Tootfinder

This https://arxiv.org/abs/2304.08356 has been replaced.
link: https://scholar.google.com/scholar?q=a

MANTRA: Temporal Betweenness Centrality Approximation through Sampling
We present MANTRA, a framework for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our method can compute probabilistically guaranteed high-quality temporal betweenness estimates (of nodes and temporal edges) under all the feasible temporal path optimalities, presented in the work of Buß et al. (KDD, 2020). We provide a sample-complexity analysis of our method and speed up the temporal betweenness computation using a state-of-the-art progressive sampling app…

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Yiwei Zou, Ting Li, Zong-fu Luo
https://arxiv.org/abs/2403.04977 …

Node Centrality Approximation For Large Networks Based On Inductive Graph Neural Networks
Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks. These measures find wide applications in critical tasks, such as community detection and network dismantling. However, their practical implementation on extensive networks remains computationally demanding due to their high time complexity. To mitigate these computational challenges, numerous approximat…

Structural analysis and the sum of nodes' betweenness centrality in complex networks
Ronghao Deng, Meizhu Li, Qi Zhang
https://arxiv.org/abs/2402.13507

Structural analysis and the sum of nodes' betweenness centrality in complex networks
Structural analysis in network science is finding the information hidden from the topology structure of complex networks. Many methods have already been proposed in the research on the structural analysis of complex networks to find the different structural information of networks. In this work, the sum of nodes' betweenness centrality (SBC) is used as a new structural index to check how the structure of the complex networks changes in the process of the network's growth. We build two four diff…

This https://arxiv.org/abs/2308.05254 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Data-driven Intra-Autonomous Systems Graph Generator
Accurate modeling of realistic network topologies is essential for evaluating novel Internet solutions. Current topology generators, notably scale-free-based models, fail to capture multiple properties of intra-AS topologies. While scale-free networks encode node-degree distribution, they overlook crucial graph properties like betweenness, clustering, and assortativity. The limitations of existing generators pose challenges for training and evaluating deep learning models in communication netwo…

This https://arxiv.org/abs/2311.13221 has been replaced.
initial toot: https://mastoxiv.page/@arX…

The behavior of rich-club coefficient in scale-free networks
The rich-club phenomenon, which provides information about the association between nodes, is a useful method to study the hierarchy structure of networks. In this work, we explore the behavior of rich-club coefficient (RCC) in scale-free networks, and find that the degree-based RCC is a power function of degree centrality with power exponent \b{eta} and the betweenness-based RCC is a linear function of betweenness centrality with slope θ. The findings from the New York metro network further va…