arXiv:2606. 07475v1 Announce Type: cross Abstract: Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2607. 00671v1 Announce Type: cross Abstract: Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role.
arXiv:2601. 17469v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have shown remarkable capabilities in learning from graph-structured data with various applications such as social analysis and bioinformatics.
arXiv:2607. 03587v1 Announce Type: new Abstract: We propose NetinfoGC, a framework for graph classification that extends the Network Usable Information (NUI) paradigm to graph-level learning.
arXiv:2505. 21285v5 Announce Type: replace Abstract: This work proposes a framework LGKDE that learns kernel density estimation for graphs.
arXiv:2608. 14823v1 Announce Type: new Abstract: Are heterophilic nodes in a graph harder to classify because they are heterophilic or because they are rare?
arXiv:2607. 03097v1 Announce Type: new Abstract: Heterogeneous Graph Neural Networks (HGNNs) have exhibited remarkable efficacy in modeling complex systems with multiple types of nodes and relations, yet their training on large-scale heterogeneous graphs remains computationally prohibitive.