Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Following a fast initial breakthrough in graph-based learning, Graph Neural Networks
(GNNs) have reached a widespread application in many science and engineering fields …

Graphs are typical non-Euclidean data of complex structures. Recently, Riemannian graph
representation learning emerges as an exciting alternative to the traditional Euclidean ones …

Graph clustering is a longstanding research topic, and has achieved remarkable success
with the deep learning methods in recent years. Nevertheless, we observe that several …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs,
and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined …

Abstract Recently proposed Graph Neural Networks (GNNs) for vertex clustering are trained
with an unsupervised minimum cut objective, approximated by a Spectral Clustering (SC) …

Higher-order graph clustering aims to partition the graph using frequently occurring
subgraphs (ie, motifs), instead of the lower-order edges, as the atomic clustering unit, which …

Graph domain adaptation models have become instrumental in addressing cross-network
learning problems due to their ability to transfer abundant label and structural knowledge …

Multi-view graph pooling utilizes information from multiple perspectives to generate a
coarsened graph, exhibiting superior performance in graph-level tasks. However, existing …