A survey on heterogeneous graph embedding: methods, techniques, applications and sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn …
Network representation learning: a systematic literature review
B Li, D Pi - Neural Computing and Applications, 2020 - Springer
Omnipresent network/graph data generally have the characteristics of nonlinearity,
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
sparseness, dynamicity and heterogeneity, which bring numerous challenges to network …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Representation learning for dynamic graphs: A survey
Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …
recommender systems, ontologies, biology, and computational finance. Traditionally …
Higher-order attribute-enhancing heterogeneous graph neural networks
Graph neural networks (GNNs) have been widely used in deep learning on graphs. They
can learn effective node representations that achieve superior performances in graph …
can learn effective node representations that achieve superior performances in graph …
Network schema preserving heterogeneous information network embedding
As heterogeneous networks have become increasingly ubiquitous, Heterogeneous
Information Network (HIN) embedding, aiming to project nodes into a low-dimensional …
Information Network (HIN) embedding, aiming to project nodes into a low-dimensional …
A survey on heterogeneous network representation learning
Y Xie, B Yu, S Lv, C Zhang, G Wang, M Gong - Pattern recognition, 2021 - Elsevier
Heterogeneous information networks usually contain different kinds of nodes and
distinguishing types of relations, which can preserve more information than homogeneous …
distinguishing types of relations, which can preserve more information than homogeneous …
Hawk: Rapid android malware detection through heterogeneous graph attention networks
Android is undergoing unprecedented malicious threats daily, but the existing methods for
malware detection often fail to cope with evolving camouflage in malware. To address this …
malware detection often fail to cope with evolving camouflage in malware. To address this …
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Abstract Graph Neural Networks (GNNs) have achieved excellent performance of graph
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …
representation learning and attracted plenty of attentions in recent years. Most of GNNs aim …
Lime: Low-Cost and Incremental Learning for Dynamic Heterogeneous Information Networks
Understanding the interconnected relationships of large-scale information networks like
social, scholar and Internet of Things networks is vital for tasks like recommendation and …
social, scholar and Internet of Things networks is vital for tasks like recommendation and …