterization of the structure of real networks. The standard approach to the identification of communities in networks is based on the optimization of a quality function known as modularity. Although modularity has been at the center of an intense research activity and many methods for its maximization have been proposed, not much is yet known about the necessary conditions that communities need to satisfy in order to be detectable with modularity maximization methods. Here, we develop a simple theory to establish these conditions, and we successfully apply it to various classes of network models. Our main result is that heterogeneity in the degree distribution helps modularity to correctly recover the community structure of a network and that, in the realistic case of scale-free networks with degree exponent γ<2.5, modularity is always able to detect the presence of communities.

Towards a bipartite graph modeling of the internet topology

Modeling the properties of the Internet topology aims at generating large scale artificial IP networks that mimic properties of real ones for simulation purposes. Current models typically consider the Internet as a simple graph where edges are point-to-point connections between routers. This approach does not take into account point-to-multipoint connections that exist at lower layers in the network, e.g. layer-2 clouds, such as Ethernet switches or MPLS networks. Instead, such physical point-to-multipoint connections are modeled as several logical IP level point-to-point connections.In this paper, we rely on recent developments in topology discovery based on IGMP probing that allows for revealing part of the network’s layer-2 structure. We take advantage of this additional knowledge for proposing an Internet model based on bipartite graphs considering both point-to-point and point-to-multipoint connections. Our model remains simple: it only takes as input the node degree sequence for both layer-2 and layer-3 nodes, randomly generates a bipartite graph respecting those distributions, and then derives the corresponding layer-3 topology. We show that, despite the simplicity of our model, realistic network properties, such as high local density, emerge naturally. This is in contrast with the now common belief that such properties can only appear with more intricate models or if explicitly injected in random models. Besides, we also provide evidences of how the analysis performed at the bipartite level might shed light on important properties of the real network structure. Finally, we propose and evaluate a bipartite graph generator based on our model that only takes two synthetic node degree distributions as input.