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
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Libraries
Use these libraries to find Network Embedding models and implementationsLatest papers
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool.
BHGNN-RT: Network embedding for directed heterogeneous graphs
Networks are one of the most valuable data structures for modeling problems in the real world.
A Simple and Powerful Framework for Stable Dynamic Network Embedding
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.
Trustworthiness-Driven Graph Convolutional Networks for Signed Network Embedding
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.
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
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.
Gradient-Based Spectral Embeddings of Random Dot Product Graphs
RDPGs crucially postulate that edge formation probabilities are given by the dot product of the corresponding latent positions.
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attributed network embedding.
Random Walk on Multiple Networks
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.
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
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.
H2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
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.