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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
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
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
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees—0
User-based Network Embedding for Collective Opinion Spammer Detection—0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
Vertex-Context Sampling for Weighted Network Embedding—0
Video Tracking Using Learned Hierarchical Features—0
VNE Strategy based on Chaotic Hybrid Flower Pollination Algorithm Considering Multi-criteria Decision Making—0
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors—0
WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows—0
weg2vec: Event embedding for temporal networks—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Zoo Guide to Network Embedding—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
A Block-based Generative Model for Attributed Networks Embedding—0
ActiveHNE: Active Heterogeneous Network Embedding—0
Adversarial Attacks on Deep Graph Matching—0
Adversarial Network Embedding—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
A General Framework for Content-enhanced Network Representation Learning—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Aligning context-based statistical models of language with brain activity during reading—0
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