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

Germinate

啜泣

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.

BOLT

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

出沒

Paradox

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

Nostalgia

故意釣到白楊

Lymphocyte

Palpable

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

使人煩燥

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