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Network Embedding

53 papers with code · Methodology

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LINE: Large-scale Information Network Embedding

12 Mar 2015tangjianpku/LINE

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

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

GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding

2 Mar 2019DeepGraphLearning/graphvite

In this paper, we propose GraphVite, a high-performance CPU-GPU hybrid system for training node embeddings, by co-optimizing the algorithm and the system.

DIMENSIONALITY REDUCTION KNOWLEDGE GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

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

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.

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

Fast Sequence Based Embedding with Diffusion Graphs

CompleNet 2018 benedekrozemberczki/karateclub

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

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

Font Size: Community Preserving Network Embedding

AAAI 2017 benedekrozemberczki/karateclub

While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.

COMMUNITY DETECTION NETWORK EMBEDDING