A survey of heterogeneous information network analysis
Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …
most contemporary researches model them as homogeneous information networks, without …
NetSpam: A network-based spam detection framework for reviews in online social media
Nowadays, a big part of people rely on available content in social media in their decisions
(eg, reviews and feedback on a topic or product). The possibility that anybody can leave a …
(eg, reviews and feedback on a topic or product). The possibility that anybody can leave a …
Hete-cf: Social-based collaborative filtering recommendation using heterogeneous relations
In this paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a social collaborative filtering …
heterogeneous social networks and proposed Hete-CF, a social collaborative filtering …
Leveraging meta-path contexts for classification in heterogeneous information networks
A heterogeneous information network (HIN) has as vertices objects of different types and as
edges the relations between objects, which are also of various types. We study the problem …
edges the relations between objects, which are also of various types. We study the problem …
{HinDom}: A robust malicious domain detection system based on heterogeneous information network with transductive classification
X Sun, M Tong, J Yang, L **nran, L Heng - … International Symposium on …, 2019 - usenix.org
Domain name system (DNS) is a crucial part of the Internet, yet has been widely exploited by
cyber attackers. Apart from making static methods like blacklists or sinkholes infeasible …
cyber attackers. Apart from making static methods like blacklists or sinkholes infeasible …
DHNE: Network representation learning method for dynamic heterogeneous networks
Analyzing the rich information behind heterogeneous networks through network
representation learning methods is signifcant for many application tasks such as link …
representation learning methods is signifcant for many application tasks such as link …
Mining health knowledge graph for health risk prediction
Nowadays classification models have been widely adopted in healthcare, aiming at
supporting practitioners for disease diagnosis and human error reduction. The challenge is …
supporting practitioners for disease diagnosis and human error reduction. The challenge is …
Social media for opioid addiction epidemiology: Automatic detection of opioid addicts from twitter and case studies
Opioid (eg, heroin and morphine) addiction has become one of the largest and deadliest
epidemics in the United States. To combat such deadly epidemic, there is an urgent need for …
epidemics in the United States. To combat such deadly epidemic, there is an urgent need for …
Social media analytics, types and methodology
The rapid growth of Social Media Networks (SMN) initiated a new era for data analytics. We
use various data mining and machine learning algorithms to analyze different types of data …
use various data mining and machine learning algorithms to analyze different types of data …
Constructing a knowledge-based heterogeneous information graph for medical health status classification
Applying Pearson correlation and semantic relations in building a heterogeneous
information graph (HIG) to develop a classification model has achieved a notable …
information graph (HIG) to develop a classification model has achieved a notable …