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Titlebook: Complex Networks and Their Applications XI; Proceedings of The E Hocine Cherifi,Rosario Nunzio Mantegna,Salvatore M Conference proceedings

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樓主: cerebral-cortex
11#
發(fā)表于 2025-3-23 10:02:24 | 只看該作者
12#
發(fā)表于 2025-3-23 14:04:40 | 只看該作者
Structure of?Core-Periphery Communitiesanding of the precise structure of core-periphery communities in social networks including the connectivity structure and interaction rates between agents. In this paper, we use a game-theoretic approach to derive a more precise characterization of the structure of core-periphery communities.
13#
發(fā)表于 2025-3-23 21:51:48 | 只看該作者
Outliers in?the?ABCD Random Graph Model with?Community Structure (ABCD+o)s and community sizes. The model generates graphs with similar properties as the well-known . one, and its main parameter . can be tuned to mimic its counterpart in the . model, the mixing parameter .. In this paper, we extend the . model to include potential outliers. We perform some exploratory ex
14#
發(fā)表于 2025-3-24 02:15:07 | 只看該作者
15#
發(fā)表于 2025-3-24 06:23:11 | 只看該作者
AutoGF: Runtime Graph Filter Tuning for?Community Node Ranking of social network users). To achieve this, approaches often start with a few example community members and employ graph filters that rank nodes based on their structural proximity to the examples. Choosing between well-known filters typically involves experiments on existing graphs, but their effic
16#
發(fā)表于 2025-3-24 09:03:27 | 只看該作者
17#
發(fā)表于 2025-3-24 13:22:07 | 只看該作者
18#
發(fā)表于 2025-3-24 16:30:06 | 只看該作者
19#
發(fā)表于 2025-3-24 22:08:11 | 只看該作者
Uniformly Scattering Neighboring Nodes of?an?Ego-Centric Network on?a?Spherical Surface for?Better Non between our space-filling distribution of points on a spherical surface to the minimum energy design on a two-dimensional flat plane with a specific gradient. We provide a demonstration on allocating nodes of an ego-centric network of 50 nodes, and some distance statistics show the good performan
20#
發(fā)表于 2025-3-25 00:59:34 | 只看該作者
AutoGF: Runtime Graph Filter Tuning for?Community Node Rankinggeneralized graph filter form with a novel optimization algorithm. Experiments on 27 community node ranking tasks across three real-world networks of various sizes reveal that runtime algorithm selection selects near-best AUC and NDCG among a list of 8 popular alternatives, and that parameter tuning
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