Network Embedding

122 papers with code • 0 benchmarks • 4 datasets

Network Embedding 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

Libraries

Use these libraries to find Network Embedding models and implementations
14 papers
1,612
3 papers
42
3 papers
42

LINE: Large-scale Information Network Embedding

12 Mar 2015

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.

8

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.

6

struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017

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

5

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

9 Oct 2017

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

4

Representation Learning for Attributed Multiplex Heterogeneous Network

5 May 2019

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

4

Multi-scale Attributed Node Embedding

28 Sep 2019

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.

4

Fast Sequence-Based Embedding with Diffusion Graphs

21 Jan 2020

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

4

Network Representation Learning with Rich Text Information

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

3

HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

31 Jan 2019

Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.

3

Multi-View Collaborative Network Embedding

17 May 2020

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

3