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Network Embedding

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

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 1–50 of 403 papers

TitleStatusHype
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Subset-Contrastive Multi-Omics Network Embedding—0
Network Alignment—0
Network Embedding Exploration Tool (NEExT)Code0
ASD Classification on Dynamic Brain Connectome using Temporal Random Walk with Transformer-based Dynamic Network EmbeddingCode0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees—0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Stationary distribution of node2vec random walks on household models—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
A novel robust integrating method by high-order proximity for self-supervised attribute network embeddingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Just Propagate: Unifying Matrix Factorization, Network Embedding, and LightGCN for Link Prediction—0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
Simplifying complex machine learning by linearly separable network embedding spaces—0
Efficient Network Embedding by Approximate Equitable PartitionsCode0
A Survey on Signed Graph Embedding: Methods and Applications—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
BlueTempNet: A Temporal Multi-network Dataset of Social Interactions in Bluesky SocialCode0
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement LearningCode2
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
MS-IMAP -- A Multi-Scale Graph Embedding Approach for Interpretable Manifold Learning—0
Simplicity within biological complexity—0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding—0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
Detecting local perturbations of networks in a latent hyperbolic embedding space—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Hedging carbon risk with a network approach—0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Trustworthiness-Driven Graph Convolutional Networks for Signed Network EmbeddingCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Network Embedding Using Sparse Approximations of Random Walks—0
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors—0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Source-Aware Embedding Training on Heterogeneous Information Networks—0
Random Walk on Multiple NetworksCode1
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Graph-Level Embedding for Time-Evolving Graphs—0
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
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