Coupled Semi-supervised Clustering: Exploring Attribute Correlations in Heterogeneous Information Networks

2019 ◽  
pp. 95-109
Author(s):  
Jianan Zhao ◽  
Ding Xiao ◽  
Linmei Hu ◽  
Chuan Shi
Keyword(s):  
Information Networks ◽  
Heterogeneous Information ◽  
Supervised Clustering ◽  
Heterogeneous Information Networks

scholarly journals Measuring diversity in heterogeneous information networks

2021 ◽  
Vol 859 ◽  
pp. 80-115
Author(s):  
Pedro Ramaciotti Morales ◽  
Robin Lamarche-Perrin ◽  
Raphaël Fournier-S'niehotta ◽  
Rémy Poulain ◽  
Lionel Tabourier ◽  
...  
Keyword(s):  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Measuring Diversity

scholarly journals Discovering Maximal Motif Cliques in Large Heterogeneous Information Networks

2019 ◽  
Author(s):  
Jiafeng Hu ◽  
Reynold Cheng ◽  
Kevin Chen-Chuan Chang ◽  
Aravind Sankar ◽  
Yixiang Fang ◽  
...  
Keyword(s):  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks

scholarly journals Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jibing Wu ◽  
Lianfei Yu ◽  
Qun Zhang ◽  
Peiteng Shi ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Real World ◽  
Tensor Decomposition ◽  
Information Networks ◽  
Community Discovery ◽  
Star Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
General Network ◽  
Real World Datasets ◽  
Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

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