Identifying Influential Spreaders Based on Adaptive Weighted Link Model

Li, Zhe; Ren, Tao; Xu, Yang; Chang, Boyu; Chen, Dongming; Sun, Shixiang

doi:10.1109/access.2020.2985713

Cited by 12 publications

(6 citation statements)

References 34 publications

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“…Using an influence node, it cannot wholly grasp the hidden information in the network. For the depth analysis of entire networks composed of nodes and edges, community structure helps to detect the community in the graph [16,17,27]. There are many existing algorithms available in the detection of community.…”

Section: Review Of Literaturementioning

confidence: 99%

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Devi¹,

Rajalakshmi²

2023

ITC

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Due to technological development, social media platforms like forums and microblogs allow people to share their experiences, thoughts, and feelings. The organization, shopping groups etc. has major discussions regarding their business advertisements and product reviews. Also, there are certain followers for particular person or group due to their interests. Here the major issue is to know who or which group in social media is more influenced. The social media analysis needs to perform for identifying influenced person in the social media. The influencer node/person detection in a certain community is already done using greedy algorithm, genetic algorithm, ant colony optimization, cuckoo search algorithms. These existing techniques takes more time for diffusion and accuracy in prediction is not satisfied by users. To overcome this issues, in this research influencer node is identified using optimized Girvan Newman Cuckoo Search Algorithm (GNCSA). First Grivan Newman is used to identify the community and perform community detection. Cuckoo search algorithm uses host bird strategy in finding cuckoo eggs in his nest. Based on the centrality measure it decides whether the node is an influencer or not. This paper proposed Influencer detection by forming community first and measures angular centrality using optimized Girvan Newman cuckoo search algorithm. Our proposed work GNCSA gives a better accuracy rate for the data sets of Dolphin 0.89, for Facebook dataset got 0.93, Twitter data set got 0.94 and for YouTube data set 0.92, karate club and football got 0.91. This proposed work increases the intracommunity of the social network and improves its performance accurately by detecting the influencer in the social network.

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Section: Review Of Literaturementioning

confidence: 99%

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Devi¹,

Rajalakshmi²

2023

ITC

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“…Li et al [82] proposed an adaptive semi-local-informationbased method that measures the influences of nodes in certain networked dynamics, in which the links in the subgraph are made up of the second-order neighbors of nodes. These links are given different weights adaptively.…”

Section: 2) Semi-local-based Analysis Techniquementioning

confidence: 99%

“…Linear Threshold model [180], [79], [82], [103], [175], [174], [189], [157], [80], [33] [92], [91], [128], [163], [170], [153], [107], [174] [134], [170], [153], [107] For each of the 12 analysis approaches described in this survey and for each set of the selected algorithms that fall under the approach, we outline our conclusions inferred from the papers reported these algorithms and our findings of the experimental results. Moreover, we outline the consensus among the characteristics of the algorithms that fall under a same approach to serve as a verification of our proposed taxonomy.…”

Section: Sir Model Independent Cascade Modelmentioning

confidence: 99%

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Taha

2022

IEEE Access

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Identifying the influential and spreader nodes in complex networks solves many types of complex scientific problems. In social networks, identifying the influential individuals can be useful for structuring techniques that accelerate or hinder information propagation. Each node in the network has unique characteristics that reflect its importance. These characteristics are used by researchers to design many different centrality algorithms. Unfortunately, current survey papers categorize these algorithms into broad classes and do not draw distinguishable boundaries among the specific techniques adopted by them. This can result in misclassifying unrelated algorithms into the same analysis category. To overcome this, we introduce a methodology-based taxonomy for classifying the algorithms that identify top-k influential spreaders into hierarchically nested, specific, and fine-grained categories. We survey 184 papers and discuss their algorithms, which fall under 26 specific techniques. Our methodological taxonomy classifies the algorithms hierarchically into the following manner: Analysis type analysis scope analysis approach analysis category analysis sub-category analysis specific technique. We introduce in this paper a comprehensive survey, review, and experimental evaluation of the recent and state-of-the-art algorithms that identify the top-k and influential spreader nodes in social networks.

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“…There are already many methods to identify important nodes in a network. For example, degree, [1] H-index [2] and k-shell [3] are based on nodes' neighbors; closeness centrality [4] and betweenness centrality [5] are based on the path; and quasi-Laplacian centrality, [6] the local gravity model [7] and AWLM [8] are based on semi-local structural information. In contrast, the identification and importance ranking of key edges has received less attention.…”

Section: Introductionmentioning

confidence: 99%

Important edge identification in complex networks based on local and global features

Song

2023

Chinese Phys. B

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Identifying important nodes and edges in complex networks has always been a popular research topic in network science and also has important implications for the protection of real-world complex systems. Finding the critical structures in a system allows us to protect the system from attacks or failures with minimal cost. Currently, the problem of identifying critical nodes in networks has been widely studied by many scholars, and the theory is becoming more and more mature. However, there is relatively little research related to edges. In fact, critical edges play an important role in maintaining the basic functions of the network and keeping the integrity of the structure. Sometimes protecting critical edges is less costly and more flexible in operation than just focusing on nodes. Considering the integrity of the network topology and the propagation dynamics on it, this paper proposes a centrality measure based on the number of high-order structural overlaps in the first and second-order neighborhoods of edges. The effectiveness of the metric is verified by the Infection-Susceptibility (SI) model, the robustness index R, and the number of connected branches θ. A comparison was made with three currently popular edge importance metrics from two synthetic and four real networks. The simulation results show that the method outperforms existing methods in identifying critical edges that have a significant impact on both network connectivity and propagation dynamics. At the same time, the near-linear time complexity can be applied to large-scale networks.

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Identifying Influential Spreaders Based on Adaptive Weighted Link Model

Cited by 12 publications

References 34 publications

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Community Detection by Node Betweenness Using Optimized Girvan-Newman Cuckoo Search Algorithm

Identifying the Top-k Influential Spreaders in Social Networks: a Survey and Experimental Evaluation

Important edge identification in complex networks based on local and global features

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