Image de couverture de livre

Random Walk Diffusion for Efficient Large-Scale Graph Generation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Bernecker, Tobias, Rehawi, Ghalia, Casale, Francesco Paolo, Knauer-Arloth, Janine, Marsico, Annalisa
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Social and Information Networks
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Hub-aware Random Walk Graph Embedding Methods for Classification
par: Tomčić, Aleksandar, et autres
Publié: (2022)

Efficient Heterogeneous Graph Learning via Random Projection
par: Hu, Jun, et autres
Publié: (2023)

Unifying Generation and Prediction on Graphs with Latent Graph Diffusion
par: Zhou, Cai, et autres
Publié: (2024)

Leveraging Non-linear Dimension Reduction and Random Walk Co-occurrence for Node Embedding
par: DeWolfe, Ryan
Publié: (2026)

Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
par: Evdaimon, Iakovos, et autres
Publié: (2024)

Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation
par: Qin, Meng, et autres
Publié: (2025)

Large Scale Community-Aware Network Generation
par: Ramavarapu, Vikram, et autres
Publié: (2025)

Efficient Learning on Large Graphs using a Densifying Regularity Lemma
par: Kouchly, Jonathan, et autres
Publié: (2025)

When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
par: Lin, Junhong, et autres
Publié: (2024)

Efficient and Scalable Graph Generation through Iterative Local Expansion
par: Bergmeister, Andreas, et autres
Publié: (2023)

Frustrated Random Walks: A Fast Method to Compute Node Distances on Hypergraphs
par: Li, Enzhi, et autres
Publié: (2024)

Density-aware Walks for Coordinated Campaign Detection
par: Gopalakrishnan, Atul Anand, et autres
Publié: (2025)

Strong and Weak Random Walks on Signed Networks
par: Babul, Shazia'Ayn, et autres
Publié: (2024)

Link Prediction with Physics-Inspired Graph Neural Networks
par: Di Francesco, Andrea Giuseppe, et autres
Publié: (2024)

What Do Temporal Graph Learning Models Learn?
par: Hayes, Abigail J., et autres
Publié: (2025)

Faster Local Solvers for Graph Diffusion Equations
par: Bai, Jiahe, et autres
Publié: (2024)

Cascade-based Randomization for Inferring Causal Effects under Diffusion Interference
par: Fatemi, Zahra, et autres
Publié: (2024)

The Effects of Randomness on the Stability of Node Embeddings
par: Schumacher, Tobias, et autres
Publié: (2020)

Diffusion-based Negative Sampling on Graphs for Link Prediction
par: Nguyen, Trung-Kien, et autres
Publié: (2024)

Scalable Deep Graph Clustering with Random-walk based Self-supervised Learning
par: Li, Xiang, et autres
Publié: (2021)

Disentangled Condensation for Large-scale Graphs
par: Xiao, Zhenbang, et autres
Publié: (2024)

Counterfactual Data Augmentation with Denoising Diffusion for Graph Anomaly Detection
par: Xiao, Chunjing, et autres
Publié: (2024)

Reconstructing Graph Diffusion History from a Single Snapshot
par: Qiu, Ruizhong, et autres
Publié: (2023)

Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning
par: Lu, Qincheng, et autres
Publié: (2024)

Learning on Large Graphs using Intersecting Communities
par: Finkelshtein, Ben, et autres
Publié: (2024)

Effective Clustering on Large Attributed Bipartite Graphs
par: Yang, Renchi, et autres
Publié: (2024)

NetDiff: Deep Graph Denoising Diffusion for Ad Hoc Network Topology Generation
par: Marcoccia, Félix, et autres
Publié: (2024)

An Efficient Loop and Clique Coarsening Algorithm for Graph Classification
par: Qi, Xiaorui, et autres
Publié: (2024)

TGTOD: A Global Temporal Graph Transformer for Outlier Detection at Scale
par: Liu, Kay, et autres
Publié: (2024)

Incorporating Attributes and Multi-Scale Structures for Heterogeneous Graph Contrastive Learning
par: Jiang, Ruobing, et autres
Publié: (2025)

InfraredGP: Efficient Graph Partitioning via Spectral Graph Neural Networks with Negative Corrections
par: Qin, Meng, et autres
Publié: (2025)

Overcoming Order in Autoregressive Graph Generation
par: Cohen-Karlik, Edo, et autres
Publié: (2024)

Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models
par: Liu, Kay, et autres
Publié: (2023)

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
par: Hu, Jun, et autres
Publié: (2022)

LEGO-Learn: Label-Efficient Graph Open-Set Learning
par: Xu, Haoyan, et autres
Publié: (2024)

Efficient and Privacy-Preserved Link Prediction via Condensed Graphs
par: Long, Yunbo, et autres
Publié: (2025)

Efficient Estimation of Shortest-Path Distance Distributions to Samples in Graphs
par: Zhu, Alan, et autres
Publié: (2025)

Generalizing Graph Transformers Across Diverse Graphs and Tasks via Pre-training
par: He, Yufei, et autres
Publié: (2024)

GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction
par: Wang, Zixiao, et autres
Publié: (2022)

HiGen: Hierarchical Graph Generative Networks
par: Karami, Mahdi
Publié: (2023)