Graph convolutional neural network for human action recognition: A comprehensive survey
Video-based human action recognition is one of the most important and challenging areas
of research in the field of computer vision. Human action recognition has found many …
of research in the field of computer vision. Human action recognition has found many …
[HTML][HTML] A survey on heterogeneous information network based recommender systems: Concepts, methods, applications and resources
As an important way to alleviate information overload, a recommender system aims to filter
out irrelevant information for users and provides them items that they may be interested in. In …
out irrelevant information for users and provides them items that they may be interested in. In …
A survey on knowledge graph-based recommender systems
To solve the information explosion problem and enhance user experience in various online
applications, recommender systems have been developed to model users' preferences …
applications, recommender systems have been developed to model users' preferences …
Meta-learning on heterogeneous information networks for cold-start recommendation
Cold-start recommendation has been a challenging problem due to sparse user-item
interactions for new users or items. Existing efforts have alleviated the cold-start issue to …
interactions for new users or items. Existing efforts have alleviated the cold-start issue to …
Learning community embedding with community detection and node embedding on graphs
In this paper, we study an important yet largely under-explored setting of graph embedding,
ie, embedding communities instead of each individual nodes. We find that community …
ie, embedding communities instead of each individual nodes. We find that community …
Meta-graph based recommendation fusion over heterogeneous information networks
Heterogeneous Information Network (HIN) is a natural and general representation of data in
modern large commercial recommender systems which involve heterogeneous types of …
modern large commercial recommender systems which involve heterogeneous types of …
Graph CNNs with motif and variable temporal block for skeleton-based action recognition
Hierarchical structure and different semantic roles of joints in human skeleton convey
important information for action recognition. Conventional graph convolution methods for …
important information for action recognition. Conventional graph convolution methods for …
Learning to count isomorphisms with graph neural networks
Subgraph isomorphism counting is an important problem on graphs, as many graph-based
tasks exploit recurring subgraph patterns. Classical methods usually boil down to a …
tasks exploit recurring subgraph patterns. Classical methods usually boil down to a …
Hetespaceywalk: A heterogeneous spacey random walk for heterogeneous information network embedding
Heterogeneous information network (HIN) embedding has gained increasing interests
recently. However, the current way of random-walk based HIN embedding methods have …
recently. However, the current way of random-walk based HIN embedding methods have …
Motif-GCNs with local and non-local temporal blocks for skeleton-based action recognition
Recent works have achieved remarkable performance for action recognition with human
skeletal data by utilizing graph convolutional models. Existing models mainly focus on …
skeletal data by utilizing graph convolutional models. Existing models mainly focus on …