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

Source: Tutorial on NLP-Inspired Network Embedding

Libraries

Use these libraries to find Network Embedding models and implementations

L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders

tonyauyeung/local2gae2global 2 Feb 2024

For analysing real-world networks, graph representation learning is a popular tool.

0
02 Feb 2024

BHGNN-RT: Network embedding for directed heterogeneous graphs

albertlordsun/bhgnn-rt 24 Nov 2023

Networks are one of the most valuable data structures for modeling problems in the real world.

0
24 Nov 2023

A Simple and Powerful Framework for Stable Dynamic Network Embedding

edwarddavis1/universal_dynamic_embedding_with_testing 14 Nov 2023

We propose that a wide class of established static network embedding methods can be used to produce interpretable and powerful dynamic network embeddings when they are applied to the dilated unfolded adjacency matrix.

0
14 Nov 2023

Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding

kmj0792/trustsgcn 2 Sep 2023

The proposed approach consists of three modules: (M1) generation of each node's extended ego-network; (M2) measurement of trustworthiness on edge signs; and (M3) trustworthiness-aware propagation of embeddings.

2
02 Sep 2023

A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks

nicknakis/hm-ldm 29 Aug 2023

Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification.

1
29 Aug 2023

Gradient-Based Spectral Embeddings of Random Dot Product Graphs

marfiori/efficient-ase 25 Jul 2023

RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions.

4
25 Jul 2023

Collaborative Graph Neural Networks for Attributed Network Embedding

qiaoyut/conn 22 Jul 2023

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.

0
22 Jul 2023

Random Walk on Multiple Networks

flyingdoog/rwm 4 Jul 2023

To take advantage of rich information in multiple networks and make better inferences on entities, in this study, we propose random walk on multiple networks, RWM.

12
04 Jul 2023

Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds

zjunet/damf 15 Jun 2023

Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice.

4
15 Jun 2023

H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces

tailvyuanliang/h2tne 14 Apr 2023

Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks.

2
14 Apr 2023