arXiv Machine Learning

Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs

The paper introduces G^2MLP, a graph‑free MLP trained via distillation from a GNN teacher while preserving the teacher’s graph‑induced geometry. It identifies two spectral failure modes—underfit on sparse graphs and overfit on dense graphs—caused by neglecting geometry during distillation. By using Ollivier‑Ricci curvature to guide supervision between prediction‑level and representation‑level alignment, G^2MLP improves node‑classification performance and reduces the teacher‑student rank gap across benchmarks.

arXiv Machine Learning

Oct 2

Model-to-Data Distillation for Graph Neural Networks

The paper introduces Model-to-Data (M2D) distillation, a method that transfers properties learned by a complex graph neural network (GNN) teacher into the graph data itself. By jointly learning augmented node features and graph structure, M2D encodes the teacher’s behavior, allowing simpler GNNs to recover high predictive performance, fairness, and robustness. The resulting graph can be reused with various downstream models, enabling them to approximate sophisticated teachers such as fairness-aware GNNs, Graph Attention Networks, and Graph Transformers.

By Debolina Halder Lina, Arlei Silva

llms efficiency safety

arXiv AI

Jul 21

DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation

arXiv:2604. 10882v2 Announce Type: replace-cross Abstract: Graph Neural Network pretraining is pivotal for leveraging unlabeled graph data.

By Yang Yan, Yunxuan Li, Qiuyan Wang, Tianjin Huang, Qiudong Yu

fine-tuning efficiency benchmarks

arXiv Machine Learning

Sep 30

The Universal Classifier for Graph Learning

arXiv:2609.36302v1 Announce Type: new Abstract: While foundation models have revolutionized natural language processing and computer vision by leveraging universal vocabularies, Graph Machine Learnin...

By Ben Finkelshtein, Andr\'{e} Linhares, Petar Veli\v{c}kovi\'{c}, Bryan Perozzi, Mikhail Galkin

diffusion computer-vision nlp

arXiv AI

Aug 6

NodeJEPA: Structure-Conditioned Latent Prediction for Node-Level Graph Self-Supervised Learning

arXiv:2608. 04381v1 Announce Type: cross Abstract: Self-supervised learning on graphs is largely shaped by contrastive methods that depend on carefully designed augmentations, and by generative methods that reconstruct node attributes in the input space.

By Tinghe Zhang, Jian Xu, Jiaheng Chen, Jiaxing Li, Yucheng Xiao, Qiang Wang

rag diffusion fine-tuning benchmarks

arXiv Machine Learning

Jun 5

The Post-GCN Decade Revisited: Curvature-Stratified Evaluation of Relational Learning

arXiv:2606. 06397v1 Announce Type: new Abstract: Current evaluation practices in relational learning rely heavily on flat leaderboards that average performance across heterogeneous datasets, implicitly assuming a uniform underlying structure.

By Shuo Wang, Xiangyu Wang, Quanxin Wang, Bailin Wu, Bokui Wang, Shunyang Huang, Boyan Deng, Haonan Liu, Ruiyi Fang, Zhenxiang Xu, Boyu Wang, Zhao Kang

benchmarks safety

Hugging Face Trending Papers

Jul 13

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods.

llms diffusion benchmarks safety

arXiv AI

Jun 11

GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).

By Weishuo Ma, Yanbo Wang, Xiyuan Wang, Lei Zou, Muhan Zhang

llms

arXiv Machine Learning

Jun 9

Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks

arXiv:2606. 08978v1 Announce Type: new Abstract: Hypergraph knowledge distillation aims to retain the predictive performance of a hypergraph neural network (HNN) teacher while reducing inference costs through a lightweight student model.

By Joohee Cho, David Yoon Suk Kang, Yunyong Ko

efficiency

arXiv Machine Learning

Jul 14

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.

By Chunyu Hu, Tianyin Liao, Ge Lan, Xingxuan Zhang, Jianxin Li, Peng Cui, Ziwei Zhang

llms diffusion benchmarks safety

Hugging Face Trending Papers

Sep 15

Repurposing Unified Topological Signatures for Graph Representation Learning

Message-passing Graph Neural Networks (GNNs) iteratively propagate and aggregate local neighborhood information followed by global readout to learn graph representations. However, their discriminative...

rag benchmarks

arXiv AI

Jun 24

Graph Alignment for Benchmarking Graph Neural Networks and Learning Positional Encodings

arXiv:2505. 13087v2 Announce Type: replace-cross Abstract: We propose a novel benchmarking methodology for graph neural networks (GNNs) based on the graph alignment problem, a combinatorial optimization task that generalizes graph isomorphism by aligning two unlabeled graphs to maximize overlapping edges.

By Adrien Lagesse, Marc Lelarge

llms rag benchmarks safety

arXiv Machine Learning

5d ago

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...

By Dario Vajda

llms fine-tuning benchmarks

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