The aging effect in evolving scientific citation networks

Hu, Feng; Ma, Lin; Zhan, Xiu-Xiu; Zhou, Yinzuo; Liu, Chuang; Zhao, Haixing; Zhang, Zi-Ke

doi:10.1007/s11192-021-03929-8

Cited by 20 publications

(5 citation statements)

References 71 publications

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“…Another possible continuation of our analysis may be achieved by application of the hypernetwork framework, when the notion of a relation between two objects is generalized to relations between many objects [26]. Such approach is effective in modeling processes when "bags of entities" arrive to join the existing network and is used to describe, e.g., the network of authors, citations or references joined by paper or journal, see [45,24].…”

Section: Discussionmentioning

confidence: 99%

Network of scientific concepts: empirical analysis and modeling

Palchykov¹,

Krasnytska²,

Mryglod³

et al. 2021

Preprint

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Concepts in a certain domain of science are linked via intrinsic connections reflecting the structure of knowledge. To get a qualitative insight and a quantitative description of this structure, we perform empirical analysis and modeling of the network of scientific concepts in the domain of physics. To this end we use a collection of manuscripts submitted to the e-print repository arXiv and the vocabulary of scientific concepts collected via the ScienceWISE.info platform and construct a network of scientific concepts based on their co-occurrences in publications. The resulting complex network possesses a number of specific features (high node density, dissortativity, structural correlations, skewed node degree distribution) that can not be understood as a result of simple growth by several commonly used network models. We show that the model based on a simultaneous account of two factors, growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network.

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Section: Discussionmentioning

confidence: 99%

Network of scientific concepts: empirical analysis and modeling

Palchykov¹,

Krasnytska²,

Mryglod³

et al. 2021

Preprint

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“…(iii) Preferential attachment-The probability Π i for each old node i to be attached by the newly added node is proportional not only to the sum of the degree k i and the initial attractiveness A i of node i but also to the power of its age, t a i , where α is the aging parameter and τ i is node iʼs age. The choice of the power function t a i to characterize the aging effect for old nodes is similar to the [41,46,47]. For simplicity, we assume an identical and constant initial attractiveness, A, for all nodes, i.e.…”

Section: Preferential Attachment Network Model With Aging and Initial...mentioning

confidence: 99%

“…Although very insightful, the above mentioned models for network growth have largely neglected the fact that during the growth of many real networks, aging of nodes occurs [19,[41][42][43][44][45][46][47]. For example, in scientific citation networks, old papers will gradually loose attractiveness since they are no longer sufficiently topical (or they are more often credited by intermediate citations) [43,44].…”

Section: Introductionmentioning

confidence: 99%

Preferential attachment network model with aging and initial attractiveness

Peng¹

2022

Commun. Theor. Phys.

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In this paper, we generalize the growing network model with preferential attachment for new links to simultaneously include aging and initial attractiveness of nodes. The network evolves with the addition of a new node per unit time, and each new node has m new links that with probability πi are connected to nodes i already present in the network. In our model, the preferential attachment probability πi is proportional not only to ki+A, the sum of the old node i's degree ki and its initial attractiveness A, but also to the aging factor τi -α , where τi is the age of the old node i . That is, πi∝(ki+A)τi -α . Based on the continuum approximation, we present a mean-field analysis that predicts the degree dynamics of the network structure. We show that depending on the aging parameter α two different network topologies can emerge. For α<1, the network exhibits scaling behavior with a power-law degree distribution P(k)∝ k -γ for large k where the scaling exponent γ increases with the aging parameter α and is linearly correlated with the ratio A/m. Moreover, the average degree k(ti,t) at time t for any node i that is added into the network at time ti scales as k(ti,t)∝ ti -β where 1/β is a linear function of A/m. For α>1, such scaling behavior disappears and the degree distribution is exponential.

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“…With the deepening development of the network science field and to overcome the limitations of binary interaction systems, hypergraphs have emerged. Meanwhile, researchers have gradually shifted their focus to related theoretical studies of hypergraph structures [ 6 , 7 ], evolution [ 8 , 9 , 10 ], and dynamics [ 11 , 12 ], while the study of centrality [ 13 , 14 , 15 ] is also thriving.…”

Section: Introductionmentioning

confidence: 99%

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

Hu,

Tian,

Zhang

2023

Entropy

Self Cite

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Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph’s s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

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The aging effect in evolving scientific citation networks

Cited by 20 publications

References 71 publications

Network of scientific concepts: empirical analysis and modeling

Network of scientific concepts: empirical analysis and modeling

Preferential attachment network model with aging and initial attractiveness

Identifying Vital Nodes in Hypergraphs Based on Von Neumann Entropy

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