Recently Graph Neural Networks (GNNs) have achieved great success in many
applications. The mini-batch training has become the de-facto way to train GNNs on giant …

Graph neural networks have been widely used to learn node representations for many real-
world static graphs. In general, they learn node representations by recursively aggregating …

Recent studies have demonstrated that recommender systems (RecSys) are vulnerable to
injective attacks. Given a limited fake user budget, attackers can inject fake users with …

Fairness-aware graph neural networks (GNNs) have gained a surge of attention as they can
reduce the bias of predictions on any demographic group (eg, female) in graph-based …

Recently, the message passing neural network (MPNN) has attracted a lot of attention,
which learns node representations based on the receptive field of the given node. Despite …

Recommender systems are vulnerable to injective attacks, which inject limited fake users
into the platforms to manipulate the exposure of target items to all users. In this work, we …

Recently, many promising embedding models have been proposed to embed knowledge
graphs (KGs) and their more general forms, such as n-ary relational data (NRD) and hyper …

Graph self-supervised learning aiming to learn the graph representation without much label
information is an important tasks in data mining and machine learning since labeled graph …

Recently, graph contrastive learning proposes to learn node representations from the
unlabeled graph to alleviate the heavy reliance on node labels in graph neural networks …

Graph neural networks (GNNs) have achieved great success on various graph tasks.
However, recent studies have revealed that GNNs are vulnerable to injective attacks. Due to …