Network Embedding
State-of-the-art leaderboards
| Trend | Dataset | Best Method | Paper title | Paper | Code | Compare |
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Greatest papers with code
LINE: Large-scale Information Network Embedding
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
struc2vec: Learning Node Representations from Structural Identity
• shenweichen/GraphEmbedding
• 
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
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.
#2 best model for
Graph Classification
on BP-fMRI-97
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
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
CANE: Context-Aware Network Embedding for Relation Modeling
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
Representation Learning for Attributed Multiplex Heterogeneous Network
• THUDM/GATNE
•
Network embedding (or graph embedding) has been widely used in many real-world applications.
GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING PRODUCT RECOMMENDATION
Splitter: Learning Node Representations that Capture Multiple Social Contexts
WWW 2019
• benedekrozemberczki/Splitter
• 
Recent interest in graph embedding methods has focused on learning a single representation for each node in the graph.
GEMSEC: Graph Embedding with Self Clustering
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
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.
Learning Role-based Graph Embeddings
IJCAI 2018 • benedekrozemberczki/role2vec
Random walks are at the heart of many existing network embedding methods.