Community detection, a fundamental task for network analysis, aims to partition a network
into multiple sub-structures to help reveal their latent functions. Community detection has …

Data-centric AI, with its primary focus on the collection, management, and utilization of data
to drive AI models and applications, has attracted increasing attention in recent years. In this …

Graph neural networks, a powerful deep learning tool to model graph-structured data, have
demonstrated remarkable performance on numerous graph learning tasks. To address the …

Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …

Abstract Graph Convolutional Networks (GCNs), aiming to obtain the representation of a
node by aggregating its neighbors, have demonstrated great power in tackling various …

Abstract Graph Convolutional Networks (GCNs) have been widely applied in various fields
due to their significant power on processing graph-structured data. Typical GCN and its …

Abstract Knowledge graph (KG) embeddings have shown great power in learning
representations of entities and relations for link prediction tasks. Previous work usually …

Heterogeneous graph learning aims to capture complex relationships and diverse relational
semantics among entities in a heterogeneous graph to obtain meaningful representations …

Abstract Graph Convolutional Network (GCN) has shown remarkable potential of exploring
graph representation. However, the GCN aggregating mechanism fails to generalize to …

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 …