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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

Showing 201250 of 403 papers

TitleStatusHype
Pairwise FastText Classifier for Entity Disambiguation0
PathRank: A Multi-Task Learning Framework to Rank Paths in Spatial Networks0
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models0
PPPNE: Personalized proximity preserved network embedding0
Predict Anchor Links across Social Networks via an Embedding Approach0
Privacy Attacks on Network Embeddings0
Progresses and Challenges in Link Prediction0
QUINT: Node embedding using network hashing0
Random Walks: A Review of Algorithms and Applications0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement0
Recommending on graphs: a comprehensive review from a data perspective0
REFINE: Random RangE FInder for Network Embedding0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding0
Relation Structure-Aware Heterogeneous Information Network Embedding0
Representation Learning for Recommender Systems with Application to the Scientific Literature0
Representation Learning for Scale-free Networks0
Resource-Efficient Neural Architect0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data0
RNE: A Scalable Network Embedding for Billion-scale Recommendation0
Scalable attribute-aware network embedding with locality0
Scalable Hierarchical Embeddings of Complex Networks0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs0
Semi-supervised Network Embedding with Differentiable Deep Quantisation0
SepNE: Bringing Separability to Network Embedding0
Signed Graph Diffusion Network0
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies0
Distributed Representations of Signed Networks0
Simplicity within biological complexity0
Simplifying complex machine learning by linearly separable network embedding spaces0
Source-Aware Embedding Training on Heterogeneous Information Networks0
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method0
Space-Invariant Projection in Streaming Network Embedding0
Stationary distribution of node2vec random walks on household models0
Streaming Network Embedding through Local Actions0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding0
Subgraph Networks with Application to Structural Feature Space Expansion0
Subset-Contrastive Multi-Omics Network Embedding0
Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks0
Temporal Network Embedding via Tensor Factorization0
Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding0
Time-aware Gradient Attack on Dynamic Network Link Prediction0
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model0
Toward Edge-Centric Network Embeddings0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks0
Tutorial on NLP-Inspired Network Embedding0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective0
Unifying Homophily and Heterophily Network Transformation via Motifs0
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