struc2vec: Learning Node Representations from Structural Identity

11 Apr 2017shenweichen/GraphEmbedding

Numerical experiments indicate that state-of-the-art techniques for learning node representations fail in capturing stronger notions of structural identity, while struc2vec exhibits much superior performance in this task, as it overcomes limitations of prior approaches.

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

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 LANGUAGE MODELLING NODE CLASSIFICATION

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