Deep Evidential Regression (DER) places a prior on the original Gaussian likelihood
function and treats learning as an evidence acquisition process to quantify uncertainty by …

Temporal attributed network embedding aspires to learn a low-dimensional vector
representation for each node in each snapshot of a temporal network, which can be capable …

For graph self-supervised learning (GSSL), masked autoencoder (MAE) follows the
generative paradigm and learns to reconstruct masked graph edges or node features while …

The rich information underlying graphs has inspired further investigation of unsupervised
graph representation. Existing studies mainly depend on node features and topological …

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 …

Contrastive learning, a powerful self-supervised learning paradigm, has shown its efficacy in
learning embed dings from independent and identically distributed (IID) as well as non-IID …

Temporal graph neural networks (T-GNNs) have emerged as leading approaches for
representation learning over dynamic graphs. However, existing solutions typically suffer …

Graph Neural Networks (GNNs) have emerged as an effective tool for graph classification,
yet their reliance on extensive labeled data poses a significant challenge, especially when …

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks
imposed by malicious activities. However, identifying anomalies in these dynamic graphs …

Dynamic community detection, which focuses on tracking local topological variation with
time, is crucial for understanding the changing affiliations of nodes to communities in …