Browse > Methodology > Representation Learning > Network Embedding

# Network Embedding Edit

46 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.

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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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# 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.

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# CANE: Context-Aware Network Embedding for Relation Modeling

Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors.

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# Representation Learning for Attributed Multiplex Heterogeneous Network

5 May 2019THUDM/GATNE

Network embedding (or graph embedding) has been widely used in many real-world applications.

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# Splitter: Learning Node Representations that Capture Multiple Social Contexts

Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.

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# GEMSEC: Graph Embedding with Self Clustering

In this paper we propose GEMSEC - a graph embedding algorithm which learns a clustering of the nodes simultaneously with the embedding.

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# Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

9 Oct 2017xptree/NetMF

This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.

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# Learning Role-based Graph Embeddings

Random walks are at the heart of many existing network embedding methods.

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