Identifying influential nodes in complex networks: A node information dimension approach

Bian, Tian; Deng, Yong

doi:10.1063/1.5030894

Cited by 99 publications

(32 citation statements)

References 41 publications

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“…Information diffusion can reach a substantial number of audiences via an IS in a short time. There exist plenty of methods in research to identify ISs for information spread and control in SN [113][114][115].…”

Section: Use Of Graphs For Information Diffusion In Social Networkmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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Graph theory (GT) concepts are potentially applicable in the field of computer science (CS) for many purposes. The unique applications of GT in the CS field such as clustering of web documents, cryptography, and analyzing an algorithm’s execution, among others, are promising applications. Furthermore, GT concepts can be employed to electronic circuit simplifications and analysis. Recently, graphs have been extensively used in social networks (SNs) for many purposes related to modelling and analysis of the SN structures, SN operation modelling, SN user analysis, and many other related aspects. Considering the widespread applications of GT in SNs, this article comprehensively summarizes GT use in the SNs. The goal of this survey paper is twofold. First, we briefly discuss the potential applications of GT in the CS field along with practical examples. Second, we explain the GT uses in the SNs with sufficient concepts and examples to demonstrate the significance of graphs in SN modeling and analysis.

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Section: Use Of Graphs For Information Diffusion In Social Networkmentioning

confidence: 99%

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Majeed

Rauf

2020

Inventions

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“…Recently, Pu et al [58] modified local dimension in the network to identify the influential nodes. Then, Bian et al [59] measured the information dimension of node to rank the influence of node which is a new research perspective. After that, Jiang et al [60] proposed the fuzzy local dimension to identify the influential nodes.…”

Section: Introductionmentioning

confidence: 99%

Vital spreaders identification in complex networks with multi-local dimension

Wen

Pelusi

Deng

2020

Knowledge-Based Systems

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The important nodes identification has been an interesting problem in this issue. Several centrality measures have been proposed to solve this problem, but most of previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and q plays a weighting coefficient for this partition information. MLD would have different expressions with different value of q, and it would degenerate to local information dimension and variant of local dimension when q = 1 when q = 0 respectively, both of which have been effective identification method for influential nodes. Thus, MLD would be a more general method which can degenerate to some exiting centrality measures. In addition, different with classical methods, the node with low MLD would be more important in the network. Some comparison methods and real-world complex networks are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method.

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“…Information fusion problems are investigated for extremely uncertain environments, such as deep‐sea exploration and robot sensing . There exist many key problems that need to be handled in pattern classification and decision making under extremely uncertain environments. The Dempster‐Shafer evidence theory (D‐S theory) has been widely focused on and adopted in recent years for its great advantages in handling and analyzing uncertain information .…”

Section: Introductionmentioning

confidence: 99%

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

Fei

Deng

2018

Int J Intell Syst

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Information fusion under extremely uncertain environments is an important issue in pattern classification and decision‐making problems. The Dempster‐Shafer evidence theory (D‐S theory) is more and more extensively applied in dealing with uncertain information. However, the results contrary to common sense are often obtained when combining different evidence using Dempster's combination rule. How to measure the difference between different evidence is still an open issue. In this paper, a new divergence is proposed based on the Kullback‐Leibler divergence to measure the difference between different basic probability assignments (BPAs). Numerical examples are used to illustrate the computational process of the proposed divergence. Then, the similarity for different BPAs is also defined based on the proposed divergence. The basic knowledge about pattern recognition is introduced, and a new classification algorithm is presented using the proposed divergence and similarity under extremely uncertain environments. The effectiveness of the classification algorithm is illustrated by a small example handling robot sensing. The proposed method is motivated by the urgent need to develop intelligent systems, such as sensor‐based data fusion manipulators, which are required to work in complicated, extremely uncertain environments. Sensory data satisfy the conditions (1) fragmentary and (2) collected from multiple levels of resolution.

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Identifying influential nodes in complex networks: A node information dimension approach

Cited by 99 publications

References 41 publications

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Graph Theory: A Comprehensive Survey about Graph Theory Applications in Computer Science and Social Networks

Vital spreaders identification in complex networks with multi-local dimension

A new divergence measure for basic probability assignment and its applications in extremely uncertain environments

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