Graph Neural Networks

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, …

Graph Learning: A Survey

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a wide spectrum of various domains such as social systems, ecosystems, biological networks, knowledge graphs, and information …

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown …

Learning Graph Neural Networks with Positive and Unlabeled Nodes

Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes. To enable graph neural network learning, …

Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning

Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. As one of the most popular graph-based SSL approaches, the recently proposed Graph Convolutional …

Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement

Recent works on One-Shot Neural Architecture Search (NAS) mostly adopt a bilevel optimization scheme to alternatively optimize the supernet weights and architecture parameters after relaxing the discrete search space into a differentiable space. …

Graph Geometry Interaction Learning

While numerous approaches have been developed to embed graphs into either Euclidean or hyperbolic spaces, they do not fully utilize the information available in graphs, or lack the flexibility to model intrinsic complex graph geometry. To utilize the …

Graph Stochastic Neural Networks for Semi-supervised Learning

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification. However, most existing models learn a deterministic classification function, which lack sufficient flexibility to explore better …

Cross-Graph: Robust and Unsupervised Embedding for Attributed Graphs with Corrupted Structure

Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structure and content information in the …

OpenWGL: Open-World Graph Learning

In traditional graph learning tasks, such as node classification, the learning is carried out in a closed-world setting where the number of classes and their training samples are provided to help train models, and the learning goal is to correctly …