arXiv Machine Learning

Efficient Recommendations via Graph Coarsening and Label Propagation

arXiv:2607. 22287v1 Announce Type: new Abstract: Graph-based recommendations are widely adopted in real-world industrial applications.

arXiv AI

Jul 2

Multi-Label Node Classification with Label Influence Propagation

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.

By Yifei Sun, Zemin Liu, Bryan Hooi, Yang Yang, Rizal Fathony, Jia Chen, Bingsheng He

rag benchmarks

arXiv AI

Jul 29

TRWH: A Text-Driven Random Walk Heterogeneous GNN for Semantic-Aware Sparse Recommendation

arXiv:2607. 25471v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively.

By He Ma, Chen Liu

llms rag benchmarks

arXiv Machine Learning

Jun 10

When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice

arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.

By Neha Sharma, Ritesh Sharma

benchmarks

arXiv AI

Jun 9

GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation

arXiv:2606. 07526v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong potential for recommendation (LLMRec) due to their powerful reasoning and generalization abilities.

By Lin Mu, Guoji Wang, Li Ni, Lei Sang, Zhize Wu, Peiquan Jin, Yiwen Zhang

llms rag benchmarks

arXiv AI

Jul 28

TextBridgeGNN: Pre-training Graph Neural Network for Cross-Domain Recommendation via Text-Guided Transfer

arXiv:2601. 02366v3 Announce Type: replace-cross Abstract: Graph-based recommendation has achieved great success in recent years.

By Yiwen Chen, Yiqing Wu, Huishi Luo, Fuzhen Zhuang, Deqing Wang, Zhao Zhang

llms rag fine-tuning

arXiv Machine Learning

Jun 30

GLIP: Graph and LLM Joint Pretraining for Graph-Level Tasks

arXiv:2606. 29773v1 Announce Type: new Abstract: Graphs are widely used to model relational systems, with applications in domains such as social networks, finance, and biomedicine.

By Haoxin Sun, Yiqing Lin, Yajun Huang, Chenhui Dong, Mingjun Li, Zhongzhi Zhang

llms diffusion fine-tuning benchmarks safety

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

Jul 30

ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

arXiv:2507. 14484v2 Announce Type: replace Abstract: In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks.

By Yule Li, Yifeng Lu, Zhen Wang, Zhewei Wei, Yaliang Li, Bolin Ding

diffusion computer-vision benchmarks

arXiv Machine Learning

Aug 4

CoRe-GNN: Multilevel Message passing on Coarsened graphs

arXiv:2608. 02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers.

By Antonin Joly, Nicolas Keriven, Aline Roumy

benchmarks

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

Jun 18

Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion

arXiv:2606. 18317v1 Announce Type: new Abstract: Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors.

By Xuling Zhang, Peng Wang, Daiyan Li, Aoran Huang, Zeiwei Chen, Yongkui Yang

diffusion benchmarks

Hugging Face Trending Papers

Aug 3

CoRe-GNN: Multilevel Message passing on Coarsened graphs

Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified perspective that exposes their respective limitations.

benchmarks

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