Structure Of Real Networks Research Articles

Modeling Public Opinion Polarization in Group Behavior by Integrating SIRS-Based Information Diffusion Process

Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization.

Complex spatial networks: Theory and geospatial applications

AbstractComplex systems modeling approaches offer the means to examine the way in which local interactions between system components form emergent systems. Using these bottom‐up modeling approaches in combination with geographic information systems (GIS) and geospatial data, the complexity inherent to spatial phenomena including geographical, urban, ecological, or geophysical systems can be captured and represented. Scientific research in the field of network science also uses a complex systems approach to conceptualize, model, and analyze geospatial systems as networks. Despite having common characteristics, complexity, geographic information, and network sciences are not yet fully integrated. Therefore, the main objective of this article is to provide a comprehensive review of scientific research related to network theory and to evaluate the potential of their integration with complex systems modeling approaches originating in the field of geographic information science (GISc). This article finds that existing literature focuses on characterizing static spatial network structures to better understand the dynamics that take place on or within them. This article argues for a necessity in research advancements to explore the way in which real spatial network structures evolve in response to spatial dynamics and advocates for the integration of geographic automata systems (GAS) modeling approaches with networks to do so. The mathematical foundation for graph theory, including the measures that are used to describe networks and the theoretical graph types, are introduced. Geospatial applications of networks and graph theory are also presented. Examples of network‐based automata models are presented as avenues for future research work in evolving spatial networks as part of GISc and computational geography.

Resilience of Urban Street Network Configurations under Low Demands

Urban street networks are subject to a variety of random disruptions. The impact of movement restrictions (e.g., one-way or left-turn restrictions) on the ability of a network to overcome these disruptions—that is, its resilience—has not been thoroughly studied. To address this gap, this paper investigates the resilience of one-way and two-way square grid street networks with and without left turns under light traffic conditions. Networks are studied using a simplified routing algorithm that can be examined analytically and a microsimulation that describes detailed vehicle dynamics. In the simplified method, routing choices are enumerated for all possible origin–destination (OD) combinations to identify how the removal of a link affects operations, both when knowledge of the disruption is and is not available at the vehicle’s origin. Disruptions on two-way networks that allow left turns tend to have little impact on travel distances because of the availability of multiple shortest paths between OD pairs and the flexibility in route modification. Two-way networks that restrict left turns at intersections only have a single shortest-distance path between any OD pair and thus experience larger increases in travel distance, even when the disruption is known ahead of time. One-way networks sometimes have multiple shortest-distance routes and thus travel distances increase less than two-way network without left turns when links are disrupted. These results reveal a clear tradeoff between improved efficiency and reduced resilience for networks that have movement restrictions, and can be used as a basis to study network resilience under more congested scenarios and in more realistic network structures.

Consequences of Dale's law on the stability-complexity relationship of random neural networks.

In the study of randomly connected neural network dynamics there is a phase transition from a simple state with few equilibria to a complex state characterized by the number of equilibria growing exponentially with the neuron population. Such phase transitions are often used to describe pathological brain state transitions observed in neurological diseases such as epilepsy. In this paper we investigate how more realistic heterogeneous network structures affect these phase transitions using techniques from random matrix theory. Specifically, we parametrize the network structure according to Dale's law and use the Kac-Rice formalism to compute the change in the number of equilibria when a phase transition occurs. We also examine the condition where the network is not balanced between excitation and inhibition causing outliers to appear in the eigenspectrum. This enables us to compute the effects of different heterogeneous network connectivities on brain state transitions, which can provide insights into pathological brain dynamics.

K-core structure of real multiplex networks

The authors show that interlayer correlations are responsible for the strong k-core structure found in real world multiplex networks. The more heterogeneous are the degree distributions of the layers, the more pivotal is the role of degree correlations. The less heterogeneous are the degree distributions, the more crucial is the role of correlations at the level of node similarities. These findings may help in identifying influential spreaders in real world multiplex systems.

Metacommunity robustness of plant-fly-wasp tripartite networks with specialization to habitat loss.

Recent observations have found plant-species-specific fly-host selection (i.e., specialization) of wasp parasitoids (wasps) in plant-fly-wasp (P-F-W) tripartite networks, yet no study has explored the dynamical implications of such high-order specialization for the persistence of this network. Here we develop a patch-dynamic framework for a unique P-F-W tripartite network with specialization observed in eastern Tibetan Plateau and explore its metacommunity robustness to habitat loss. We show that specialization in parasitoidism promotes fly species diversity, while the richness of both plant and wasp decreases. Compared to other two null models, real network structure favors plant species coexistence but increases the extinction risk for both flies and wasps. However, these effects of specialization and network structure would be weakened and ultimately disappear with increasing habitat loss. Interestingly, intermediate levels of habitat loss can maximize the diversity of flies and wasps, while increasing or decreasing habitat loss results in more species losses, supporting intermediate disturbance hypothesis. Finally, we observe that high levels of habitat loss initiate a bottom-up cascade of species extinction from plants to both flies and wasps, resulting in a rapid collapse of the whole tripartite networks. Overall, this theoretical framework is the first attempt to characterize the dynamics of whole tripartite metacommunities interacting in realistic high-order ways, offering new insights into complex multipartite networks.

Bayonet-corpus: a trajectory prediction method based on bayonet context and bidirectional GRU

Predicting travel trajectory of vehicles can not only provide personalized services to users, but also have a certain effect on traffic guidance and traffic control. In this paper, we build a Bayonet-Corpus based on the context of traffic intersections, and use it to model a traffic network. Besides, Bidirectional Gated Recurrent Unit (Bi-GRU) is used to predict the sequence of traffic intersections in one single trajectory. Firstly, considering that real traffic networks are usually complex and disorder and cannot reflect the higher dimensional relationship among traffic intersections, this paper proposes a new traffic network modeling algorithm based on the context of traffic intersections: inspired by the probabilistic language model, a Bayonet-Corpus is constructed from traffic intersections in real trajectory sequence, so the high-dimensional similarity between corpus nodes can be used to measure the semantic relation of real traffic intersections. This algorithm maps vehicle trajectory nodes into a high-dimensional space vector, blocking complex structure of real traffic network and reconstructing the traffic network space. Then, the bayonets sequence in real traffic network is mapped into a matrix. Considering the trajectories sequence is bidirectional, and Bi-GRU can handle information from forward and backward simultaneously, we use Bi-GRU to bidirectionally model the trajectory matrix for the purpose of prediction.

Research on the flex network structure for testbeds of distribution system

Objective: The real distribution network testbed is a testing and experiment environment for the popularization and application of new technologies, new equipment and new methods in distribution network. In order to simulate the operation of the actual distribution network as much as possible, the grid structure of the real testbed should be consistent with the actual distribution network. However, due to site conditions and investment constraints, the construction of a flexible grid structure and the conversion of different connection modes through the on-off configuration of distribution switches is the key to the construction of real distribution network testbed. Methods: This paper analyzes the structural characteristics of typical connection modes of domestic 10kV distribution lines, puts forward the decomposition and reconstruction method of typical connection modes based on unit structure, and then puts forward the grid structure of real distribution network testbed which can be switched flexibly. Results: By using the grid structure, the typical connection modes of all kinds of cables, overhead and hybrid lines, as well as the atypical connection modes with branch lines can be flexibly switched, which can meet the requirements of all kinds of tests and experiments of the real distribution network testbed. Conclusion: Based on the actual engineering example, the method proposed in this paper is scientific and effective, and provides method support for the construction scheme of real distribution network testbed.

Exchangeable Random Measures for Sparse and Modular Graphs with Overlapping Communities

SummaryWe propose a novel statistical model for sparse networks with overlapping community structure. The model is based on representing the graph as an exchangeable point process and naturally generalizes existing probabilistic models with overlapping block structure to the sparse regime. Our construction builds on vectors of completely random measures and has interpretable parameters, each node being assigned a vector representing its levels of affiliation to some latent communities. We develop methods for efficient simulation of this class of random graphs and for scalable posterior inference. We show that the approach proposed can recover interpretable structure of real world networks and can handle graphs with thousands of nodes and tens of thousands of edges.

Optimal timescale for community detection in growing networks

Time-stamped data are increasingly available for many social, economic, and information systems that can be represented as networks growing with time. The World Wide Web, social contact networks, and citation networks of scientific papers and online news articles, for example, are of this kind. Static methods can be inadequate for the analysis of growing networks as they miss essential information on the system’s dynamics. At the same time, time-aware methods require the choice of an observation timescale, yet we lack principled ways to determine it. We focus on the popular community detection problem which aims to partition a network’s nodes into meaningful groups. We use a multi-layer quality function to show, on both synthetic and real datasets, that the observation timescale that leads to optimal communities is tightly related to the system’s intrinsic aging timescale that can be inferred from the time-stamped network data. The use of temporal information leads to drastically different conclusions on the community structure of real information networks, which challenges the current understanding of the large-scale organization of growing networks. Our findings indicate that before attempting to assess structural patterns of evolving networks, it is vital to uncover the timescales of the dynamical processes that generated them.

Smeared phase transitions in percolation on real complex networks

Percolation on complex networks is used both as a model for dynamics on networks, such as network robustness or epidemic spreading, and as a benchmark for our models of networks, where our ability to predict percolation measures our ability to describe the networks themselves. In many applications, correctly identifying the phase transition of percolation on real-world networks is of critical importance. Unfortunately, this phase transition is obfuscated by the finite size of real systems, making it hard to distinguish finite size effects from the inaccuracy of a given approach that fails to capture important structural features. Here, we borrow the perspective of smeared phase transitions and argue that many observed discrepancies are due to the complex structure of real networks rather than to finite size effects only. In fact, several real networks often used as benchmarks feature a smeared phase transition where inhomogeneities in the topological distribution of the order parameter do not vanish in the thermodynamic limit. We find that these smeared transitions are sometimes better described as sequential phase transitions within correlated subsystems. Our results shed light not only on the nature of the percolation transition in complex systems, but also provide two important insights on the numerical and analytical tools we use to study them. First, we propose a measure of local susceptibility to better detect both clean and smeared phase transitions by looking at the topological variability of the order parameter. Second, we highlight a shortcoming in state-of-the-art analytical approaches such as message passing, which can detect smeared transitions but not characterize their nature.

Overlapping community detection via preferential learning model

Overlapping community detection has become one of the most important tasks in network analysis because it can better reflect the characteristics of the real network structure. Research on overlapping communities detection cannot only promote the study of network functions but also bring insight into a deep understanding of the network topology. In this paper, we get inspiration from learning behaviors and information exchanges in the real world, and propose a dynamic relationship-based preference learning model applied to dynamic systems. We apply this model to the label propagation algorithm and present an overlapping community detection algorithm based on Preferential Learning and Label Propagation Algorithm, called PLPA. The algorithm regards the network as a dynamic system. Each node selects the learning target to update its own label according to the degree of preference to its neighbor nodes. With learning, the information in the system will finally reach a steady state. We consider nodes that have the same label belonging to the same community, so that the overlapping community structure in the network will be separated. In the experiments, we verified the performance of our algorithm through real-world and synthetic networks. Results show that PLPA not only has better performance than many state-of-the-art algorithms on most data sets, but it is also more applicable to some networks with ambiguous community structure, especially sparse networks.

Simulating Transport Networks With a <italic>Physarum</italic> Foraging Model

Designing effective transport networks can be considered as one of the most debated problems in the area of computational intelligence. Some nature-inspired algorithms have shown excellent abilities in the adaptive network construction. In this aspect, a unique creature, called Physarum , has exhibited the computing capacity to optimize protoplasmic networks connecting distributed food sources. This inspires our work to design a Physarum foraging platform for constructing transport networks. Specifically, the traditional Physarum foraging model is adapted to construct transport networks in China. In order to get close to the real scenario, practical data are collected to build the environment of the Physarum foraging model and the structure of real transport networks. Some measurements in the domain of complex networks, such as average path length, network efficiency, topology robustness, and functional robustness, are used for performance comparison. The experimental results demonstrate that Physarum foraging models excel in constructing highly efficient and robust networks, which can be utilized for directing the design of transport networks in the real world.

BciNet: A Biased Contest-Based Crowdsourcing Incentive Mechanism Through Exploiting Social Networks

Crowdsourcing has proved to be a splendid tool to aggregate the knowledge from a pool of individuals in order to perform abundant microtasks efficiently. Recently, with the explosive growth of online social network, Word of Mouth (WoM)-based crowdsourcing systems have emerged, in which besides conducting the tasks by themselves, participants simultaneously recruit other individuals through exploiting their social networks to help solve crowdsourced tasks. This crowdsourcing paradigm can greatly facilitate to grow the pool of crowdworkers. However, there exist two conflicting challenges in designing an effective WoM-based incentive mechanism: 1) sybil attack and 2) heterogeneous effect of participants. That is, intuitively, incentivizing (usually compensating for) common-ability individuals will inevitably stimulate the behavior of sybil attack (i.e., some individuals create multiple sybils, and split the total efforts into those sybils to expect more compensation). This paper proposes a novel biased contest-based crowdsourcing incentive mechanism through exploiting social networks (BciNet), aiming to balance those two conflicting objectives. BciNet is composed of two phases. First, based on spreading activation model, an enhanced geometric virtual point dissemination mechanism is able to provide sybil-proof property and accommodate the realistic social network structure. Second, based on participants’ virtual points, a biased contest gives more reward to less able participants. Through carefully calibrating the bias factor, simulation results based on the real dataset show that BciNet can greatly improve the amount of participants’ effort levels, and actually be robust against the sybil attack. In brief, for a practical incentive mechanism, the methodology to address conflicting goals is to put rational individuals into dilemma: to be sybil or not to be, it is the problem, i.e., the potential gain from the sybils in the second phase may be offset by the loss in the first phase.

Modeling and Analyzing the Influence of Multi-Information Coexistence on Attention

The research of information dissemination in online social networks has always been a hot topic in the field of network public opinion research. The traditional information dissemination model has a strong dependence on the complete network topology structure and the information of participants’ neighbors. In real-network hot events, it is difficult to obtain the real-network topology structure, and most participants who participate in network events do not have social relationships. We propose a multiple information coexistence attention model (MICAM), which does not depend on the network topology and the information of participants’ neighbors. According to the real news data, the corresponding mapping table of model parameters is established. The model is more in line with the actual situation of social networks due to parameter mapping tables. The MICAM model is adopted to study the changes of various types of information attention in the case of multi-information coexistence. The accuracy of the model is proved by comparing the simulation results with the actual data. In addition, the factors that affect the attention of participants are also studied.

Percolation of hierarchical networks and networks of networks

Much work has been devoted to studying percolation of networks and interdependent networks under varying levels of failures. Researchers have considered many different realistic network structures such as scale-free networks, spatial networks, and more. However, thus far no study has analyzed a system of hierarchical community structure of many networks. For example, infrastructure across cities is likely distributed with nodes tightly connected within small neighborhoods, somewhat less connected across the whole city, and even fewer connections between cities. Furthermore, while previous work identified interconnected nodes, those nodes with links outside their neighborhood, to be more likely to be attacked or to fail, in a hierarchical structure nodes can be interconnected in different layers (between neighborhoods, between cities, etc.). We consider here the case where the nodes with interconnections at the highest level of the hierarchy are most likely to fail, followed by those with interconnections at the next level, etc. This is because nodes at higher levels of the hierarchy have the longest links as well as having more flow passing through them. We develop an analytic solution for percolation of both single and interdependent networks of this structure and verify our theory through simulations. We find that, depending on the number of levels in the hierarchy, there may be multiple transitions in the giant component (fraction of connected nodes), as the network separates at the various levels. Our results show that these multiple jumps are a feature of hierarchical networks and can affect the vulnerability of infrastructure networks.

Morphometric Reconstruction of Coronary Vasculature Incorporating Uniformity of Flow Dispersion.

Experimental limitations in measurements of coronary flow in the beating heart have led to the development of in silico models of reconstructed coronary trees. Previous coronary reconstructions relied primarily on anatomical data, including statistical morphometry (e.g., diameters, length, connectivity, longitudinal position). Such reconstructions are non-unique, however, often leading to unrealistic predicted flow features. Thus, it is necessary to impose physiological flow constraints to ensure realistic tree reconstruction. Since a vessel flow depends on its diameter to fourth power, diameters are the logical candidates to guide vascular reconstructions to achieve realistic flows. Here, a diameter assignment method was developed where each vessel diameter was determined depending on its downstream tree size, aimed to reduce flow dispersion to within measured range. Since the coronary micro-vessels are responsible for a major portion of the flow resistance, the auto regulated coronary flow was analyzed in a morphometry-based reconstructed 400 vessel arterial microvascular sub-tree spanning vessel orders 1–6. Diameters in this subtree were re-assigned based on the flow criteria. The results revealed that diameter re-assignment, while adhering to measured morphometry, significantly reduced the flow dispersion to realistic levels while adhering to measured morphometry. The resulting network flow has longitudinal pressure distribution, flow fractal nature, and near-neighboring flow autocorrelation, which agree with measured coronary flow characteristics. Collectively, these results suggest that a realistic coronary tree reconstruction should impose not only morphometric data but also flow considerations. The work is of broad significance in providing a novel computational framework in the field of coronary microcirculation. It is essential for the study of coronary circulation by model simulation, based on a realistic network structure.

Distributed Rumor Blocking With Multiple Positive Cascades

Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is assumed in the existing works that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there is multiple positive cascades generated by different agents. For the multiple-cascade diffusion, we propose the peer-to-peer independent cascade model for private social communications. The main contribution of this paper is an analysis of the rumor blocking effect (i.e., the number of the users activated by rumor) when the agents noncooperatively generate the positive cascades. We show that the rumor blocking effect provided by the Nash equilibrium will not be arbitrarily worse even if the positive cascades are generated noncooperatively. In addition, we give a discussion on how the cascade priority and activation order affect the rumor blocking problem. We experimentally examine the Nash equilibrium of the proposed games by simulations done on real social network structures.

Social hotspot propagation dynamics model based on heterogeneous mean field and evolutionary games

In the field of social network analysis, information diffusion is a focus of current research. Taking into account the real topological relations among the participants and the psychological characteristics of the users, in this paper, a hotspot propagation model based on heterogeneous mean field and evolutionary games is proposed. First, in real social networks, the changes of hotspot’s trend could lead to the dynamic changes of users’ willingness to participate in the hot topic. This effect is reflected in the dynamic behaviors among the users. In this work, based on the evolutionary games, a dynamic evolution mechanism for users’ willingness to participate in hotspot is constructed and dynamically adjusts the infection rate of information dissemination model. Second, in view of the heterogeneity of the real network structure and the complexity of the heterogeneous mean field, graphical evolutionary game is introduced to improve the heterogeneous mean field. Thus, a new dynamics model of information dissemination is constructed based on graphical evolutionary game. Finally, considering the dynamic behavior among the nodes and the heterogeneity of real social networks, we obtain a hotspot propagation model based on dynamic evolution mechanism and improved heterogeneous mean field. To verify the proposed model, we perform simulations over synthetic networks and real network. Experiments show that the model effectively reveal the impact of different driving factors on information dissemination, and predict the trend of information dissemination in social networks.

Robustness of spatial networks and networks of networks

Much work has been devoted to studying percolation of networks and interdependent networks under varying levels of failures. Researchers have considered many different realistic network structures, but thus far no study has incorporated the hierarchical structure of many networks. For example, infrastructure across cities will likely be distributed such that nodes are tightly connected within small neighborhoods, somewhat less connected across the whole city, and have even fewer connections between cities. Furthermore, while previous work identified interconnected nodes, those nodes with links outside their neighborhood, to be more likely to be attacked, here we have various levels of interconnections (between neighborhoods, between cities, etc.). We consider the nodes with interconnections at the highest level most likely to be attacked, followed by those with interconnections at the next level, etc. We develop an analytic solution for both single and interdependent networks of this structure and verify our theory through simulations. We find that depending on the number of levels in the hierarchy there may be multiple transitions in the giant component (fraction of interconnected nodes), as the network separates at the various levels. Our results show that these multiple jumps are a feature of hierarchical networks and can affect the vulnerability of infrastructure networks.