Social network analysis is an important problem in data mining. A fundamental step for
analyzing social networks is to encode network data into low-dimensional representations …

Graph Convolutional Networks (GCNs) have gained great popularity in tackling various
analytics tasks on graph and network data. However, some recent studies raise concerns …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Networks have been widely used to represent the relations between objects such as
academic networks and social networks, and learning embedding for networks has thus …

Abstract Graph Contrastive Learning (GCL), learning the node representations by
augmenting graphs, has attracted considerable attentions. Despite the proliferation of …

Nodes in a multiplex network are connected by multiple types of relations. However, most
existing network embedding methods assume that only a single type of relation exists …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Attributed networks are ubiquitous in the real world, such as social networks. Therefore,
many researchers take the node attributes into consideration in the network representation …

Abstract Graph Contrastive Learning (GCL) has emerged as a popular training approach for
learning node embeddings from augmented graphs without labels. Despite the key principle …

Effectively detecting anomalous nodes in attributed networks is crucial for the success of
many real-world applications such as fraud and intrusion detection. Existing approaches …