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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 151–200 of 403 papers

TitleStatusHype
Neurally boosted supervised spectral clustering—0
Network representation learning systematic review: ancestors and current development state—0
QUINT: Node embedding using network hashing—0
REFINE: Random RangE FInder for Network Embedding—0
Temporal Network Embedding via Tensor Factorization—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
Temporal Graph Network Embedding with Causal Anonymous Walks RepresentationsCode0
Deep Contrastive Multiview Network Embedding—0
TextCNN with Attention for Text ClassificationCode0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Large-Scale Network Embedding in Apache Spark—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Relation order histograms as a network embedding toolCode0
GENE: Global Event Network EmbeddingCode0
Network embedding unveils the hidden interactions in the mammalian virome—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender SystemsCode0
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
Hierarchical Graph Neural Networks—0
MUSE: Multi-faceted Attention for Signed Network Embedding—0
Network Embedding via Deep Prediction Model—0
ASBERT: Siamese and Triplet network embedding for open question answering—0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
Dynamic Network Embedding Survey—0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Progresses and Challenges in Link Prediction—0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
NEMR: Network Embedding on Metric of Relation—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
Signed Graph Diffusion Network—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Adversarial Attacks on Deep Graph Matching—0
Exploiting Node Content for Multiview Graph Convolutional Network and Adversarial RegularizationCode0
User-based Network Embedding for Collective Opinion Spammer Detection—0
Multi-View Dynamic Heterogeneous Information Network Embedding—0
Toward Edge-Centric Network Embeddings—0
Embedding Node Structural Role Identity into Hyperbolic Space—0
Identifying Transition States of Chemical Kinetic Systems using Network Embedding Techniques—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
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