In the past few years, the emergence of pre-training models has brought uni-modal fields
such as computer vision (CV) and natural language processing (NLP) to a new era …

Graph neural networks generalize conventional neural networks to graph-structured data
and have received widespread attention due to their impressive representation ability. In …

In recent years, the size of pre-trained language models (PLMs) has grown by leaps and
bounds. However, efficiency issues of these large-scale PLMs limit their utilization in real …

Continual graph learning routinely finds its role in a variety of real-world applications where
the graph data with different tasks come sequentially. Despite the success of prior works, it …

Recent methods in geometric deep learning have introduced various neural networks to
operate over data that lie on Riemannian manifolds. Such networks are often necessary to …

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

Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry
due to its superior capability for representing hierarchies. The topological structures of real …

This paper introduces an end-to-end residual network that operates entirely on the Poincare
ball model of hyperbolic space. Hyperbolic learning has recently shown great potential for …

Temporal networks arise in various real-world scenarios, including social networks, user-
item networks, traffic networks, financial transaction networks, etc. Modeling the dynamics of …

The sequential interaction network usually find itself in a variety of applications, eg,
recommender system. Herein, inferring future interaction is of fundamental importance, and …