Graph neural networks have been extensively studied for learning with inter-connected data.
Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing …

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so
that research on out-of-distribution (OOD) generalization comes into the spotlight …

Learning on graphs, where instance nodes are inter-connected, has become one of the
central problems for deep learning, as relational structures are pervasive and induce data …

We study a new paradigm of knowledge transfer that aims at encoding graph topological
information into graph neural networks (GNNs) by distilling knowledge from a teacher GNN …

Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …

Graph structure learning is a well-established problem that aims at optimizing graph
structures adaptive to specific graph datasets to help message passing neural networks (ie …

Prediction over tabular data is an essential and fundamental problem in many important
downstream tasks. However, existing methods either take a data instance of the table …

Data science is concerned with mining data patterns from a database, which is assembled
by tabular data. As the routine of machine learning, most of the previous work mining the …

Cross-Domain Sequential Recommendation (CDSR) methods aim to tackle the data sparsity
and cold-start problems present in Single-Domain Sequential Recommendation (SDSR) …

Graph neural networks (GNNs), in general, are built on the assumption of a static set of
features characterizing each node in a graph. This assumption is often violated in practice …