scholarly journals Best influential spreaders identification using network global structural properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Amrita Namtirtha ◽  
Animesh Dutta ◽  
Biswanath Dutta ◽  
Amritha Sundararajan ◽  
Yogesh Simmhan
Keyword(s):  
Structural Properties ◽  
Network Connectivity ◽  
Ground Truth ◽  
Sir Model ◽  
Information Propagation ◽  
Virus Propagation ◽  
Spreading Process ◽  
Indexing Methods ◽  
Indexing Method ◽  
Influential Nodes

AbstractInfluential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality (ngsc) which intelligently combines existing kshell and sum of neighbors’ degree methods with knowledge of the network’s global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.

scholarly journals Identifying Influential Nodes in Complex Networks Based on Node Itself and Neighbor Layer Information

Symmetry ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1570 ◽  
Author(s):  
Jingcheng Zhu ◽  
Lunwen Wang
Keyword(s):  
Complex Networks ◽  
Sir Model ◽  
Identification Accuracy ◽  
Degree Centrality ◽  
Network Stability ◽  
Propagation Process ◽  
Calculation Results ◽  
Influential Nodes ◽  
Nearest Neighborhood ◽  
Accuracy Degree

Identifying influential nodes in complex networks is of great significance for clearly understanding network structure and maintaining network stability. Researchers have proposed many classical methods to evaluate the propagation impact of nodes, but there is still some room for improvement in the identification accuracy. Degree centrality is widely used because of its simplicity and convenience, but it has certain limitations. We divide the nodes into neighbor layers according to the distance between the surrounding nodes and the measured node. Considering that the node’s neighbor layer information directly affects the identification result, we propose a new node influence identification method by combining degree centrality information about itself and neighbor layer nodes. This method first superimposes the degree centrality of the node itself with neighbor layer nodes to quantify the effect of neighbor nodes, and then takes the nearest neighborhood several times to characterize node influence. In order to evaluate the efficiency of the proposed method, the susceptible–infected–recovered (SIR) model was used to simulate the propagation process of nodes on multiple real networks. These networks are unweighted and undirected networks, and the adjacency matrix of these networks is symmetric. Comparing the calculation results of each method with the results obtained by SIR model, the experimental results show that the proposed method is more effective in determining the node influence than seven other identification methods.

Combination methods for identifying influential nodes in networks

2015 ◽  
Vol 26 (06) ◽  
pp. 1550067 ◽  
Author(s):  
Chao Gao ◽  
Lu Zhong ◽  
Xianghua Li ◽  
Zili Zhang ◽  
Ning Shi
Keyword(s):  
Recommendation System ◽  
Ranking Methods ◽  
Virus Propagation ◽  
Propagation Models ◽  
Scale Free ◽  
Combination Methods ◽  
Scale Free Networks ◽  
Theoretical Significance ◽  
Different Types ◽  
Influential Nodes

Identifying influential nodes is of theoretical significance in many domains. Although lots of methods have been proposed to solve this problem, their evaluations are under single-source attack in scale-free networks. Meanwhile, some researches have speculated that the combinations of some methods may achieve more optimal results. In order to evaluate this speculation and design a universal strategy suitable for different types of networks under the consideration of multi-source attacks, this paper proposes an attribute fusion method with two independent strategies to reveal the correlation of existing ranking methods and indicators. One is based on feature union (FU) and the other is based on feature ranking (FR). Two different propagation models in the fields of recommendation system and network immunization are used to simulate the efficiency of our proposed method. Experimental results show that our method can enlarge information spreading and restrain virus propagation in the application of recommendation system and network immunization in different types of networks under the condition of multi-source attacks.

Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

2018 ◽  
Vol 32 (06) ◽  
pp. 1850118 ◽  
Author(s):  
Mengtian Li ◽  
Ruisheng Zhang ◽  
Rongjing Hu ◽  
Fan Yang ◽  
Yabing Yao ◽  
...  
Keyword(s):  
Complex Networks ◽  
Nearest Neighbors ◽  
Sir Model ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Competitive Performance ◽  
Spreading Process ◽  
Degree Sum ◽  
Local Degree ◽  
Crucial Problem

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.

Comparing information diffusion mechanisms by matching on cascade size

2021 ◽  
Vol 118 (46) ◽  
pp. e2100786118
Author(s):  
Jonas L. Juul ◽  
Johan Ugander
Keyword(s):  
Structural Properties ◽  
Information Diffusion ◽  
Spreading Process ◽  
Information Spread ◽  
Structural Differences ◽  
Classical Models ◽  
Diffusion Mechanisms ◽  
Speed Of Propagation ◽  
Types Of Information ◽  
False News

Do some types of information spread faster, broader, or further than others? To understand how information diffusions differ, scholars compare structural properties of the paths taken by content as it spreads through a network, studying so-called cascades. Commonly studied cascade properties include the reach, depth, breadth, and speed of propagation. Drawing conclusions from statistical differences in these properties can be challenging, as many properties are dependent. In this work, we demonstrate the essentiality of controlling for cascade sizes when studying structural differences between collections of cascades. We first revisit two datasets from notable recent studies of online diffusion that reported content-specific differences in cascade topology: an exhaustive corpus of Twitter cascades for verified true- or false-news content by Vosoughi et al. [S. Vosoughi, D. Roy, S. Aral. Science 359, 1146–1151 (2018)] and a comparison of Twitter cascades of videos, pictures, news, and petitions by Goel et al. [S. Goel, A. Anderson, J. Hofman, D. J. Watts. Manage. Sci. 62, 180–196 (2016)]. Using methods that control for joint cascade statistics, we find that for false- and true-news cascades, the reported structural differences can almost entirely be explained by false-news cascades being larger. For videos, images, news, and petitions, structural differences persist when controlling for size. Studying classical models of diffusion, we then give conditions under which differences in structural properties under different models do or do not reduce to differences in size. Our findings are consistent with the mechanisms underlying true- and false-news diffusion being quite similar, differing primarily in the basic infectiousness of their spreading process.

scholarly journals The Urgent Need for River Health Biomonitoring Tools for Large Tropical Rivers in Developing Countries: Preliminary Development of a River Health Monitoring Tool for Myanmar Rivers

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1408 ◽  
Author(s):  
Nyein Thandar Ko ◽  
Phil Suter ◽  
John Conallin ◽  
Martine Rutten ◽  
Thom Bogaard
Keyword(s):  
Developing Countries ◽  
Asian Countries ◽  
Anthropogenic Pressures ◽  
River Health ◽  
Index Method ◽  
Indexing Methods ◽  
Indexing Method ◽  
Additional Testing ◽  
Best Fit ◽  
Country Specific

Anthropogenic pressures such as river infrastructure, agriculture and power generation are rapidly increasing in Southeast Asia, aimed at providing food security within the region. However, this will lead to unintended river health consequences, and, currently, most Southeast Asian countries have no country-specific tools for monitoring river health. In Myanmar, one of Southeast Asia’s poorest and most rapidly developing countries, no country-specific tools exist, and there is an urgent need to provide tools that can inform better management and trade-off decision making. This research evaluated three rapid macroinvertebrate bioassessment methods under Myanmar conditions. The objective of the research was to assess the applicability of existing internationally accepted indexing methods for use in Myanmar. Through taxa identification in the laboratory and statistical analysis, it was concluded that the method with the best fit for Myanmar taxa is The Asia Foundation index method, although differences were small. This Asia Foundation method is comparable to the Australian Waterwatch method but includes a family present in our samples that is not included in the Waterwatch method. We then modified this method to include Myanmar taxa not recorded in The Asia Foundation method. The modified index method could be further developed into a Myanmar specific tool for widespread use potentially in combination with the also tested miniSASS, a much easier order-based method better suitable for non-professionals. We recommend additional testing using sites on other rivers across the country to establish a professional indexing method for Myanmar.

scholarly journals The Local Triangle Structure Centrality Method to Rank Nodes in Networks

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaojian Ma ◽  
Yinghong Ma
Keyword(s):  
Real World ◽  
Local Structure ◽  
Sir Model ◽  
Evaluation Methods ◽  
Ranking Method ◽  
Information Propagation ◽  
Practical Application ◽  
H Index ◽  
Simulation Results ◽  
Important Nodes

Detecting influential spreaders had become a challenging and crucial topic so far due to its practical application in many areas, such as information propagation inhibition and disease dissemination control. Some traditional local based evaluation methods had given many discussions on ranking important nodes. In this paper, ranking nodes of networks continues to be discussed. A semilocal structures method for ranking nodes based on the degree and the neighbors’ connections of the node is presented. The semilocal structures are regarded as the number of neighbors of the nodes and the connections between the node and its neighbors. We combined the triangle structure and the degree information of the neighbors to define the inner-outer spreading ability of the nodes and then summed the node neighbors’ inner-outer spreading ability to be used as the local triangle structure centrality (LTSC). The LTSC avoids the defect “pseudo denser connections” in measuring the structure of neighbors. The performance of the proposed LTSC method is evaluated by comparing the spreading ability on both real-world and synthetic networks with the SIR model. The simulation results of the discriminability and the correctness compared with pairs of ranks (one is generated by SIR model and the others are generated by central nodes measures) show that LTSC outperforms some other local or semilocal methods in evaluating the node’s influence in most cases, such as degree, betweenness, H-index, local centrality, local structure centrality, K-shell, and S-shell. The experiments prove that the LTSC is an efficient and accurate ranking method which provides a more reasonable evaluating index to rank nodes than some previous approaches.

Community centrality for node's influential ranking in complex network

2014 ◽  
Vol 25 (03) ◽  
pp. 1350096 ◽  
Author(s):  
Biao Cai ◽  
Xian-Guo Tuo ◽  
Kai-Xue Yang ◽  
Ming-Zhe Liu
Keyword(s):  
Complex Network ◽  
Spanning Tree ◽  
Time Complexity ◽  
Local Community ◽  
Sir Model ◽  
Minimal Spanning Tree ◽  
Path Computation ◽  
Influential Nodes ◽  
Shortest Path Computation ◽  
Very High

Some tiny party of influential nodes may highly affect spread of information in complex networks. For the case of very high time complexity in the shortest path computation of global centralities, making use of local community centrality to identify influential nodes is an open and possible problem. Compared to degree and local centralities, a five-heartbeat forward community centrality is proposed in this paper, in which a five-step induced sub-graph of certain node in the network will be achieved. Next, we induce the minimal spanning tree (MMT) of the sub-graph. Finally, we take the sum of all weights of the MMT as community centrality measurement that needs to be the influential ranking of the node. We use the susceptible, infected and recovered (SIR) model to evaluate the performance of this method on several public test network data and explore the forward steps of community centrality by experiments. Simulative results show that our method with five steps can identify the influential ranking of nodes in complex network as well.

Measures of node centrality in mobile social networks

2015 ◽  
Vol 26 (09) ◽  
pp. 1550107 ◽  
Author(s):  
Zhenxiang Gao ◽  
Yan Shi ◽  
Shanzhi Chen
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Human Mobility ◽  
Graph Model ◽  
Information Propagation ◽  
Mobile Social Networks ◽  
Mobility Patterns ◽  
Centrality Metrics ◽  
Influential Nodes ◽  
Over Time

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

scholarly journals Systematic biases in disease forecasting - the role of behavior change

2018 ◽  
Author(s):  
Ceyhun Eksin ◽  
Keith Paarporn ◽  
Joshua S. Weitz
Keyword(s):  
Behavior Change ◽  
Ground Truth ◽  
Sir Model ◽  
Disease Spread ◽  
Forecast Errors ◽  
Cumulative Number ◽  
Initial Speed ◽  
Sir Epidemic Model ◽  
Final Size ◽  
Forecast Models

ABSTRACTIn a simple susceptible-infected-recovered (SIR) model, the initial speed at which infected cases increase is indicative of the long-term trajectory of the outbreak. Yet during real-world outbreaks, individuals may modify their behavior and take preventative steps to reduce infection risk. As a consequence, the relationship between the initial rate of spread and the final case count may become tenuous. Here, we evaluate this hypothesis by comparing the dynamics arising from a simple SIR epidemic model with those from a modified SIR model in which individuals reduce contacts as a function of the current or cumulative number of cases. Dynamics with behavior change exhibit significantly reduced final case counts even though the initial speed of disease spread is nearly identical for both of the models. We show that this difference in final size projections depends critically in the behavior change of individuals. These results also provide a rationale for integrating behavior change into iterative forecast models. Hence, we propose to use a Kalman filter to update models with and without behavior change as part of iterative forecasts. When the ground truth outbreak includes behavior change, sequential predictions using a simple SIR model perform poorly despite repeated observations while predictions using the modified SIR model are able to correct for initial forecast errors. These findings highlight the value of incorporating behavior change into baseline epidemic and dynamic forecast models.

scholarly journals Adaptive SIR model for propagation of SARS-CoV-2 in Brazil

2020 ◽  
Author(s):  
I. F. F. dos Santos ◽  
G. M. A. Almeida ◽  
F. A. B. F. de Moura
Keyword(s):  
Sir Model ◽  
Virus Propagation ◽  
Epidemic Dynamics ◽  
Time Required ◽  
Near Future ◽  
Official Data ◽  
Over Time

Abstract We study the spreading of SARS-CoV-2 in Brazil based on official data available since March 22, 2020. The calculations are based on an adaptive susceptible-infected-removed (SIR) model featuring dynamical recuperation and propagation rates and can reproduce the number of confirmed cases over time with less than $5\%$ error. Furthermore, it is possible to predict the epidemic progression in the near future and we go on to estimate the time required to stabilize the virus propagation. The model is also proved to be accurate for analyzing the epidemic dynamics in other countries.

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