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Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however,…

社会与信息网络 · 计算机科学 2025-07-10 Chang Su , Fang Zhou , Linyuan Lü

With great theoretical and practical significance, identifying the node spreading influence of complex network is one of the most promising domains. So far, various topology-based centrality measures have been proposed to identify the node…

物理与社会 · 物理学 2014-08-27 Jian-Hong Lin , Jian-Guo Liu , Qiang Guo

Identifying the most influential spreaders is important to understand and control the spreading process in a network. As many real-world complex systems can be modeled as multilayer networks, the question of identifying important nodes in…

物理与社会 · 物理学 2021-01-08 Qi Zeng , Ying Liu , Liming Pan , Ming Tang

Identifying the most influential spreaders is an important issue in controlling the spreading processes in complex networks. Centrality measures are used to rank node influence in a spreading dynamics. Here we propose a node influence…

物理与社会 · 物理学 2016-03-23 Ying Liu , Ming Tang , Tao Zhou , Younghae Do

One of the most central questions in network science is: which nodes are most important? Often this question is answered using structural properties such as high connectedness or centrality in the network. However, static structural…

物理与社会 · 物理学 2021-07-02 Casper van Elteren , Rick Quax , Peter Sloot

Background: Controlling global epidemics in the real world and accelerating information propagation in the artificial world are of great significance, which have activated an upsurge in the studies on networked spreading dynamics. Lots of…

物理与社会 · 物理学 2012-04-03 Ai-Xiang Cui , Zimo Yang , Tao Zhou

As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank…

物理与社会 · 物理学 2015-12-18 Ye Sun , Long Ma , An Zeng , Wen-Xu Wang

The identification of the most influential spreaders in networks is important to control and understand the spreading capabilities of the system as well as to ensure an efficient information diffusion such as in rumor-like dynamics. Recent…

In complex networks there are overlapping substructures or "circles" that consist of nodes belonging to multiple cohesive subgroups. Yet the role of these overlapping nodes in influence spreading processes remains underexplored. In the…

社会与信息网络 · 计算机科学 2026-03-12 Kosti Koistinen , Vesa Kuikka , Kimmo Kaski

Identifying key players in collective dynamics remains a challenge in several research fields, from the efficient dissemination of ideas to drug target discovery in biomedical problems. The difficulty lies at several levels: how to single…

物理与社会 · 物理学 2012-03-06 Konstantin Klemm , M. Angeles Serrano , Victor M. Eguiluz , Maxi San Miguel

An efficient strategy for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and…

物理与社会 · 物理学 2018-10-23 Shi-Long Luo , Kai Gong , Li Kang

In spite of the vast literature on spreading dynamics on complex networks, the role of local synergy, i.e., the interaction of elements that when combined produce a total effect greater than the sum of the individualelements, has been…

物理与社会 · 物理学 2017-05-03 Quan-Hui Liu , Wei Wang , Ming Tang , Tao Zhou , Ying-Cheng Lai

We propose an efficient and accurate measure for ranking spreaders and identifying the influential ones in spreading processes in networks. While the edges determine the connections among the nodes, their specific role in spreading should…

物理与社会 · 物理学 2017-09-06 Ying Liu , Ming Tang , Younghae Do , Pak Ming Hui

A pivotal idea in network science, marketing research and innovation diffusion theories is that a small group of nodes -- called influencers -- have the largest impact on social contagion and epidemic processes in networks. Despite the…

物理与社会 · 物理学 2018-12-12 Flavio Iannelli , Manuel Sebastian Mariani , Igor M. Sokolov

Identifying highly susceptible individuals in spreading processes is of great significance in controlling outbreaks. In this paper, we explore the susceptibility of people in susceptible-infectious-recovered (SIR) and rumor spreading…

物理与社会 · 物理学 2015-05-20 Shaoting Tang , Xian Teng , Sen Pei , Shu Yan , Zhiming Zheng

Understanding spreading dynamics will benefit society as a whole in better preventing and controlling diseases, as well as facilitating the socially responsible information while depressing destructive rumors. In network-based spreading…

物理与社会 · 物理学 2015-01-16 Ai-Xiang Cui , Zimo Yang , Tao Zhou

Spreading broadly refers to the notion of an entity propagating throughout a networked system via its interacting components. Evidence of its ubiquity and severity can be seen in a range of phenomena, from disease epidemics to financial…

物理与社会 · 物理学 2018-04-25 Christos Ellinas

In this paper, we study the crucial elements of complex networks, namely nodes, and edges and their properties such as their community structure, which play an important role in dictating the robustness of the network towards structural…

社会与信息网络 · 计算机科学 2021-02-04 V. Parimi , A. Pal , S. Ruj , P. Kumaraguru , T. Chakraborty

The structure of a network dramatically affects the spreading phenomena unfolding upon it. The contact distribution of the nodes has long been recognized as the key ingredient in influencing the outbreak events. However, limited knowledge…

物理与社会 · 物理学 2014-07-30 Luca Ferreri , Paolo Bajardi , Mario Giacobini , Silvia Perazzo , Ezio Venturino

Identifying influential nodes plays a pivotal role in understanding, controlling, and optimizing the behavior of complex systems, ranging from social to biological and technological domains. Yet most centrality-based approaches rely on…

物理与社会 · 物理学 2025-12-12 Yajing Hao , Longzhao Liu , Xin Wang , Zhihao Han , Ming Wei , Zhiming Zheng , Shaoting Tang
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