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

Wang, Bin; Guan, Wanghao; Sheng, Yuxuan; Sheng, Jinfang; Dai, Jinying; Zhang, Junkai; Li, Qiong; Dong, Qiangqiang; Chen, Long

doi:10.1142/s0129183121500121

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Timestamp T2: Remove nodes and corresponding edges that are not present in T2 as it is a dynamic network shown in 7. We remove nodes 7, 8, and 9 from T1 and delete the related edges (8, 1), (8, 2), (8,3), (8,4), (9, 1), (9,3), and (10, 3) since nodes 8, 9 and 10 are absent from T2. After combining T1 and T2, the resulting network is shown in 8.…”

Section: Internal-external Community Index Calculationmentioning

confidence: 99%

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Influential Node Detection in Social Networks for Real-World Applications

Dutta,

Kule,

Chakraborty

2024

Preprint

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Influential node detection is a crucial task in real-world networks, such as social networks, biological networks, or information networks. It helps us to understand network dynamics as well as identify important participants. A commonly used method for detecting important nodes involves the utilisation of community detection, which seeks to divide the network into clusters of nodes that reflect high levels of connectivity. The Overlapping Community Detection approach named as Internal-External overlapping Community Detection method has been proposed to find different communities. We have introduced a new factor namely Influence Detection Factor that is used to determine influential node of a network. State transition of every node occurred in a time-varying network. State transition based on the influential node can be useful for tracking different frames of every influential node with time. Dynamic Social network analysis in disease spreading is an emerging field of research. Disease Network can be considered a real-world application in various contexts. We have considered COVID-19 India data as a case study. Each node and edge is depicted as a province/district and the number of COVID-19 cases, in the network respectively. Edge Weight is calculated using our proposed metric. We have used different centrality measures, such as degree centrality, closeness centrality, betweenness centrality, etc., to compare our metrics over real-world datasets. The observations obtained through our proposed method demonstrate a significant correlation with the real-time occurrences. Experimental results of our proposed methodology demonstrate better results in comparison to other existing approaches.

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Section: Internal-external Community Index Calculationmentioning

confidence: 99%

“…Social networks (SN) can facilitate the spread of influence. Numerous techniques are available for finding influential nodes [3] in static as well as dynamic networks. In order to govern and spread information, it is necessary to identify central nodes that are significant.…”

Section: Introductionmentioning

confidence: 99%

Influential Node Detection in Social Networks for Real-World Applications

Dutta,

Kule,

Chakraborty

2024

Preprint

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“…The complexity of influencing factors makes it impossible to add only a single influencing factor to analyze the characteristics of the whole network system in the prediction process. Therefore, the index "node attraction" [43] is introduced to comprehensively consider the influence of influencing factors on the actual network from many aspects.…”

Section: Node Attraction Indexmentioning

confidence: 99%

Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction

Jin¹,

Liu²

2022

Sustainability

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Unstable factors such as international relations, geopolitics, and transportation routes make natural gas trade complex and changeable. Diversified and flexible sources of liquefied natural gas (LNG) can guarantee the energy supply security of natural gas-consuming countries. Therefore, it is very important to find potential natural gas trade links to help the government find potential partners and prepare strategically in advance. In this paper, the global LNG network is taken as the research object. In order to fully consider the importance of nodes and the influence of economic and political factors, the “centrality degree” and “node attraction degree” are added into the link prediction algorithm, and multifactor coupling is carried out. The reliability of the improved algorithm is verified using the area under the curve (AUC) evaluation index, and the prediction results are analyzed. The results are as follows: Trinidad, Russia, Algeria, Nigeria, Angola, and Equatorial Guinea (Eq. Guinea) are more likely to establish new LNG trading relationships with other countries. For all potential trade relationships, potential relations involving the above countries are more likely to be realized within 5 years, while potential relations involving China, India, Japan, and South Korea are more likely to be realized within 2 years. China, India, and South Korea are more likely to import LNG from Algeria, and Taiwan Province is more likely to import LNG from Algeria, Angola, Eq. Guinea, and America. On the basis of the above study, states and governments can give priority to the above countries and regions when dealing with the possible LNG supply crisis.

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“…Wang et al [15] systematically introduced the relevant basis of complex networks and described the propagation mechanism and synchronization control of complex networks in detail, which is an area of particular interest in many fields. Moreover, Wang et al [16] constructed a complex network recovery model based on a polar Dalian Tong subgraph boundary and designed network average recovery and optimal recovery strategies. Zhang et al [17] studied the random synchronization of complex network clusters through fixed time control technology and made a contribution to the theoretical research on the synchronization convergence of complex networks.…”

Section: Introductionmentioning

confidence: 99%

E-Commerce Credit Network Control Strategy from a Critical Perspective

Zhang

Zheng

2022

Mathematical Problems in Engineering

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Credit problems are the main bottleneck in the development of e-commerce. Both the time and degree of e-commerce credit control directly affect its economic benefits. From the perspective of the criticality of complex systems, this paper explores the control node of e-commerce credit behaviour. Implementing control in this node can not only ensure the stable development of the credit network but also optimise the control cost. This paper constructs a credit behaviour model for the four regulatory behaviours of the transaction subject, analyses the evolution law of the credit network, and determines the critical state of the network. Finally, the paper’s empirical analysis and simulation experiments prove that when the false information in the credit network in the empirical data is 32%, the implementation of credit control has an efficient control effect. Nevertheless, 32% is not a universal result; the specific critical point value needs to be recalculated according to the theoretical derivation of the critical point combined with the actual network. These research results can help regulators obtain the highest regulatory return with the lowest regulatory investment.

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

Cited by 9 publications

References 33 publications

Influential Node Detection in Social Networks for Real-World Applications

Influential Node Detection in Social Networks for Real-World Applications

Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction

E-Commerce Credit Network Control Strategy from a Critical Perspective

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