Copertina

Hub-aware Random Walk Graph Embedding Methods for Classification

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Tomčić, Aleksandar, Savić, Miloš, Radovanović, Miloš
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Machine Learning Social and Information Networks
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Dynamic Graph Embedding Through Hub-aware Random Walks
di: Tomčić, Aleksandar, et al.
Pubblicazione: (2025)

Hub‐aware random walk graph embedding methods for classification
di: Aleksandar Tomčić, et al.
Pubblicazione: (2024)

Local Intrinsic Dimensionality for Dynamic Graph Embeddings
di: Knežević, Dušica, et al.
Pubblicazione: (2024)

Random Walk Diffusion for Efficient Large-Scale Graph Generation
di: Bernecker, Tobias, et al.
Pubblicazione: (2024)

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

Density-aware Walks for Coordinated Campaign Detection
di: Gopalakrishnan, Atul Anand, et al.
Pubblicazione: (2025)

Encoder Embedding for General Graph and Node Classification
di: Shen, Cencheng
Pubblicazione: (2024)

GuideWalk: A Novel Graph-Based Word Embedding for Enhanced Text Classification
di: Mohammed, Sarmad N., et al.
Pubblicazione: (2024)

A Survey on Signed Graph Embedding: Methods and Applications
di: Ghosh, Shrabani
Pubblicazione: (2024)

Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
di: Li, Yicong, et al.
Pubblicazione: (2024)

Frustrated Random Walks: A Fast Method to Compute Node Distances on Hypergraphs
di: Li, Enzhi, et al.
Pubblicazione: (2024)

Efficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation
di: Qin, Meng, et al.
Pubblicazione: (2025)

The Effects of Randomness on the Stability of Node Embeddings
di: Schumacher, Tobias, et al.
Pubblicazione: (2020)

Node Classification in Random Trees
di: Nuijten, Wouter W. L., et al.
Pubblicazione: (2023)

Recurrent Attention Walk for Semi-supervised Classification
di: Akujuobi, Uchenna, et al.
Pubblicazione: (2019)

Homophily-aware Heterogeneous Graph Contrastive Learning
di: Wang, Haosen, et al.
Pubblicazione: (2025)

Self-attention Dual Embedding for Graphs with Heterophily
di: Lai, Yurui, et al.
Pubblicazione: (2023)

Synergistic Graph Fusion via Encoder Embedding
di: Shen, Cencheng, et al.
Pubblicazione: (2023)

Aitchison Embeddings for Learning Compositional Graph Representations
di: Nakis, Nikolaos, et al.
Pubblicazione: (2026)

Incorporating Heterophily into Graph Neural Networks for Graph Classification
di: Yang, Jiayi, et al.
Pubblicazione: (2022)

Principal Graph Encoder Embedding and Principal Community Detection
di: Shen, Cencheng, et al.
Pubblicazione: (2025)

Temporal Link Prediction Using Graph Embedding Dynamics
di: Fard, Sanaz Hasanzadeh, et al.
Pubblicazione: (2024)

Epidemiology-informed Graph Neural Network for Heterogeneity-aware Epidemic Forecasting
di: Zheng, Yufan, et al.
Pubblicazione: (2024)

The Graph-Embedded Hazard Model (GEHM): Stochastic Network Survival Dynamics on Economic Graphs
di: Vallarino, Diego
Pubblicazione: (2025)

Learning Graph Filters for Structure-Function Coupling based Hub Node Identification
di: Ortiz-Bouza, Meiby, et al.
Pubblicazione: (2024)

GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs
di: Chatterjee, Shraban Kumar, et al.
Pubblicazione: (2024)

Graph Encoder Ensemble for Simultaneous Vertex Embedding and Community Detection
di: Shen, Cencheng, et al.
Pubblicazione: (2023)

Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
di: Nakis, Nikolaos, et al.
Pubblicazione: (2024)

Fully-inductive Node Classification on Arbitrary Graphs
di: Zhao, Jianan, et al.
Pubblicazione: (2024)

Disambiguated Node Classification with Graph Neural Networks
di: Zhao, Tianxiang, et al.
Pubblicazione: (2024)

GraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
di: Zhu, Chaofan, et al.
Pubblicazione: (2025)

Efficient Heterogeneous Graph Learning via Random Projection
di: Hu, Jun, et al.
Pubblicazione: (2023)

DuoGNN: Topology-aware Graph Neural Network with Homophily and Heterophily Interaction-Decoupling
di: Mancini, K., et al.
Pubblicazione: (2024)

Structure-Preference Enabled Graph Embedding Generation under Differential Privacy
di: Zhang, Sen, et al.
Pubblicazione: (2025)

A Probabilistic Model for Node Classification in Directed Graphs
di: Huerta, Diego, et al.
Pubblicazione: (2025)

Edge Classification on Graphs: New Directions in Topological Imbalance
di: Cheng, Xueqi, et al.
Pubblicazione: (2024)

An Efficient Loop and Clique Coarsening Algorithm for Graph Classification
di: Qi, Xiaorui, et al.
Pubblicazione: (2024)

Strong and Weak Random Walks on Signed Networks
di: Babul, Shazia'Ayn, et al.
Pubblicazione: (2024)

Refined Graph Encoder Embedding via Self-Training and Latent Community Recovery
di: Shen, Cencheng, et al.
Pubblicazione: (2024)

Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding
di: Jiang, Mengying, et al.
Pubblicazione: (2024)