Identifying Influential Spreaders in Complex Networks by Considering the Impact of the Number of Shortest Paths

2021 ◽  
Author(s):  
Yangyang Luan ◽  
Zhongkui Bao ◽  
Haifeng Zhang
Keyword(s):  
Complex Networks ◽  
Shortest Paths ◽  
The Impact

scholarly journals Identifying Node Importance in a Complex Network Based on Node Bridging Feature

2018 ◽  
Vol 8 (10) ◽  
pp. 1914 ◽  
Author(s):  
Lincheng Jiang ◽  
Yumei Jing ◽  
Shengze Hu ◽  
Bin Ge ◽  
Weidong Xiao
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Shortest Paths ◽  
Evaluation Criteria ◽  
Local Network ◽  
Network Efficiency ◽  
Network Attack ◽  
Node Importance ◽  
Pass Through ◽  
Large Scale Networks

Identifying node importance in complex networks is of great significance to improve the network damage resistance and robustness. In the era of big data, the size of the network is huge and the network structure tends to change dynamically over time. Due to the high complexity, the algorithm based on the global information of the network is not suitable for the analysis of large-scale networks. Taking into account the bridging feature of nodes in the local network, this paper proposes a simple and efficient ranking algorithm to identify node importance in complex networks. In the algorithm, if there are more numbers of node pairs whose shortest paths pass through the target node and there are less numbers of shortest paths in its neighborhood, the bridging function of the node between its neighborhood nodes is more obvious, and its ranking score is also higher. The algorithm takes only local information of the target nodes, thereby greatly improving the efficiency of the algorithm. Experiments performed on real and synthetic networks show that the proposed algorithm is more effective than benchmark algorithms on the evaluation criteria of the maximum connectivity coefficient and the decline rate of network efficiency, no matter in the static or dynamic attack manner. Especially in the initial stage of attack, the advantage is more obvious, which makes the proposed algorithm applicable in the background of limited network attack cost.

scholarly journals Visualizing real-world networks

2021 ◽  
Author(s):  
Lyndsay Roach
Keyword(s):  
Complex Networks ◽  
Real World ◽  
The Internet ◽  
Network Data ◽  
Community Structures ◽  
Large Networks ◽  
Computing Power ◽  
On Line ◽  
Using Data ◽  
The Impact

The study of networks has been propelled by improvements in computing power, enabling our ability to mine and store large amounts of network data. Moreover, the ubiquity of the internet has afforded us access to records of interactions that have previously been invisible. We are now able to study complex networks with anywhere from hundreds to billions of nodes; however, it is difficult to visualize large networks in a meaningful way. We explore the process of visualizing real-world networks. We first discuss the properties of complex networks and the mechanisms used in the network visualizing software Gephi. Then we provide examples of voting, trade, and linguistic networks using data extracted from on-line sources. We investigate the impact of hidden community structures on the analysis of these real-world networks.

Multi-scenario simulation on the impact of China's electricity bidding policy based on complex networks model

Energy Policy ◽  
2021 ◽  
Vol 158 ◽  
pp. 112573
Author(s):  
Di Wang ◽  
Zhiyuan Zhang ◽  
Xiaodi Yang ◽  
Yanfang Zhang ◽  
Yuman Li ◽  
...  
Keyword(s):  
Complex Networks ◽  
Scenario Simulation ◽  
The Impact

scholarly journals The Impact of Countries’ Roles on the International Photovoltaic Trade Pattern: The Complex Networks Analysis

2016 ◽  
Vol 8 (4) ◽  
pp. 313 ◽  
Author(s):  
Qing Guan ◽  
Haizhong An ◽  
Xiaoqing Hao ◽  
Xiaoliang Jia
Keyword(s):  
Complex Networks ◽  
Trade Pattern ◽  
Networks Analysis ◽  
The Impact

scholarly journals Finite-Time Synchronization for a Class of Multiweighted Complex Networks with Markovian Switching and Time-Varying Delay

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-25
Author(s):  
Ying Liu ◽  
Fei Chen ◽  
Bin Yang ◽  
Xin Wang ◽  
Weiming Wang
Keyword(s):  
Complex Networks ◽  
Finite Time ◽  
Time Synchronization ◽  
Time Estimation ◽  
Markovian Switching ◽  
Convergence Time ◽  
Time Varying ◽  
Time Varying Delay ◽  
The Impact ◽  
Varying Delay

In this paper, we investigate the finite-time synchronization control for a class of nonlinear coupled multiweighted complex networks (NCMWCNs) with Markovian switching and time-varying delay analytically and quantitatively. The value of this study lies in four aspects: First, it designs the finite-time synchronization controller to make the NCMWCNs with Markovian switching and time-varying delay achieve global synchronization in finite time. Second, it derives two kinds of finite-time estimation approaches by analyzing the impact of the nonlinearity of nonlinear coupled function on synchronization dynamics and synchronization convergence time. Third, it presents the relationship between Markovian switching parameters and synchronization problems of subsystems and the overall system. Fourth, it provides some numerical examples to demonstrate the effectiveness of the theoretical results.

Resilience of Urban Street Network Configurations under Low Demands

2020 ◽  
Vol 2674 (9) ◽  
pp. 982-994
Author(s):  
Zhengyao Yu ◽  
Vikash V. Gayah
Keyword(s):  
Routing Algorithm ◽  
Shortest Paths ◽  
Left Turn ◽  
Urban Street ◽  
Traffic Conditions ◽  
Street Networks ◽  
Shortest Distance ◽  
Left Turns ◽  
The Impact ◽  
Travel Distances

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.

scholarly journals Distance closures on complex networks

2015 ◽  
Vol 3 (2) ◽  
pp. 227-268 ◽  
Author(s):  
TIAGO SIMAS ◽  
LUIS M. ROCHA
Keyword(s):  
Complex Networks ◽  
Diffusion Processes ◽  
Shortest Paths ◽  
Approximate Reasoning ◽  
Weighted Graphs ◽  
Dijkstra Algorithm ◽  
Simple Method ◽  
Distance Graphs ◽  
Network Analyses ◽  
Metric Distances

AbstractTo expand the toolbox available to network science, we study the isomorphism between distance and Fuzzy (proximity or strength) graphs. Distinct transitive closures in Fuzzy graphs lead to closures of their isomorphic distance graphs with widely different structural properties. For instance, the All Pairs Shortest Paths (APSP) problem, based on the Dijkstra algorithm, is equivalent to a metric closure, which is only one of the possible ways to calculate shortest paths in weighted graphs. We show that different closures lead to different distortions of the original topology of weighted graphs. Therefore, complex network analyses that depend on the calculation of shortest paths on weighted graphs should take into account the closure choice and associated topological distortion. We characterize the isomorphism using the max-min and Dombi disjunction/conjunction pairs. This allows us to: (1) study alternative distance closures, such as those based on diffusion, metric, and ultra-metric distances; (2) identify the operators closest to the metric closure of distance graphs (the APSP), but which are logically consistent; and (3) propose a simple method to compute alternative path length measures and corresponding distance closures using existing algorithms for the APSP. In particular, we show that a specific diffusion distance is promising for community detection in complex networks, and is based on desirable axioms for logical inference or approximate reasoning on networks; it also provides a simple algebraic means to compute diffusion processes on networks. Based on these results, we argue that choosing different distance closures can lead to different conclusions about indirect associations on network data, as well as the structure of complex networks, and are thus important to consider.

From paths to blocks: New measures for street patterns

2016 ◽  
Vol 44 (2) ◽  
pp. 256-271 ◽  
Author(s):  
Marc Barthelemy
Keyword(s):  
Betweenness Centrality ◽  
Spatial Organization ◽  
Shortest Paths ◽  
Urban Systems ◽  
Conditional Probability Distribution ◽  
Planar Network ◽  
Formation And Evolution ◽  
Straight Lines ◽  
The Impact ◽  
Planar Networks

The street network is an important aspect of cities and contains crucial information about their organization and evolution. Characterizing and comparing various street networks could then be helpful for a better understanding of the mechanisms governing the formation and evolution of these systems. Their characterization is however not easy: there are no simple tools to classify planar networks and most of the measures developed for complex networks are not useful when space is relevant. Here, we describe recent efforts in this direction and new methods adapted to spatial networks. We will first discuss measures based on the structure of shortest paths, among which the betweenness centrality. In particular for time-evolving road networks, we will show that the spatial distribution of the betweenness centrality is able to reveal the impact of important structural transformations. Shortest paths are however not the only relevant ones. In particular, they can be very different from those with the smallest number of turns—the simplest paths. The statistical comparison of the lengths of the shortest and simplest paths provides a nontrivial and nonlocal information about the spatial organization of planar graphs. We define the simplicity index as the average ratio of these lengths and the simplicity profile characterizes the simplicity at different scales. Measuring these quantities on artificial (roads, highways, railways) and natural networks (leaves, insect wings) show that there are fundamental differences—probably related to their different function—in the organization of urban and biological systems: there is a clear hierarchy of the lengths of straight lines in biological cases, but they are randomly distributed in urban systems. The paths are however not enough to fully characterize the spatial pattern of planar networks such as streets and roads. Another promising direction is to analyze the statistics of blocks of the planar network. More precisely, we can use the conditional probability distribution of the shape factor of blocks with a given area, and define what could constitute the fingerprint of a city. These fingerprints can then serve as a basis for a classification of cities based on their street patterns. This method applied on more than 130 cities in the world leads to four broad families of cities characterized by different abundances of blocks of a certain area and shape. This classification will be helpful for identifying dominant mechanisms governing the formation and evolution of street patterns.

scholarly journals Inferring propagation paths for sparsely observed perturbations on complex networks

2016 ◽  
Vol 2 (10) ◽  
pp. e1501638 ◽  
Author(s):  
Francesco Alessandro Massucci ◽  
Jonathan Wheeler ◽  
Raúl Beltrán-Debón ◽  
Jorge Joven ◽  
Marta Sales-Pardo ◽  
...  
Keyword(s):  
Social Sciences ◽  
Complex System ◽  
Complex Networks ◽  
Probabilistic Model ◽  
Belief Propagation ◽  
Shortest Paths ◽  
Single Observation ◽  
Perturbed System ◽  
The Social ◽  
Propagation Paths

In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed.

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