A novel measure to identify influential nodes: Return Random Walk Gravity Centrality

Information Sciences, Год журнала: 2023, Номер 628, С. 177 - 195

Опубликована: Янв. 20, 2023

Язык: Английский

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Identifying influential nodes in complex networks via Transformer

Information Processing & Management, Год журнала: 2024, Номер 61(5), С. 103775 - 103775

Опубликована: Май 17, 2024

Язык: Английский

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LSS: A locality-based structure system to evaluate the spreader’s importance in social complex networks

Expert Systems with Applications, Год журнала: 2023, Номер 228, С. 120326 - 120326

Опубликована: Май 6, 2023

Язык: Английский

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Identifying key rumor refuters on social media

Expert Systems with Applications, Год журнала: 2023, Номер 231, С. 120603 - 120603

Опубликована: Июнь 9, 2023

Язык: Английский

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Identifying important nodes in complex networks based on extended degree and E-shell hierarchy decomposition

Scientific Reports, Год журнала: 2023, Номер 13(1)

Опубликована: Фев. 23, 2023

Abstract The identification of important nodes is a hot topic in complex networks. Many methods have been proposed different fields for solving this problem. Most previous work emphasized the role single feature and, as result, rarely made full use multiple items. This paper proposes new method that utilizes characteristics evaluation their importance. First, an extended degree defined to improve classical degree. And E-shell hierarchy decomposition put forward determining nodes’ position through network’s hierarchical structure. Then, based on combination these two components, hybrid characteristic centrality and its version are evaluating importance nodes. Extensive experiments conducted six real networks, susceptible–infected–recovered model monotonicity criterion introduced test performance approach. comparison results demonstrate approach exposes more competitive advantages both accuracy resolution compared other five approaches.

Язык: Английский

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An improved gravity centrality for finding important nodes in multi-layer networks based on multi-PageRank

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122171 - 122171

Опубликована: Окт. 18, 2023

Язык: Английский

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Identifying influential nodes based on the disassortativity and community structure of complex network

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Апрель 11, 2024

The complex networks exhibit significant heterogeneity in node connections, resulting a few nodes playing critical roles various scenarios, including decision-making, disease control, and population immunity. Therefore, accurately identifying these influential that play crucial is very important. Many methods have been proposed different fields to solve this issue. This paper focuses on the types of disassortativity existing innovatively introduces concept node, namely, inconsistency between degree degrees its neighboring nodes, proposes measure (DoN) by step function. Furthermore, analyzes indicates many real-world network applications, such as online social networks, influence within often associated with community boundary structure network. Thus, metric based (mDC) proposed. Extensive experiments are conducted synthetic real performance DoN mDC validated through robustness immune experiment infection. Experimental analytical results demonstrate compared other state-of-the-art centrality measures, (DoN mDC) exhibits superior identification efficiency, particularly non-disassortative clear structures. we find high stability noise inaccuracies data.

Язык: Английский

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Ranking influential nodes in complex networks with community structure

PLoS ONE, Год журнала: 2022, Номер 17(8), С. e0273610 - e0273610

Опубликована: Авг. 29, 2022

Quantifying a node's importance is decisive for developing efficient strategies to curb or accelerate any spreading phenomena. Centrality measures are well-known methods used quantify the influence of nodes by extracting information from network's structure. The pitfall these pinpoint located in vicinity each other, saturating their shared zone influence. In this paper, we propose ranking strategy exploiting ubiquity community structure real-world networks. proposed community-aware naturally selects set distant spreaders with most significant One can use it centrality measure. We investigate its effectiveness using and synthetic networks controlled parameters Susceptible-Infected-Recovered (SIR) diffusion model scenario. Experimental results indicate superiority over all counterparts agnostic about Additionally, show that performs better strong high number communities heterogeneous sizes.

Язык: Английский

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Artificial benchmark for community detection with outliers (ABCD+o)

Applied Network Science, Год журнала: 2023, Номер 8(1)

Опубликована: Май 22, 2023

Abstract The A rtificial B enchmark for C ommunity D etection graph ( ABCD ) is a random model with community structure and power-law distribution both degrees sizes. generates graphs similar properties as the well-known LFR one, its main parameter $$\xi$$ ξ can be tuned to mimic counterpart in model, mixing $$\mu$$ μ . In this paper, we extend include potential outliers. We perform some exploratory experiments on new ABCD+o well real-world network show that outliers pose distinguishable properties. This ensures our may serve benchmark of outlier detection algorithms.

Язык: Английский

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Identifying influential nodes in complex networks based on spreading probability

Chaos Solitons & Fractals, Год журнала: 2022, Номер 164, С. 112627 - 112627

Опубликована: Сен. 15, 2022

Язык: Английский

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