6 accepted papers
Hypergraph neural networks (HNNs) have emerged as powerful tools for modeling high-order relationships in complex systems. However, most existing HNNs are designed under the assumption of homophily, which does not hold in many real-world scenarios where connected nodes often exhibit diverse semantic
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for graph classification. Specifically, most existing GNNs mainly rely on the message passing strategy between neighbor nodes, where the expressivity is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Although a number of k
Session-based recommendation aims to predict users’ next actions by modeling their ongoing interaction sequences, particularly in scenarios where long-term user profiles are unavailable. While existing methods have achieved promising results by leveraging sequential and graph-based structures, they
Hyperedge prediction plays a central role in hypergraph learning, enabling the inference of high-order relations among multiple entities. However, existing methods often rely on a simplistic flat set assumption, treating candidate hyperedges as unstructured collections of nodes and neglecting their
In this paper, we propose training very deep neural networks (DNNs) for supervised learning of hash codes. Existing methods in this context train relatively "shallow" networks limited by the issues arising in back propagation (e.g. vanishing gradients) as well as computational efficiency. We propose…
The group membership prediction (GMP) problem involves predicting whether or not a collection of instances share a certain semantic property. For instance, in kinship verification given a collection of images, the goal is to predict whether or not they share a familial relationship. In this context…