Clustering is a fundamental machine learning task, which aim at assigning instances into
groups so that similar samples belong to the same cluster while dissimilar samples belong …

Link prediction aims at inferring missing links or predicting future ones based on the
currently observed network. This topic is important for many applications such as social …

Abstract Knowledge Distillation (KD) aims at transferring knowledge from a larger well-
optimized teacher network to a smaller learnable student network. Existing KD methods …

Generative graph self-supervised learning (SSL) aims to learn node representations by
reconstructing the input graph data. However, most existing methods focus on unsupervised …

Learning low-dimensional representations for Heterogeneous Information Networks (HINs)
has drawn increasing attention recently for its effectiveness in real-world applications …

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing
graph neural networks (GNNs) have shown impressive results. Despite the success of …

Sampling strategies have been widely applied in many recommendation systems to
accelerate model learning from implicit feedback data. A typical strategy is to draw negative …

User recommendation aims at recommending users with potential interests in the social
network. Previous works have mainly focused on the undirected social networks with …

Graph embedding aims to encode nodes/edges into low-dimensional continuous features,
and has become a crucial tool for graph analysis including graph/node classification, link …

Graph autoencoders (GAEs), as a kind of generative self-supervised learning approach,
have shown great potential in recent years. GAEs typically rely on distance-based criteria …