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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 251–300 of 403 papers

TitleStatusHype
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
Zoo Guide to Network Embedding—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Learning Features of Network Structures Using Graphlets—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph-Level Embedding for Time-Evolving Graphs—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Heterogeneous Edge Embeddings for Friend Recommendation—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
HONEM: Learning Embedding for Higher Order Networks—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
Hyperbolic Node Embedding for Signed Networks—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Flexible Attributed Network EmbeddingCode0
Collaborative Graph Neural Networks for Attributed Network EmbeddingCode0
Font Size: Community Preserving Network EmbeddingCode0
Adversarial network embedding with bootstrapped representations for sparse networksCode0
Representation Learning on Heterostructures via Heterogeneous Anonymous WalksCode0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Unsupervised Attributed Multiplex Network EmbeddingCode0
CANE: Context-Aware Network Embedding for Relation ModelingCode0
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