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Found 7 papers, 6 papers with code
Why do embedding spaces look as they do?
The power of embedding representations is a curious phenomenon.
Fast and Accurate Network Embeddings via Very Sparse Random Projection
Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimization-free method for dimension reduction.
Enhanced Network Embeddings via Exploiting Edge Labels
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network.
Social and Information Networks Physics and Society
Learning to Represent Bilingual Dictionaries
Bilingual word embeddings have been widely used to capture the similarity of lexical semantics in different human languages.
A Tutorial on Network Embeddings
We further demonstrate the applications of network embeddings, and conclude the survey with future work in this area.
Social and Information Networks
HARP: Hierarchical Representation Learning for Networks
We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features.
Social and Information Networks
Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
We present Walklets, a novel approach for learning multiscale representations of vertices in a network.
Social and Information Networks Physics and Society
