31 papers with code ·
Methodology

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Representation Learning

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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.

#5 best model for Node Classification on Wikipedia

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

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

#2 best model for Node Classification on Wikipedia

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.

ACL 2017 • thunlp/CANE •

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

COMMUNITY DETECTION LINK PREDICTION MACHINE TRANSLATION NETWORK EMBEDDING

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

WWW 2019 • benedekrozemberczki/Splitter •

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

ASONAM 17 2016 • benedekrozemberczki/walklets

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

GRAPH EMBEDDING MULTI-LABEL CLASSIFICATION NETWORK EMBEDDING NODE CLASSIFICATION

NeurIPS 2017 • ntumslab/PRUNE •

We investigate an unsupervised generative approach for network embedding.

COMMUNITY DETECTION LEARNING-TO-RANK LINK PREDICTION NETWORK EMBEDDING

IJCAI 2018 • benedekrozemberczki/role2vec

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

IJCAI 2018 • benedekrozemberczki/role2vec

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