# Network Embedding

136 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 implementations## Most implemented papers

# LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction.

# Structural Deep Network Embedding

Therefore, how to ﬁnd a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

# struc2vec: Learning Node Representations from Structural Identity

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

# Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.

# Representation Learning for Attributed Multiplex Heterogeneous Network

Network embedding (or graph embedding) has been widely used in many real-world applications.

# Multi-scale Attributed Node Embedding

We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.

# Fast Sequence-Based Embedding with Diffusion Graphs

A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.

# Network Representation Learning with Rich Text Information

Representation learning has shown its effectiveness in many tasks such as image classification and text mining.

# Outlier Aware Network Embedding for Attributed Networks

We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique.

# Multi-View Collaborative Network Embedding

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes.