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

Source: Tutorial on NLP-Inspired Network Embedding

Libraries

Use these libraries to find Network Embedding models and implementations

Most implemented papers

LINE: Large-scale Information Network Embedding

shenweichen/GraphEmbedding 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.

Structural Deep Network Embedding

shenweichen/GraphEmbedding KDD 2016

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

leoribeiro/struc2vec 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

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

xptree/NetMF 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.

Representation Learning for Attributed Multiplex Heterogeneous Network

cenyk1230/GATNE 5 May 2019

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

Multi-scale Attributed Node Embedding

benedekrozemberczki/MUSAE 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.

Fast Sequence-Based Embedding with Diffusion Graphs

benedekrozemberczki/diff2vec 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.

Network Representation Learning with Rich Text Information

albertyang33/TADW IJCAI 2015

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

HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

zhoushengisnoob/HAHE 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.

Multi-View Collaborative Network Embedding

cenyk1230/GATNE 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.