Spreaders In Complex Networks Research Articles

A novel ranking approach for identifying crucial spreaders in complex networks based on Tanimoto Correlation

The identification and rank of crucial spreaders aim to survey the diffusion capability, which has a significant impact on controlling information spread in networks. However, most of the findings neglect the fused information of the structural hole and neighbors within two orders. In addition, they fail to focus on the effect brought by the association strength between a pair of different neighborhood sets. To solve these issues, in this work, we design a novel semi-local ranking algorithm for achieving better recognition effects, referred to as the Extended Tanimoto Correlation (ETC). We first construct a topological structure of neighborhood sets Cluster, and define HierarchyVectorofCluster based on a novel layered approach IKsD. Considering the structural hole, Tanimoto Correlation (TC) is presented to contribute to measuring the association strength between different clusters. Additionally, we explicitly extend the Tanimoto Correlation metric on semi-local information to calculate each spreader’s influence score. Simulation experiments employ capability indicators and correctness metrics to apply in various networks. Results demonstrate that our method outperforms state-of-the-art algorithms in identifying and ranking effects for good spreading influence.

Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ([Formula: see text]) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

Identifying vital spreaders in complex networks based on the interpretative structure model and improved Kshell

Identifying vital spreaders in complex networks based on the interpretative structure model and improved Kshell

A hybrid recognition framework of crucial seed spreaders in complex networks with neighborhood overlap

A hybrid recognition framework of crucial seed spreaders in complex networks with neighborhood overlap

Identifying influential spreaders in complex networks based on density entropy and community structure

In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible–infected–recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

A study on centrality measures in weighted networks: A case of the aviation network

<abstract><p>Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. An aviation network is a typical complex network, and accurately identifying the key city nodes in the aviation network can help us better prevent network attacks and control the spread of diseases. In this paper, a method for identifying key nodes in undirected weighted networks, called weighted Laplacian energy centrality, was proposed and applied to an aviation network constructed from real flight data. Based on the analysis of the topological structure of the network, the paper recognized critical cities in this network, then simulation experiments were conducted on key city nodes from the perspectives of network dynamics and robustness. The results indicated that, compared with other methods, weighted Laplacian energy centrality can identify the city nodes with the most spreading influence in the network. From the perspective of network robustness, the identified key nodes also have the characteristics of accurately and quickly destroying network robustness.</p></abstract>

Identifying key spreaders in complex networks based on local clustering coefficient and structural hole information

Identifying key spreaders in a network is one of the fundamental problems in the field of complex network research, and accurately identifying influential propagators in a network holds significant practical implications. In recent years, numerous effective methods have been proposed and widely applied. However, many of these methods still have certain limitations. For instance, some methods rely solely on the global position information of nodes to assess their propagation influence, disregarding local node information. Additionally, certain methods do not consider clustering coefficients, which are essential attributes of nodes. Inspired by the quality formula, this paper introduces a method called Structural Neighborhood Centrality (SNC) that takes into account the neighborhood information of nodes. SNC measures the propagation power of nodes based on first and second-order neighborhood degrees, local clustering coefficients, structural hole constraints, and other information, resulting in higher accuracy. A series of pertinent experiments conducted on 12 real-world datasets demonstrate that, in terms of accuracy, SNC outperforms methods like CycleRatio and KSGC. Additionally, SNC demonstrates heightened monotonicity, enabling it to distinguish subtle differences between nodes. Furthermore, when it comes to identifying the most influential Top-k nodes, SNC also displays superior capabilities compared to the aforementioned methods. Finally, we conduct a detailed analysis of SNC and discuss its advantages and limitations.

Identifying and Ranking Influential Spreaders in Complex Networks by Localized Decreasing Gravity Model

Abstract Identifying crucial nodes in complex networks is paid more attention in recent years. Some classical methods, such as degree centrality, betweenness centrality and closeness centrality, have their advantages and disadvantages. Recently, the gravity model is applied to describe the relationship of nodes in a complex network. However, the interaction force in gravity model follows the square law of distance, which is inconsistent with the actual situation. Most people are generally affected by those who are surrounding them, which means that local influence should be emphasized. To address this issue, we propose an indexing method called localized decreasing gravity centrality by maximizing the local influence of a node. In the proposed measure, the mass and radius of gravity model are redefined, which can represent the spreading ability of the node. In addition, a decreasing weight is added to strengthen the local influence of a node. To evaluate the performance of the proposed method, we utilize four different types of networks, including interaction networks, economic networks, collaboration networks and animal social networks. Also, two different infectious disease models, susceptible-infectious-recovered (SIR) and susceptible-exposed-low risk-high risk-recovered (SELHR), are utilized to examine the spreading ability of influential nodes.

Identifying influential nodes in complex networks using a gravity model based on the H-index method

Identifying influential spreaders in complex networks is a widely discussed topic in the field of network science. Numerous methods have been proposed to rank key nodes in the network, and while gravity-based models often perform well, most existing gravity-based methods either rely on node degree, k-shell values, or a combination of both to differentiate node importance without considering the overall impact of neighboring nodes. Relying solely on a node's individual characteristics to identify influential spreaders has proven to be insufficient. To address this issue, we propose a new gravity centrality method called HVGC, based on the H-index. Our approach considers the impact of neighboring nodes, path information between nodes, and the positional information of nodes within the network. Additionally, it is better able to identify nodes with smaller k-shell values that act as bridges between different parts of the network, making it a more reasonable measure compared to previous gravity centrality methods. We conducted several experiments on 10 real networks and observed that our method outperformed previously proposed methods in evaluating the importance of nodes in complex networks.

Identifying influential spreaders in complex networks based on limited spreading domain

In recent years, the problem of influential spreader identification in complex networks has attracted extensive attention as its fundamental role in social network analysis, rumor controlling, viral marketing and other related fields. Centrality measures that consider different scales of neighborhood are commonly utilized for ranking node influence. The 2-hop neighborhood of the target node is deemed a suitable evaluation metric. However, as the network scale expands, only considering 2-hop neighborhood is overly restrictive. Furthermore, the interconnections among nodes are often disregarded. In this article, a new method named Limited Spreading Domain (LSD) is proposed to identify influential spreaders. LSD defines the target node’s 2-hop neighborhood as basic domain and takes the neighbors who are 3–6 hops away from target node as extended domain. The influence of target node is modeled as diffusion along the paths with limited length in basic domain and extended domain. A series of experiments are conducted in eight real complex networks and results demonstrate that LSD outperforms common centralities in terms of accuracy, stability,distinguishability and scalability.

Edge-weights-based method to identify influential spreaders in complex networks

Identifying influential nodes has drawn great attention in recent years. In this paper, a novel method for identifying influential spreaders based on potential edge weights (WDK for simplicity) for both undirected and unweighted networks is proposed. Degree and k-shell of a node and its neighbors are considered simultaneously, which are regarded as the weight of the edge directly connected to the node. The algorithm considers not only the local information of nodes but also their location information. The proposed method not only improves the accuracy of node mining but also has approximately linear time complexity, which indicates that the proposed method is suitable for large-scale networks. In order to validate the effectiveness of the proposed method, different evaluation indexes are introduced in nine real networks. Compared with five classical key nodes identification methods, the experimental results show that the proposed method performs optimally in all networks.

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks.

Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.

A New Method for Identifying Influential Spreaders in Complex Networks

Abstract Social networks have an important role in the distribution of ideas. With the rapid development of the social networks, identifying the influential nodes provides a chance to turn the new potential of global information spread into reality. The measurement of the spreading capabilities of nodes is an attractive challenge in social networks analysis. In this paper, a novel method is proposed to identify the influential nodes in complex networks. The proposed method determines the spreading capability of a node based on its local and global positions. The degree centrality is improved by the Shannon entropy to measure the local influence of nodes. The k-shell method is improved by the clustering coefficient to measure the global influence of nodes. To rank the importance of nodes, the entropy weighting method is used to calculate the weight for the local and global influences. The Vlsekriterijumska Optimizacija I Kompromisno Resenje method is used to integrate the local and global influences of a node and obtain its importance. The experiments are conducted on 13 real-world networks to evaluate the performance of the proposed method. The experimental results show that the proposed method is more powerful and accurate to identify influential nodes than other methods.

Key nodes mining for complex networks based on local gravity model

Identification of key nodes in complex networks can effectively speed up the spread of favourable information or prevent the spread of rumours and diseases. An algorithm based on local gravity model is proposed to mine the key spreaders in complex networks. The existing algorithms based on gravity model consider the shortest distance between nodes. However, nodes not only influence each other through the shortest path, which will lead the loss of information between nodes. Different from the existing gravity model, the distance considered in this model is not the shortest distance between nodes, but the reciprocal of the number of feasible paths between nodes. Conveniently, the model is called FPLGM (Local Gravity Model Based on Feasible Paths). Ten different networks are utilised to verify the effectiveness of FPLGM. Results show that the FPLGM performs best in comparison with the well-known state-of-the-art methods.

Influential Spreaders Identification in Complex Networks With TOPSIS and K-Shell Decomposition

In view that the K-shell decomposition method can only effectively identify a single most influential node, but cannot accurately identify a group of most influential nodes, this article proposes a hybrid method based on K-shell decomposition to identify the most influential spreaders in complex networks. First, the K-shell decomposition method is used to decompose the network, and the network is regarded as a hierarchical structure from the inner core to the periphery core. Second, the existing centrality methods such as H-index are used as the secondary score of the proposed method to select nodes in each hierarchy of the network. In addition, for the sake of alleviating the overlapping problem, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced to calculate the comprehensive score of secondary score and overlapping range, and the node with the highest comprehensive score will be selected in each round. The proposed algorithm can be used as a general framework to improve the existing centrality method which can represent nodes with definite values of centrality. Experimental results show that in the susceptible–infected–recovered (SIR) model experiment, compared with the benchmark methods, the infection scale of the proposed K-TOPSIS method in nine real networks is improved by 1.15%, 2.23%, 1.95%, 3.12%, 6.29%, −0.37%, 4.01%, 0.48%, and 0.48%, respectively. The novel method is improved by 0.44, 1.18, 1.16, 11.30, 2.03, 2.53, 2.70, and 2.13 in average shortest path length experiment, respectively, except for Facebook network. It shows that the novel method is reasonable and effective.

A mechanics model based on information entropy for identifying influencers in complex networks.

The network, with some or all characteristics of scale-free, self-similarity, self-organization, attractor and small world, is defined as a complex network. The identification of significant spreaders is an indispensable research direction in complex networks, which aims to discover nodes that play a crucial role in the structure and function of the network. Since influencers are essential for studying the security of the network and controlling the propagation process of the network, their assessment methods are of great significance and practical value to solve many problems. However, how to effectively combine global information with local information is still an open problem. To solve this problem, the generalized mechanics model is further improved in this paper. A generalized mechanics model based on information entropy is proposed to discover crucial spreaders in complex networks. The influence of each neighbor node on local information is quantified by information entropy, and the interaction between each node on global information is considered by calculating the shortest distance. Extensive tests on eleven real networks indicate the proposed approach is much faster and more precise than traditional ways and state-of-the-art benchmarks. At the same time, it is effective to use our approach to identify influencers in complex networks.

CSS: A new combined spreading score measurement for identifying multiple influential spreaders in complex networks

Identifying multiple influential spreaders is a significant procedure to understand, control or accelerate the dynamics of information diffusion process in complex networks effectively. For a given network [Formula: see text] and an integer [Formula: see text], we need to find a set of [Formula: see text] vertices as “seeds” which carry the information originally, and then through a certain diffusion model, the information can be spread as widely as possible. Note that these seeds cannot be too close to each other, otherwise information is easy to be congested. In this paper, we make an attempt to identify multiple spreaders by considering the “marginal benefit” [Formula: see text] of a vertex [Formula: see text] when it is added to an existing seed set [Formula: see text]. Here [Formula: see text] is defined as a function of [Formula: see text]’s influential ability and the common influence range between vertex [Formula: see text] and [Formula: see text]. In particular, we use the degree to measure the vertex’s influential ability and use the number of common neighbors between vertex [Formula: see text] and [Formula: see text] to measure their common influence range. In order to verify this new algorithm’s validity, we apply it on several social networks and the new method performs better than others. This new method is simple to implement and has lower time complexity, thus is expected to have promising applications in the future.

Identifying multiple influential spreaders in complex networks based on spectral graph theory

One of the hot research topics in propagation dynamics is identifying a set of critical nodes that can influence maximization in a complex network. The importance and dispersion of critical nodes among them are both vital factors that can influence maximization. We therefore propose a multiple influential spreaders identification algorithm based on spectral graph theory. This algorithm first quantifies the role played by the local structure of nodes in the propagation process, then classifies the nodes based on the eigenvectors of the Laplace matrix, and finally selects a set of critical nodes by the constraint that nodes in the same class are not adjacent to each other while different classes of nodes can be adjacent to each other. Experimental results on real and synthetic networks show that our algorithm outperforms the state-of-the-art and classical algorithms in the SIR model.

Identification of node centrality based on Laplacian energy of networks

Identifying influential spreaders in complex networks is a crucial issue that can help control the propagation process in complex networks. Existing methods propose substantial improvements over many classical centrality methods. Over the years, some researchers have applied concepts of graph energy to node recognition. Based on this, we propose a new node centrality — the third Laplacian energy centrality (LC). This method is to define the centrality of nodes from a global perspective and can be simplified into a local formula while inheriting the advantages of the global property, which greatly reduces the time complexity. By assuming that the propagation process in the network follows a susceptible–infected–recovery (SIR) model, we conduct extensive experiments in 13 real networks, and compare the performance of LC with a range of other centrality measures. The results show that LC is more reasonable and superior than other methods in identifying influential spreaders.

TSIFIM: A three-stage iterative framework for influence maximization in complex networks

The problem of influence maximization is a classic issue that has been well-studied in the field of network science, but most of existing researches are compromising among computational complexity or result accuracy. In this work, a three-stage iterative framework for influence maximization (TSIFIM) is presented to find a set of seed spreaders in complex networks. In TSIFIM, the initial candidate seeds are first selected by considering the global communicability of each node and its importance in their local network. Then, in addition to the candidate seeds, other remained nodes are assigned to the specific communities based on the proposed local resource allocation similarity index, and the core node in each community which satisfies the local influence threshold condition are selected as the supplementary candidate seeds. Furthermore, we employ an adaptive search strategy to find the optimal solution among these candidates. The proposed algorithm is compared with eight popular influence maximization algorithms on nine real-world networks to verify the performance. Experimental results show that TSIFIM has better performance in terms of influence spreading, sensitivity analysis, seed dispersion and statistical test.