# Network Embedding Edit

69 papers with code · Methodology

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

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# struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017shenweichen/GraphEmbedding

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

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# Structural Deep Network Embedding

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.

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# LINE: Large-scale Information Network Embedding

12 Mar 2015shenweichen/GraphEmbedding

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.

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# Fast Network Embedding Enhancement via High Order Proximity Approximation

Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently.

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# Fast Sequence-Based Embedding with Diffusion Graphs

21 Jan 2020benedekrozemberczki/karateclub

A graph embedding is a representation of graph vertices in a low-dimensional space, which approximately preserves properties such as distances between nodes.

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# A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

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# Multi-scale Attributed Node Embedding

28 Sep 2019benedekrozemberczki/karateclub

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.

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# Enhanced Network Embedding with Text Information

TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information.

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# Binarized Attributed Network Embedding

To this end, we present a Binarized Attributed Network Embedding model (BANE for short) to learn binary node representation.

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# Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation

As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.

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