LINE: Large-scale Information Network Embedding

12 Mar 2015shenweichen/GraphEmbedding

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.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

DeepWalk: Online Learning of Social Representations

26 Mar 2014shenweichen/GraphEmbedding

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.

ANOMALY DETECTION DOCUMENT CLASSIFICATION LANGUAGE MODELLING NODE CLASSIFICATION

struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017shenweichen/GraphEmbedding

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

GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

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.

LINK PREDICTION NETWORK EMBEDDING

node2vec: Scalable Feature Learning for Networks

3 Jul 2016shenweichen/GraphEmbedding

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

LINK PREDICTION MULTI-LABEL CLASSIFICATION NODE CLASSIFICATION REPRESENTATION LEARNING

Cannot find the paper you are looking for? You can Submit a new open access paper.