Network Embedding

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

Source: Tutorial on NLP-Inspired Network Embedding

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Greatest papers with code

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

GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

Structural Deep Network Embedding

KDD 2016 shenweichen/GraphEmbedding

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.

GRAPH CLASSIFICATION LINK PREDICTION NETWORK EMBEDDING

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.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

Fast Network Embedding Enhancement via High Order Proximity Approximation

‏‏‎ ‎ 2020 benedekrozemberczki/karateclub

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

DIMENSIONALITY REDUCTION LINK PREDICTION MULTI-LABEL CLASSIFICATION NETWORK EMBEDDING

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.

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING

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

CIKM 2019 benedekrozemberczki/karateclub

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.

COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION

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.

NETWORK EMBEDDING

Enhanced Network Embedding with Text Information

24th International Conference on Pattern Recognition (ICPR) 2018 benedekrozemberczki/karateclub

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.

MULTI-CLASS CLASSIFICATION NETWORK EMBEDDING NODE CLASSIFICATION

Binarized Attributed Network Embedding

ICDM 2018 benedekrozemberczki/karateclub

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

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

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

ASONAM 2019 2019 benedekrozemberczki/karateclub

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.

FEATURE ENGINEERING NETWORK EMBEDDING