A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks

Berahmand, Kamal; Bouyer, Asgarali; Samadi, Negin

doi:10.1016/j.chaos.2018.03.014

Cited by 89 publications

(49 citation statements)

References 42 publications

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“…In order to assess the performance of DCC, four experiments are conducted based on the four datasets. DC (Degree centrality), CC (Closeness centrality), BC (Betweenness centrality), EC (Eigencentrality), Ks (K-shell centrality), LC (Local centrality) and NP (the centrality proposed in [32]) are applied to the same datasets for comparison. In addition, SI model is adopted to simulate the spreading ability.…”

Section: B Analysis and Resultsmentioning

confidence: 99%

“…In this paper, a novel centrality (abbreviated as DCC) belonging to semi-local centrality measures is proposed to identify influential nodes in complex networks. As indicated in [32], local characteristics including degree and clustering coefficient play important roles in identifying influential nodes, but the method in [32] (denoted as NP) does not consider neighbor information comprehensively. In our method, we take degree and clustering coefficient into consideration as well as neighbor information.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Centrality of Influential Nodes Identification in Complex Networks

et al. 2020

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Influential nodes identification in complex networks is vital for understanding and controlling the propagation process in complex networks. Some existing centrality measures ignore the impacts of neighbor node. It is well-known that degree is a famous centrality measure for influential nodes identification, and the contributions of neighbors also should be taken into consideration. Furthermore, topological connections among neighbors will affect nodes' spreading ability, that is, the denser the connections among neighbors, the greater the chance of infection. In this paper, we propose a novel centrality, called DCC, to identify influential nodes by comprehensively considering degree and clustering coefficient as well as neighbors. The weights of degree and clustering coefficient are calculated by entropy technology. To verify the feasibility and effectiveness of DCC, the comparisons between DCC and other centrality measures in four aspects are conducted based on four real networks. The experimental results demonstrate that DCC is more effective in identifying influential nodes. INDEX TERMS Complex networks, influential nodes, degree centrality, neighbor node, clustering coefficient.

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Section: B Analysis and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Centrality of Influential Nodes Identification in Complex Networks

et al. 2020

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“…In general, the methods can be categorized into four classifications [13]: (1) Neighborhood-based centrality, such as degree and H-index [14], whose essence is to measure the importance of nodes based on the number of 1-step neighbors or multi-step neighbors; (2) Path-based centrality, such as closeness [15] and Katz centrality [16], whose essence is to measure the importance of nodes based on the shortest path; (3) Iterative refinement centrality, such as Eigenvector [17] and LeaderRank [18], whose essence is to measure the importance of the nodes based on the importance of their neighbors; (4) Node-operation-based centrality, such as connectivity-sensitive method [19] and stability-based method [20], whose essence is to measure the importance of the nodes by observing the impact on topological structure when deleting or merging the nodes. Among these methods, there are some centrality methods specializing in identifying influential nodes such as a centrality based on the negative and positive effects of the clustering coefficient [21], and a new local and multidimensional ranking measure [22].But theses methods just consider a certain structure in general.…”

Section: Related Workmentioning

confidence: 99%

A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks

et al. 2020

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In complex networks, identifying influential nodes is of great importance for its wide applications. Traditional centrality methods are usually directly based on topological structures of networks, and different centrality methods consider different structural characteristics related to the functional importance. However, in many scenarios, it always exists a complex and nonlinear relationship between the functional importance of a node and its various features including local location, global location, etc., which is hard to be described by one centrality. In order to solve this problem, this paper proposes a framework based on machine learning to measure the importance of nodes in the propagation scenario. This framework first constructs the feature vector of each node based on the existing centrality methods which can reflect nodes' different topological structures and the infection rate which is an important factor in the propagation scenarios, then labels each node based on the real propagation ability obtained from simulated propagation experiments based on SIR model, last uses seven machine learning algorithms to learn the complex relationship between the real propagation ability of each node and its various structural features. The experimental results in real-world networks show that the classification accuracy of the model based on machine learning is generally higher than that of the traditional centrality methods based on one certain topology. INDEX TERMS Complex networks, influential nodes, machine learning, centrality.

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“…Yang et al [34] identify the influential nodes by incorporating the degree and clustering-coefficient of neighbor nodes. Berahmand et al [35] incorporate the natural characteristics of complex networks to capture the spreader node. Wang et al [36] identify the influential spreader by considering the weight neighborhood nodes in complex networks.…”

Section: B Network Centralitymentioning

confidence: 99%

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

2020

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Constructing social networks for real epidemic cases is very challenging. Many mathematical models have been proposed to model such networks using simulation models, such as susceptibleinfected (SI), susceptible-infected-recovered (SIR), and susceptible-exposed-infectious-removed (SEIR). Nonetheless, social network analyses can fail to capture real conditions, as such models are constructed based on many assumptions. Furthermore, unlike standard online social networks (OSNs), a social epidemic network requires different treatment from both model construction and social network analysis perspectives, especially for the detection of superspreaders. To address these issues, we propose a trajectory linkage method to automatically discover social networks from historical patient-trajectory data, wherein relations among patients are determined by spatial proximity and time-windows. Moreover, we introduce a novel spreader centrality measure that is devised to identify superspreaders in a social epidemic network. Extensive experiments were performed using real epidemic data. The results revealed that trajectory linkage can obtain a denser social network model than is possible by only incorporating patient data (the "who is infected by whom" relationship). By performing a social network analysis, the trajectory linkage model can express the real conditions of the patient relationship. Furthermore, our spreader centrality can capture the real superspreaders more effectively than can the existing centrality measure in social epidemic networks. INDEX TERMS Social networks, epidemic networks, network centrality, spreader centrality, social network analysis.

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A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks

Cited by 89 publications

References 42 publications

A Novel Centrality of Influential Nodes Identification in Complex Networks

A Novel Centrality of Influential Nodes Identification in Complex Networks

A Machine Learning Based Framework for Identifying Influential Nodes in Complex Networks

Trajectory Linkage and Spreader Centrality for Social Epidemic Networks

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