Anomalies are rare observations (eg, data records or events) that deviate significantly from
the others in the sample. Over the past few decades, research on anomaly mining has …

Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Previous multi-view spectral clustering methods are a two-step strategy, which first learns a
fixed common representation (or common affinity matrix) of all the views from original data …

Feature extraction becomes increasingly important as data grows high dimensional.
Autoencoder as a neural network based feature extraction method achieves great success …

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes
that capture various relationships in a multi-view network, where each view represents a …

Graph neural networks (GNNs) are now the mainstream method for mining graph-structured
data and learning low-dimensional node-and graph-level embeddings to serve downstream …

Real-life objects typically contain complex structures, and the graph is a prevalent
presentation for describing such objects. Multi-graph multi-label (MGML) learning is a …

Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To
date, all existing MTL methods have been designed for tasks with feature-vector represented …

Graphs have a superior ability to represent relational data, such as chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Link prediction is a demanding task in real-world scenarios, such as recommender systems,
which targets to predict the unobservable links between different objects by learning network …