46 papers with code ·
Methodology

Subtask of
Representation Learning

Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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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.

#2 best model for Graph Classification on BP-fMRI-97

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.

SOTA for Node Classification on YouTube

DIMENSIONALITY REDUCTION KNOWLEDGE GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

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

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

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING PRODUCT RECOMMENDATION

WWW 2019 • benedekrozemberczki/Splitter •

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

ASONAM 2019 • benedekrozemberczki/GEMSEC •

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

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

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

IJCAI 2018 • benedekrozemberczki/role2vec

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