Network Embedding
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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
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.
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.
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.
PRUNE: Preserving Proximity and Global Ranking for Network Embedding
NeurIPS 2017
• ntumslab/PRUNE
• 
We investigate an unsupervised generative approach for network embedding.
COMMUNITY DETECTION LEARNING-TO-RANK LINK PREDICTION NETWORK EMBEDDING
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
• GentleZhu/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.
Don't Walk, Skip! Online Learning of Multi-scale Network Embeddings
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
Font Size: Community Preserving Network Embedding
AAAI 2017
• benedekrozemberczki/M-NMF
•
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.
Learning Role-based Graph Embeddings
IJCAI 2018 • benedekrozemberczki/role2vec
Random walks are at the heart of many existing network embedding methods.
Learning Role-based Graph Embeddings
IJCAI 2018 • benedekrozemberczki/role2vec
Random walks are at the heart of many existing network embedding methods.