Network Embedding
153 papers with code • 0 benchmarks • 4 datasets
Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction
Benchmarks
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Libraries
Use these libraries to find Network Embedding models and implementations
arXiv Astro-Ph
Netzschleuder
Latest papers
H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks.
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News Detection
ds4ai-upb/danes
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The growing popularity of social media platforms has simplified the creation and distribution of news articles but also creates a conduit for spreading fake news.
Learning Semantic Relationship Among Instances for Image-Text Matching
CrossmodalGroup/HREM
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CVPR 2023
Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities.
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster Structure
Temporal networks are an important type of network whose topological structure changes over time.
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement Learning
Aiming at selecting a small subset of nodes with maximum influence on networks, the Influence Maximization (IM) problem has been extensively studied.
Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
Multiplex Heterogeneous Graph Convolutional Network
nsssjss/mhgcn
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Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification.
Online Knowledge Distillation via Mutual Contrastive Learning for Visual Recognition
winycg/mcl
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This enables each network to learn extra contrastive knowledge from others, leading to better feature representations, thus improving the performance of visual recognition tasks.
Unsupervised Network Embedding Beyond Homophily
Here, we formulate the unsupervised NE task as an r-ego network discrimination problem and develop the SELENE framework for learning on networks with homophily and heterophily.
Monkey Business: Reinforcement learning meets neighborhood search for Virtual Network Embedding
In this article, we consider the Virtual Network Embedding (VNE) problem for 5G networks slicing.
