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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
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
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—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
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