Network Embedding
166 papers with code • 0 benchmarks • 5 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
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arXiv Astro-Ph
Netzschleuder
Most implemented papers
LINE: Large-scale Information Network Embedding
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
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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
Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec
Multi-scale Attributed Node Embedding
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.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
With experiments on a series of synthetic datasets, a large-scale internal Snapchat dataset, and two public datasets, we confirm the validity and importance of preservation and collaboration as two objectives for multi-view network embedding.
Representation Learning for Attributed Multiplex Heterogeneous Network
cenyk1230/GATNE
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Network embedding (or graph embedding) has been widely used in many real-world applications.
Fast Sequence-Based Embedding with Diffusion Graphs
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
Representation learning has shown its effectiveness in many tasks such as image classification and text mining.
Outlier Aware Network Embedding for Attributed Networks
We also consider different downstream machine learning applications on networks to show the efficiency of ONE as a generic network embedding technique.
