An Effective Algorithm for Optimizing Surprise in Network Community Detection
Many methods have been proposed to detect communities/modules in various networks such as biological molecular networks and disease networks, while optimizing statistical measures for community structures is one of the most popular ways for community detection. Surprise, which is a statistical measu...
Guardado en:
Autores principales: | Yan-Ni Tang, Ju Xiang, Yuan-Yuan Gao, Zhi-Zhong Wang, Hui-Jia Li, Shi Chen, Yan Zhang, Jian-Ming Li, Yong-Hong Tang, Yong-Jun Chen |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
IEEE
2019
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b962e79c8ed34d83b0db87ef3b15e781 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
The Yield Curve Factors and Economic Surprises in the Chilean Bond Market
por: Ceballos,Luis
Publicado: (2014) -
The Influence of Negative Surprise on Hedonic Adaptation
por: Ana Paula Kieling, et al.
Publicado: (2016) -
Risk of Refractive Prediction Error After Cataract Surgery in Patients with Thyroid Eye Disease
por: Strong Caldwell A, et al.
Publicado: (2021) -
An Approach to Detect Anomaly in Video Using Deep Generative Network
por: Savath Saypadith, et al.
Publicado: (2021) -
A lightweight model for multi-traffic object detection based on deep learning under complex traffic conditions
por: Guoqiang Chen, et al.
Publicado: (2021)