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 …
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 …
Venue link detection for social media messages
A method associates social media messages with venues. A social network graph includes
users, messages from users, and venues. The venues include multiple primary venues and …
users, messages from users, and venues. The venues include multiple primary venues and …
Embedding of embedding (EOE) joint embedding for coupled heterogeneous networks
Network embedding is increasingly employed to assist network analysis as it is effective to
learn latent features that encode linkage information. Various network embedding methods …
learn latent features that encode linkage information. Various network embedding methods …
A link prediction method for heterogeneous networks based on BP neural network
Most real-world systems, composed of different types of objects connected via many
interconnections, can be abstracted as various complex heterogeneous networks. Link …
interconnections, can be abstracted as various complex heterogeneous networks. Link …
Mining user interests over active topics on social networks
Inferring users' interests from their activities on social networks has been an emerging
research topic in the recent years. Most existing approaches heavily rely on the explicit …
research topic in the recent years. Most existing approaches heavily rely on the explicit …
Collective prediction of disease-associated miRNAs based on transduction learning
The discovery of human disease-related miRNA isa challenging problem for complex
disease biology research. For existing computational methods, it is difficult to achieve …
disease biology research. For existing computational methods, it is difficult to achieve …
Reinforcement learning based meta-path discovery in large-scale heterogeneous information networks
Meta-paths are important tools for a wide variety of data mining and network analysis tasks
in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to …
in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to …
Machine-learning based anomaly detection for heterogenous data sources
Embodiments of an automated anomaly detection system are disclosed that can detect
anomalous data from heterogeneous data sources. The anomaly detection system can …
anomalous data from heterogeneous data sources. The anomaly detection system can …