Research and Practice of Personalized Learning Recommendation Based on Dynamic Community Discovery

2019 ◽  
Author(s):  
Zhengqiao XU ◽  
Dewei ZHAO
Keyword(s):  
Personalized Learning ◽  
Community Discovery ◽  
Research And Practice ◽  
Dynamic Community

A Framework for Detecting Weighted Communities in the International Trade Network

2014 ◽  
Vol 513-517 ◽  
pp. 2059-2062
Author(s):  
Lei Ming Yan ◽  
Jin Han
Keyword(s):  
Social Networks ◽  
International Trade ◽  
Social Network ◽  
Network Analysis ◽  
Trade Network ◽  
Open Problems ◽  
Community Discovery ◽  
Detection Algorithms ◽  
Dynamic Community ◽  
International Trade Network

Community discovery is a crucial task in social network analysis, especially in describing the evolution of social networks. Although some works have focused on finding the dynamic community, there are still some open problems need to be conquered, such as analyzing the dynamic and weighted community. In this paper, we propose a framework for analyzing weighted communities and their evolutions via clustering correlated weight vectors to enhance existing community detection algorithms. The International trade network is used to verify our framework. Experiments show that the framework discovers and captures the evolving behaviors with temporal elements and weight values.

scholarly journals Dynamic Community Discovery via Common Subspace Projection

2021 ◽  
Author(s):  
Lanlan Yu ◽  
Ping Li ◽  
Jie Zhang ◽  
Juergen Kurths
Keyword(s):  
Subspace Projection ◽  
Community Discovery ◽  
Dynamic Community

An Intelligent Personalized Learning Model Based on Community Discovery Method

2010 ◽  
Vol 159 ◽  
pp. 248-251 ◽  
Author(s):  
Min Xu ◽  
Yuan Zhang ◽  
Mei Qi Fang ◽  
Ning Li
Keyword(s):  
Collaborative Learning ◽  
Social Group ◽  
Knowledge Structure ◽  
Personalized Learning ◽  
Learning Model ◽  
Learning System ◽  
Knowledge Communities ◽  
Community Discovery ◽  
System Recommendation ◽  
Discovery Method

In this paper, we proposed a model of support personalized learning based on SGCL (Social Group Collaborative Learning System). In the model, we provide two algorithms to discover knowledge communities. Based on the community discovery result and system recommendation policy, we give our user the recommendation suggestions to help them to construct their personalized knowledge structure. The paper mainly introduce these algorithms, the AG algorithm based on aggregation and the KC algorithm based on K-Clique model, which are algorithms to discover knowledge communities in SGCL.

Dynamic community discovery algorithm of Web services based on collaborative filtering

2013 ◽  
Vol 33 (8) ◽  
pp. 2095-2099
Author(s):  
Zhong WU ◽  
Guihua NIE ◽  
Donglin CHEN ◽  
Peilu ZHANG
Keyword(s):  
Web Services ◽  
Collaborative Filtering ◽  
Community Discovery ◽  
Dynamic Community
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