Network Embedding
152 papers with code • 0 benchmarks • 4 datasets
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
Benchmarks
These leaderboards are used to track progress in Network Embedding
Libraries
Use these libraries to find Network Embedding models and implementationsLatest papers with no code
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition
Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach to significantly improve latent recognition results.
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Using this likelihood, we propose the Dynamic Impact Single-Event Embedding model (DISEE).
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
The Loss Landscape of Shallow ReLU-like Neural Networks: Stationary Points, Saddle Escaping, and Network Embedding
Additionally, we show that, if a stationary point does not contain "escape neurons", which are defined with first-order conditions, then it must be a local minimum.
Detecting local perturbations of networks in a latent hyperbolic space
Graph theoretical approaches have been proven to be effective in the characterization of connected systems, as well as in quantifying their dysfunction due to perturbation.
Clustering Molecular Energy Landscapes by Adaptive Network Embedding
In order to efficiently explore the chemical space of all possible small molecules, a common approach is to compress the dimension of the system to facilitate downstream machine learning tasks.
Hedging carbon risk with a network approach
As a consequence, while it is possible to obtain an efficient hedging portfolio strategy with our methodology for the carbon factor, the same cannot be achieved for the ESG one.
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring
In this paper we investigate automated annotation of tabular data using semantic technologies in combination with neural network embedding.
Network Embedding Using Sparse Approximations of Random Walks
In this paper, we propose an efficient numerical implementation of Network Embedding based on commute times, using sparse approximation of a diffusion process on the network obtained by a modified version of the diffusion wavelet algorithm.
A Weakly Supervised Segmentation Network Embedding Cross-scale Attention Guidance and Noise-sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors
The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors.