Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Networks represent relationships between entities in many complex systems, spanning from
online social interactions to biological cell development and brain connectivity. In many …

Video anomaly detection under weak labels is formulated as a typical multiple-instance
learning problem in previous works. In this paper, we provide a new perspective, ie, a …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Attributed networks are ubiquitous and form a critical component of modern information
infrastructure, where additional node attributes complement the raw network structure in …

Network embedding leverages the node proximity manifested to learn a low-dimensional
node vector representation for each node in the network. The learned embeddings could …

Given the prevalence of large-scale graphs in real-world applications, the storage and time
for training neural models have raised increasing concerns. To alleviate the concerns, we …

One of the greatest advancements of the modern era is the evolution of smart grid (SG),
which integrates information communication technologies with advanced power electronic …

Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …

Due to the expensive cost of data annotation, few-shot learning has attracted increasing
research interests in recent years. Various meta-learning approaches have been proposed …