arXiv AI August 11, 2026 By Killian Cressant, Pedro B. Velloso

LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN

arXiv:2608. 09596v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) suffer from two fundamental limitations: over-smoothing, where node representations become indistinguishable with depth, and over-squashing, where long-range information is compressed through limited message-passing channels.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 14

Distance-Preserving Embeddings in Inhomogeneous Random Graphs

arXiv:2607. 10074v1 Announce Type: new Abstract: Graph machine learning provides powerful tools for understanding complex networks and learning meaningful node representations.

By My Le, Luana Ruiz, Souvik Dhara

rag safety

arXiv AI

Jun 3

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs

arXiv:2606. 03068v1 Announce Type: cross Abstract: While Virtual Nodes (VNs) are often utilized in Message Passing Neural Networks (MPNNs) to facilitate effective message passing, existing VN-based methods have limitations, such as constraining all nodes to connect to the same number of VNs, fixing the connections before applying MPNNs, and connecting a node to a VN independently of the other nodes that connect to the same VN.

By Jaejun Lee, Joyce Jiyoung Whang

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv Machine Learning

Jul 7

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

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.

By Abdullah Shaik, Anwar Said

rag benchmarks

arXiv Machine Learning

1d ago

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

arXiv:2603. 06952v2 Announce Type: replace Abstract: As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods.

By Yuhang Song, Naima Abrar Shami, Romaric Duvignau, Vasiliki Kalavri

arXiv Machine Learning

Jun 2

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs

arXiv:2606. 01660v1 Announce Type: new Abstract: Pre-propagation graph neural networks (PPGNNs) push all graph-dependent computation into a preprocessing step and train only on the resulting dense hop features, which makes them highly scalable.

By Zichao Yue, Zhiru Zhang

diffusion benchmarks

Related stories

Distance-Preserving Embeddings in Inhomogeneous Random Graphs

Learn When and Where to Connect: Adaptive Virtual Nodes for Dynamic Message Passing on Graphs

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

Graph Classification via Network Usable Information: From Representation Evaluation to Structure Selection

Not All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines

Gate the Filter, Not the Message: Node-Channel Mixtures for Pre-Propagation GNNs