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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 201–250 of 403 papers

TitleStatusHype
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks—0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
PPPNE: Personalized proximity preserved network embedding—0
Predict Anchor Links across Social Networks via an Embedding Approach—0
Privacy Attacks on Network Embeddings—0
Progresses and Challenges in Link Prediction—0
QUINT: Node embedding using network hashing—0
Random Walks: A Review of Algorithms and Applications—0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement—0
Recommending on graphs: a comprehensive review from a data perspective—0
REFINE: Random RangE FInder for Network Embedding—0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
Representation Learning for Recommender Systems with Application to the Scientific Literature—0
Representation Learning for Scale-free Networks—0
Resource-Efficient Neural Architect—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Scalable attribute-aware network embedding with locality—0
Scalable Hierarchical Embeddings of Complex Networks—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
SepNE: Bringing Separability to Network Embedding—0
Signed Graph Diffusion Network—0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
Distributed Representations of Signed Networks—0
Simplicity within biological complexity—0
Simplifying complex machine learning by linearly separable network embedding spaces—0
Source-Aware Embedding Training on Heterogeneous Information Networks—0
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method—0
Space-Invariant Projection in Streaming Network Embedding—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
Stationary distribution of node2vec random walks on household models—0
Streaming Network Embedding through Local Actions—0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
Subgraph Networks with Application to Structural Feature Space Expansion—0
Subset-Contrastive Multi-Omics Network Embedding—0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks—0
Temporal Network Embedding via Tensor Factorization—0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding—0
Time-aware Gradient Attack on Dynamic Network Link Prediction—0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model—0
Toward Edge-Centric Network Embeddings—0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks—0
Tutorial on NLP-Inspired Network Embedding—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
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