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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 226–250 of 403 papers

TitleStatusHype
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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