Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Spiking neural networks (SNNs) have garnered increased attention in the field of artificial
general intelligence (AGI) research due to their low power consumption, high computational …

Data augmentation has recently seen increased interest in graph machine learning given its
demonstrated ability to improve model performance and generalization by added training …

Numerous deep learning algorithms have been inspired by and understood via the notion of
information bottleneck, where unnecessary information is (often implicitly) minimized while …

Graph convolution networks (GCNs) for recommendations have emerged as an important
research topic due to their ability to exploit higher-order neighbors. Despite their success …

Recently, multivariate time series (MTS) forecasting techniques have seen rapid
development and widespread applications across various fields. Transformer-based and …

Existing research on multimodal relation extraction (MRE) faces two co-existing challenges,
internal-information over-utilization and external-information under-exploitation. To combat …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …