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 …

Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Text classification is a critical research topic with broad applications in natural language
processing. Recently, graph neural networks (GNNs) have received increasing attention in …

Multimodal learning (MML) aims to jointly exploit the common priors of different modalities to
compensate for their inherent limitations. However, existing MML methods often optimize a …

Network anomaly detection, also known as graph anomaly detection, aims to find network
elements (eg, nodes, edges, subgraphs) with significantly different behaviors from the vast …

Few-shot Node Classification (FSNC) is a challenge in graph representation learning where
only a few labeled nodes per class are available for training. To tackle this issue, meta …

Few-shot learning (FSL) aims to learn a classifier that can be easily adapted to
accommodate new tasks, given only a few examples. To handle the limited-data in few-shot …

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic
flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some …

Supervised machine learning has several drawbacks that make it difficult to use in many
situations. Drawbacks include heavy reliance on massive training data, limited …

The ability to incrementally learn new classes is vital to all real-world artificial intelligence
systems. A large portion of high-impact applications like social media, recommendation …