scholarly journals Comprehensive influence of topological location and neighbor information on identifying influential nodes in complex networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251208
Author(s):  
Xiaohua Wang ◽  
Qing Yang ◽  
Meizhen Liu ◽  
Xiaojian Ma
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Hybrid Method ◽  
Spreading Process ◽  
Position Information ◽  
The Real ◽  
Contact Distance ◽  
Influential Nodes ◽  
Highly Correlated ◽  
Influential Individuals

Identifying the influential nodes of complex networks is now seen as essential for optimizing the network structure or efficiently disseminating information through networks. Most of the available methods determine the spreading capability of nodes based on their topological locations or the neighbor information, the degree of node is usually used to denote the neighbor information, and the k-shell is used to denote the locations of nodes, However, k-shell does not provide enough information about the topological connections and position information of the nodes. In this work, a new hybrid method is proposed to identify highly influential spreaders by not only considering the topological location of the node but also the neighbor information. The percentage of triangle structures is employed to measure both the connections among the neighbor nodes and the location of nodes, the contact distance is also taken into consideration to distinguish the interaction influence by different step neighbors. The comparison between our proposed method and some well-known centralities indicates that the proposed measure is more highly correlated with the real spreading process, Furthermore, another comprehensive experiment shows that the top nodes removed according to the proposed method are relatively quick to destroy the network than other compared semi-local measures. Our results may provide further insights into identifying influential individuals according to the structure of the networks.

Improved influential nodes identification in complex networks

2021 ◽  
pp. 1-9
Author(s):  
Shi Dong ◽  
Wengang Zhou
Keyword(s):  
Theoretical Analysis ◽  
Complex Networks ◽  
Network Structure ◽  
Information Entropy ◽  
Network System ◽  
Weighted Degree ◽  
Identification Methods ◽  
Hybrid Mechanism ◽  
Influential Nodes ◽  
Influential Node

Influential node identification plays an important role in optimizing network structure. Many measures and identification methods are proposed for this purpose. However, the current network system is more complex, the existing methods are difficult to deal with these networks. In this paper, several basic measures are introduced and discussed and we propose an improved influential nodes identification method that adopts the hybrid mechanism of information entropy and weighted degree of edge to improve the accuracy of identification (Hm-shell). Our proposed method is evaluated by comparing with nine algorithms in nine datasets. Theoretical analysis and experimental results on real datasets show that our method outperforms other methods on performance.

scholarly journals Identifying Influential Nodes of Complex Networks Based on Trust-Value

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 280
Author(s):  
Jinfang Sheng ◽  
Jiafu Zhu ◽  
Yayun Wang ◽  
Bin Wang ◽  
Zheng’ang Hou
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Real World ◽  
Trust Value ◽  
The Real ◽  
The World ◽  
Attribute Information ◽  
Similarity Ratio ◽  
Influential Nodes ◽  
Influential Node

The real world contains many kinds of complex network. Using influence nodes in complex networks can promote or inhibit the spread of information. Identifying influential nodes has become a hot topic around the world. Most of the existing algorithms used for influential node identification are based on the structure of the network such as the degree of the nodes. However, the attribute information of nodes also affects the ranking of nodes’ influence. In this paper, we consider both the attribute information between nodes and the structure of networks. Therefore, the similarity ratio, based on attribute information, and the degree ratio, based on structure derived from trust-value, are proposed. The trust–PageRank (TPR) algorithm is proposed to identify influential nodes in complex networks. Finally, several real networks from different fields are selected for experiments. Compared with some existing algorithms, the results suggest that TPR more rationally and effectively identifies the influential nodes in networks.

scholarly journals Identifying and Ranking Influential Nodes in Complex Networks Based on Dynamic Node Strength

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 82
Author(s):  
Xu Li ◽  
Qiming Sun
Keyword(s):  
Computational Complexity ◽  
Complex Networks ◽  
Dynamic Process ◽  
Local Structure ◽  
Experimental Results ◽  
Spreading Process ◽  
Ranking List ◽  
Low Computational Complexity ◽  
Influential Nodes ◽  
Type Decomposition

Identifying and ranking the node influence in complex networks is an important issue. It helps to understand the dynamics of spreading process for designing efficient strategies to hinder or accelerate information spreading. The idea of decomposing network to rank node influence is adopted widely because of low computational complexity. Of this type, decomposition is a dynamic process, and each iteration could be regarded as an inverse process of spreading. In this paper, we propose a new ranking method, Dynamic Node Strength Decomposition, based on decomposing network. The spreading paths are distinguished by weighting the edges according to the nodes at both ends. The change of local structure in the process of decomposition is considered. Our experimental results on four real networks with different sizes show that the proposed method can generate a more monotonic ranking list and identify node influence more effectively.

scholarly journals Hunting for Influential Nodes in Complex Networks Using Local Information

2021 ◽  
Author(s):  
Zhihao Dong ◽  
Yuanzhu Chen ◽  
Terrence S. Tricco ◽  
Cheng Li ◽  
Ting Hu
Keyword(s):  
Complex Networks ◽  
Real World ◽  
Structure And Function ◽  
Epidemic Threshold ◽  
Global Knowledge ◽  
The Real ◽  
Influential Nodes ◽  
Influential Node ◽  
And Function ◽  
High Degree

Abstract Complex networks in the real world are often with heterogeneous degree distributions. The structure and function of nodes can vary significantly, with influential nodes playing a crucial role in information spread and other spreading phenomena. Identifying high-degree nodes enables change to the network’s structure and function. Previous work either redefines metrics used to measure the nodes’ importance or focus on developing algorithms to efficiently find influential nodes. These approaches typically rely on global knowledge of the network and assume that the structure of the network does not change over time, both of which are difficult to achieve in the real world. In this paper, we propose a decentralized strategy that can find influential nodes without global knowledge of the network. Our Joint Nomination (JN) strategy selects a random set of nodes along with a set of nodes connected to those nodes, and together they nominate the influential node set. Experiments are conducted on 12 network datasets, including both synthetic and real-world networks, both undirected and directed networks. Results show that average degree of the identified node set is about 3–8 times higher than that of the full node set, and the degree distribution skews toward higher-degree nodes. Removal of influential nodes increase the average shortest path length by 20–70% over the original network, or about 8–15% longer than the other decentralized strategies. Immunization based on JN is more efficient than other strategies, consuming around 12–40% less immunization resources to raise the epidemic threshold to 𝜏 ~ 0:1. Susceptible-Infected-Recovered (SIR) simulations on networks with 30% influential nodes removed using JN delays the arrival time of infection peak significantly and reduce the total infection scale to 15%.

A novel measure for influence nodes across complex networks based on node attraction

2020 ◽  
pp. 2150012
Author(s):  
Bin Wang ◽  
Wanghao Guan ◽  
Yuxuan Sheng ◽  
Jinfang Sheng ◽  
Jinying Dai ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Real World ◽  
Time Complexity ◽  
New Method ◽  
Complex Structures ◽  
The Real ◽  
Complex Problems ◽  
Influential Nodes

The real-world network is heterogeneous, and it is an important and challenging task to effectively identify the influential nodes in complex networks. Identification of influential nodes is widely used in social, biological, transportation, information and other networks with complex structures to help us solve a variety of complex problems. In recent years, the identification of influence nodes has received a lot of attention, and scholars have proposed various methods based on different practical problems. This paper proposes a new method to identify influential nodes, namely Attraction based on Node and Community (ANC). By considering the attraction of nodes to nodes and nodes to community structure, this method quantifies the attraction of a node, and the attraction of a node is used to represent its influence. To illustrate the effectiveness of ANC, we did extensive experiments on six real-world networks and the results show that the ANC algorithm is superior to the representative algorithms in terms of the accuracy and has lower time complexity as well.

scholarly journals Centralities for networks with consumable resources

2019 ◽  
Vol 7 (3) ◽  
pp. 376-401
Author(s):  
Hayato Ushijima-Mwesigwa ◽  
Zadid Khan ◽  
Mashrur A. Chowdhury ◽  
Ilya Safro
Keyword(s):  
Random Walk ◽  
Real World ◽  
Shortest Paths ◽  
Information Loss ◽  
Centrality Measures ◽  
Spreading Process ◽  
Social Actors ◽  
Simulation Based ◽  
Influential Nodes ◽  
Highly Correlated

AbstractIdentification of influential nodes is an important step in understanding and controlling the dynamics of information, traffic, and spreading processes in networks. As a result, a number of centrality measures have been proposed and used across different application domains. At the heart of many of these measures lies an assumption describing the manner in which traffic (of information, social actors, particles, etc.) flows through the network. For example, some measures only count shortest paths while others consider random walks. This paper considers a spreading process in which a resource necessary for transit is partially consumed along the way while being refilled at special nodes on the network. Examples include fuel consumption of vehicles together with refueling stations, information loss during dissemination with error-correcting nodes, and consumption of ammunition of military troops while moving. We propose generalizations of the well-known measures of betweenness, random-walk betweenness, and Katz centralities to take such a spreading process with consumable resources into account. In order to validate the results, experiments on real-world networks are carried out by developing simulations based on well-known models such as Susceptible-Infected-Recovered and congestion with respect to particle hopping from vehicular flow theory. The simulation-based models are shown to be highly correlated with the proposed centrality measures.Reproducibility: Our code and experiments are available at https://github.com/hmwesigwa/soc_centrality

scholarly journals MODELING SARS SPREADING ON COMPLEX NETWORKS

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2734-2739 ◽  
Author(s):  
HUIJIE YANG ◽  
FANGCUI ZHAO ◽  
ZHONGNAN LI ◽  
WEI ZHANG ◽  
YUN ZHOU
Keyword(s):  
Complex Networks ◽  
Network Structure ◽  
Contact Network ◽  
Spreading Process ◽  
Initial Network ◽  
New Phase

The spreading of SARS will destruct the initial network structure to a new phase, and in turn the spreading process will be weakened effectively and finally halted by this evolution of network structure. This mechanism is called immunity of contact network in this paper. What we can do is to accelerate effectively this process only.

Experimental Studies of Sedimentation Basin Dynamics

1977 ◽  
Vol 12 (1) ◽  
pp. 77-90
Author(s):  
J.F. Cordoba-Molina ◽  
P.L. Silveston ◽  
R. R. Hudgins
Keyword(s):  
Dynamic Response ◽  
Froude Number ◽  
Flow Model ◽  
Experimental Studies ◽  
Densimetric Froude Number ◽  
Number Range ◽  
The Real ◽  
Highly Correlated ◽  
Sedimentation Basins ◽  
Simple Flow

Abstract A simple Flow Model is proposed to describe the dynamic response of sedimentation basins. The response predicted by this model is linear as opposed to the real response of the basin which is nonlinear. However, the real response of the basin is highly correlated with its densimetric Froude number, and as a consequence our linear model effectively predicts the response of the basin in a restricted densimetric Froude Number range. Our experiments show that the response of the basin becomes more sluggish and erratic as the densimetric Froude number decreases.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

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