Browse > Methodology > Representation Learning > Network Embedding

# Network Embedding Edit

25 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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# 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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# 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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# 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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# PRUNE: Preserving Proximity and Global Ranking for Network Embedding

We investigate an unsupervised generative approach for network embedding.

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# Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

10 Jul 2018GentleZhu/HEER

To cope with the challenges in the comprehensive transcription of HINs, we propose the HEER algorithm, which embeds HINs via edge representations that are further coupled with properly-learned heterogeneous metrics.

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# Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings

We present Walklets, a novel approach for learning multiscale representations of vertices in a network.

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# Font Size: Community Preserving Network Embedding

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.

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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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# Learning Role-based Graph Embeddings

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

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