Complex Network Topology Research Articles

GerbilSphere: Inner sphere network visualization

Advanced techniques are needed to understand the underlying topologies of large complex networks. This paper introduces a novel way to address the issue of visualizing large-scale network topologies. We propose an inner sphere visualization method that projects the network topology on the inside of a sphere. User navigation around the network is accomplished through moving the sphere around the user’s point of view. Previous research has shown that the spatial cognition ability in humans greatly affects the usefulness of a user interface. In this direction, we performed two empirical experiments to test the usefulness of viewing topologies on a sphere compared to a flat surface. The user study indicates that network navigation on a sphere is faster but can also be confusing. Thus, we add guidance tips to create a more intuitive user interface and to improve navigability. Our inner sphere visualization method is implemented as a tool for dynamic interactive network visualization called GerbilSphere (available at http://cse.unr.edu/∼mgunes/gerbilSphere).

Metacommunity theory explains the emergence of food web complexity

Food webs are highly complex ecological networks, dynamic in both space and time. Metacommunity models are now at the core of unified theories of biodiversity, but to date they have not addressed food web complexity. Here we show that metacommunity theory can explain the emergence of species-rich food webs with complex network topologies. Our analysis shows that network branching in the food web is maximized at intermediate colonization rates and limited dispersal scales, which also leads to concomitant peaks in species diversity. Increased food web complexity and species diversity are made possible by the structural role played by network branches that are supported by omnivore and generalist feeding links. Thus, in contrast to traditional food web theory, which emphasizes the destabilizing effect of omnivory feeding in closed systems, metacommunity theory predicts that these feeding links, which are commonly observed in empirical food webs, play a critical structural role as food webs assemble in space. As this mechanism functions at the metacommunity level, evidence for its operation in nature will be obtained through multiscale surveys of food web structure. Finally, we apply our theory to reveal the effects of habitat destruction on network complexity and metacommunity diversity.

Comparing Signaling Networks between Normal and Transformed Hepatocytes Using Discrete Logical Models

Substantial effort in recent years has been devoted to constructing and analyzing large-scale gene and protein networks on the basis of "omic" data and literature mining. These interaction graphs provide valuable insight into the topologies of complex biological networks but are rarely context specific and cannot be used to predict the responses of cell signaling proteins to specific ligands or drugs. Conversely, traditional approaches to analyzing cell signaling are narrow in scope and cannot easily make use of network-level data. Here, we combine network analysis and functional experimentation by using a hybrid approach in which graphs are converted into simple mathematical models that can be trained against biochemical data. Specifically, we created Boolean logic models of immediate-early signaling in liver cells by training a literature-based prior knowledge network against biochemical data obtained from primary human hepatocytes and 4 hepatocellular carcinoma cell lines exposed to combinations of cytokines and small-molecule kinase inhibitors. Distinct families of models were recovered for each cell type, and these families clustered topologically into normal and diseased sets.

Natural Human Mobility Patterns and Spatial Spread of Infectious Diseases

We investigate a model for spatial epidemics explicitly taking into account bi-directional movements between base and destination locations on individual mobility networks. We provide a systematic analysis of generic dynamical features of the model on regular and complex metapopulation network topologies and show that significant dynamical differences exist to ordinary reaction-diffusion and effective force of infection models. On a lattice we calculate an expression for the velocity of the propagating epidemic front and find that in contrast to the diffusive systems, our model predicts a saturation of the velocity with increasing traveling rate. Furthermore, we show that a fully stochastic system exhibits a novel threshold for attack ratio of an outbreak absent in diffusion and force of infection models. These insights not only capture natural features of human mobility relevant for the geographical epidemic spread, they may serve as a starting point for modeling important dynamical processes in human and animal epidemiology, population ecology, biology and evolution.

Complex networks analysis of obstructive nephropathy data

Congenital obstructive nephropathy (ON) is one of the most frequent nephropathy observed among newborns and children, and the first cause of end-stage renal diseases treated by dialysis or transplantation. This pathology is characterized by the presence of an obstacle in the urinary tract, e.g., stenosis or abnormal implantation of the urethra in the kidney. In spite of important advances, pathological mechanisms are not yet fully understood. In this contribution, the topology of complex networks created upon vectors of features for control and ON subjects is related with the severity of the pathology. Nodes in these networks represent genetic and metabolic profiles, while connections between them indicate an abnormal relation between their expressions. Resulting topologies allow discriminating ON subjects and detecting which genetic or metabolic elements are responsible for the malfunction.

Return probability for random walks on scale-free complex trees

The time course of random processes usually differs depending on the topology of complex networks which are a substrate for the process. However, as this Letter demonstrates, the first-return as well as the survival probabilities for random walks on the scale-free (SF) trees decay in time according to the same invariant power-law behavior. This means that both quantities are independent of the node power-law degree distributions which are distinguished by different scaling exponents. It is also shown here that the crucial property of the networks, affecting the dynamics of random walks, is their tree-like topology and not SF architecture. All analytical results quantifying these predictions have been verified through extensive computer simulations.

Biology, methodology or chance? The degree distributions of bipartite ecological networks.

The distribution of the number of links per species, or degree distribution, is widely used as a summary of the topology of complex networks. Degree distributions have been studied in a range of ecological networks, including both mutualistic bipartite networks of plants and pollinators or seed dispersers and antagonistic bipartite networks of plants and their consumers. The shape of a degree distribution, for example whether it follows an exponential or power-law form, is typically taken to be indicative of the processes structuring the network. The skewed degree distributions of bipartite mutualistic and antagonistic networks are usually assumed to show that ecological or co-evolutionary processes constrain the relative numbers of specialists and generalists in the network. I show that a simple null model based on the principle of maximum entropy cannot be rejected as a model for the degree distributions in most of the 115 bipartite ecological networks tested here. The model requires knowledge of the number of nodes and links in the network, but needs no other ecological information. The model cannot be rejected for 159 (69%) of the 230 degree distributions of the 115 networks tested. It performed equally well on the plant and animal degree distributions, and cannot be rejected for 81 (70%) of the 115 plant distributions and 78 (68%) of the animal distributions. There are consistent differences between the degree distributions of mutualistic and antagonistic networks, suggesting that different processes are constraining these two classes of networks. Fit to the MaxEnt null model is consistently poor among the largest mutualistic networks. Potential ecological and methodological explanations for deviations from the model suggest that spatial and temporal heterogeneity are important drivers of the structure of these large networks.

Analyzing the Resilience of Complex Supply Network Topologies Against Random and Targeted Disruptions

In this paper, we study the resilience of supply networks against disruptions and provide insights to supply chain managers on how to construct a resilient supply network from the perspective of complex network topologies. Our goal is to study how different network topologies, which are created from different growth models, affect the network's resilience against both random and targeted disruptions. Of particular interest are situations where the type of disruption is unknown. Using a military logistic network as a case study, we propose new network resilience metrics that reflect the heterogeneous roles (e.g., supply, relay, and demand) of nodes in supply networks. We also present a hybrid and tunable network growth model called Degree and Locality-based Attachment (DLA), in which new nodes make connections based on both degree and locality. Using computer simulations, we compare the resilience of several supply network topologies that are generated with different growth models. The results show that the new resilience metrics can capture important resilience requirements for supply networks very well. We also found that the supply network topology generated by the DLA model provides balanced resilience against both random and targeted disruptions.

Network Topology for the Application Research of Electrical Control System Fault Propagation

Fault diagnosis and failure analysis is crucial issue in the process of health management and predicative maintenance for large complex system. Fault propagation and its cause-effect relationship in the system are of priority with regard to the fault diagnosis. The fundamentals for modeling the network topology are presented according to the characteristics of the electrical control system connection relationship and its functionality, a complex network topology model was built subsequently. The cause and effect relationships of the components are mapped to the relationships of the vertices in a complex network. The search method for the fault propagation path from the source vertex to the target vertex is studied using node coordinates connections, and application research of electrical control system fault propagation is shown and discussed in the paper. The proposed method is practical in dealing with the fault analysis and evaluation for large complex systems.

Competitive cluster growth on networks: Complex dynamics and survival strategies

We extend the study of a model of competitive cluster growth in an active medium from a regular topology to a complex network topology; this is done by adding nonlocal connections with probability p to sites on a regular lattice, thus enabling one to interpolate between regularity and full randomness. The model on networks demonstrates high sensitivity to small changes in initial configurations, which we characterize using damage spreading. The main focus of this paper is, however, the devising of survival strategies through selective networking, to alter the fate of an arbitrarily chosen cluster: whether this be to revive a dying cluster to life, or to make a weak survivor into a stronger one. Although such goals are typically achieved by networking with relatively small clusters, our results suggest that it ought to be possible also to network successfully with peers and larger clusters. The main indication of this comes from the probability distributions of mass differences between survivors and their immediate neighbours, which show an interesting universality; they suggest strategies for winning against the odds.

Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation

Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells, we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells. We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.

The chaotic dynamics of the social behavior selection networks in crowd simulation

This paper researches the nonlinear dynamics of the behavior selection networks (BSN) model by virtue of which we can understand the origin of flocking behaviors in social networks. To commentate the notion of BSN, this article introduces a social behavior selection model for evolutionary dynamics of behaviors in social networks that exhibits a rich set of emergent behaviors of evolution. For behavioral networks with different complex networks topology, we analyze the nonlinear dynamics including the chaotic dynamics by the numerical simulation tools. With changing the topological structure, the behavioral networks behave affluent dynamical phenomena. Lastly, we draw the conclusion and paste the prospection about the networks model.

Transit network design: A procedure and an application to a large urban area

This paper describes a procedure for solving the bus network design problem and its application in a large urban area (the city of Rome), characterized by: (a) a complex road network topology; (b) a multimodal public transport system (rapid rail transit system, buses and tramways lines); (c) a many-to-many transit demand. The solving procedure consists of a set of heuristics, which includes a first routine for the route generation based on the flow concentration process and a parallel genetic algorithm for finding a sub-optimal set of routes with the associated frequencies. The final goal of the research is to develop an operative tool to support the mobility agency of Rome for the bus network design phase.

Linear Control Theory for Gene Network Modeling

Systems biology is an interdisciplinary field that aims at understanding complex interactions in cells. Here we demonstrate that linear control theory can provide valuable insight and practical tools for the characterization of complex biological networks. We provide the foundation for such analyses through the study of several case studies including cascade and parallel forms, feedback and feedforward loops. We reproduce experimental results and provide rational analysis of the observed behavior. We demonstrate that methods such as the transfer function (frequency domain) and linear state-space (time domain) can be used to predict reliably the properties and transient behavior of complex network topologies and point to specific design strategies for synthetic networks.

A CONSTRAINT-BASED STOICHIOMETRIC MODEL OF THE STEROIDOGENIC NETWORK OF ZEBRAFISH (DANIO RERIO)

The metabolic process of steroidogenesis exhibits a complex biochemical network topology as the activity of various steroidogenic enzymes control cholesterol metabolism to steroid hormone derivatives. In this paper, a stoichiometric reconstruction of a sub-set of 65 reactions from the zebrafish (Danio rerio) steroidogenic network is presented and simulated using uniform reaction constraints. The reconstruction defined a set of 65 enzyme catalyzed reactions and 37 exchange or transport reactions for steroid metabolites. The reconstructed reactions were inclusive of cholesterol and androgen/estrogen metabolism. Biased (statement of network objective function) and un-biased (no statement of objective function) analyses were applied to identify network properties dependent on reaction stoichiometry. Random sampling of flux distributions through the network identified highly-correlated reaction sets that corresponded to the catalysis of steroid metabolites of physiological relevance. Subsequently, optimal flux distributions through network pathways were determined for the production of the three steroidogenic metabolites of: 11-deoxycorticosterone, testosterone and 17β-estradiol. Furthermore, flux variability analyses revealed and confirmed optimal network fluxes through physiologically feasible pathways. The stoichiometric dependence of reactions was also confirmed by conducting deletions of reactions utilized for the optimal production of 17β-estradiol. This paper demonstrates the potential application of constraint-based reconstruction and simulation techniques in enabling the construction of deterministic and predictive physiological models. This acknowledgement is poignant considering the susceptibility of the steroidogenic network to environmental and anthropogenic stressors.

Pore space analysis of beech wood: The vessel network

AbstractWater transport in wood is vital for the survival of trees. With synchrotron radiation X-ray tomographic microscopy (SRXTM), it has become possible to characterize and quantify the three-dimensional (3D) network formed by vessels that are responsible for longitudinal transport. In the present study, the spatial size dependence of vessels and the organization inside single growth rings in terms of vessel-induced porosity was studied by SRXTM. Network characteristics, such as connectivity, were deduced by digital image analysis from the processed tomographic data and related to known complex network topologies.

Coupled Simulation-Measurements Platform for the Evaluation of Frequency Reuse in the 2.45 GHz ISM Band for Multimode Nodes with Multiple Antennas

We address the problem of efficiently evaluating performance of concurrent radio links on overlapped channels. In complex network topologies with various standards and frequency channels, simulating a realistic PHY layer communication is a key point. The presented coupled simulation-measurement platform offers a very promising way of rapidly modelling and validating effective performance of multimode, multichannel and multiantenna radio nodes. An accurate analysis of radio channel is performed and then realistic performance with or without antenna processing is shown, verifying theoretical performance. Finally, available performance of concurrent communications on overlapped channels is exposed, showing that this approach is viable to enhance network capacity.

MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS

The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modifications in their intrinsic organization. For instance, the network of transportation routes between cities and towns (hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways (paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation (in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.

Control and Flocking of Networked Systems via Pinning

One of the main purposes for the study of complex network topology and modeling is to understand the influence of network structure on its function and consequently to find effective ways to improve network performance. This paper surveys some recent advances in pinning control approaches to making a dynamical network have a desired behavior. For a network with fixed topology, we review the feasibility, stability and effectiveness of pinning control and adaptive pinning control. We also review pinning-based flocking control of mobile multi-agent networked systems with time-varying topologies. Finally, we point out some future directions.

Graph annotations in modeling complex network topologies

The coarsest approximation of the structure of a complex network, such as the Internet, is a simple undirected unweighted graph. This approximation, however, loses too much detail. In reality, objects represented by vertices and edges in such a graph possess some nontrivial internal structure that varies across and differentiates among distinct types of links or nodes. In this work, we abstract such additional information as network annotations . We introduce a network topology modeling framework that treats annotations as an extended correlation profile of a network. Assuming we have this profile measured for a given network, we present an algorithm to rescale it in order to construct networks of varying size that still reproduce the original measured annotation profile. Using this methodology, we accurately capture the network properties essential for realistic simulations of network applications and protocols, or any other simulations involving complex network topologies, including modeling and simulation of network evolution. We apply our approach to the Autonomous System (AS) topology of the Internet annotated with business relationships between ASs. This topology captures the large-scale structure of the Internet. In depth understanding of this structure and tools to model it are cornerstones of research on future Internet architectures and designs. We find that our techniques are able to accurately capture the structure of annotation correlations within this topology, thus reproducing a number of its important properties in synthetically-generated random graphs.