The proliferation of fake news on social media has the probability to bring an unfavorable
impact on public opinion and social development. Many efforts have been paid to develop …

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 …

Graphs are used to model pairwise relations between entities in many real-world scenarios
such as social networks. Graph Neural Networks (GNNs) have shown their superior ability in …

The widespread of fake news on social media and other platforms can bring significant
damage to the harmony and stability of our society. To defend against fake news …

Network embedding has gained more attentions in recent years. It has been shown that the
learned low-dimensional node vector representations could advance a myriad of graph …

Link prediction targets to predict the future node interactions mainly based on the current
network snapshot. It is a key step in understanding the formation and evolution of the …

Abstract Deep Neural Network (DNN) has made great leaps in image classification and
speech recognition in recent years. However, employing DNN for node classification such …

The adversarial attack methods based on gradient information can adequately find the
perturbations, that is, the combinations of rewired links, thereby reducing the effectiveness of …

Network representation learning, as an approach to learn low dimensional representations
of vertices, has attracted considerable research attention recently. It has been proven …

Vital node detection in a social graph is a long-standing challenge for understanding the
exchange, sharing, and dissemination of information on social media. As one of the most …