Leveraging social media networks for classification

By Lei Tang, Huan Liu. 2011

356 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Graph Learning

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

In Graph Learning

Citations in the corpus

node2vec: Scalable Feature Learning for Networks (2016, methodology): “• Spectral clustering [29]: This is a matrix factorization approach in which we take the top d eigenvectors of the normalized Laplacian matrix of graph G as the feature vector representations for nodes.”
DeepWalk: online learning of social representations (2014, methodology): “A number of techniques for generating features from graphs have also been proposed [13,17,39–41].”
Variational Graph Auto-Encoders (2016, methodology): “Method Cora Citeseer Pubmed AUC AP AUC AP AUC AP SC [5] 84.”
Graphite: Iterative Generative Modeling of Graphs (2018, methodology): “We evaluated Graphite-VAE and Graphite-AE against the following baselines: Spectral Clustering (SC) [45], DeepWalk [35], node2vec [13], and GAE/VGAE [22].”
Adversarially Regularized Graph Autoencoder (2018, methodology): “• Spectral Clustering [Tang and Liu, 2011]: is an effective approach for learning social embedding.”
Deep Neural Networks for Learning Graph Representations (2016, methodology): “The learned vertex representations were shown to be effective across a few tasks, outperforming several previous approaches such as spectral clustering and modularity method (Tang and Liu 2009a; 2009b; 2011; Macskassy and Provost 2003).”
Discriminative Deep Random Walk for Network Classification (2016, methodology): “(Tang and Liu, 2011) propose the concept of social dimensions to represent the latent affiliations of the entities.”
Learning Community Embedding with Community Detection and Node Embedding on Graphs (2017, methodology): “Then, a straightforward approach for community embedding is to: (1) run community detection, such as Spectral Clustering [25], on the graph to get community assignments for each node; (2) apply node embedding, such as Deep-Walk [20] or LINE [24], on the graph to get an embedding vector for each…”
Large-Scale Discovery of Disease-Disease and Disease-Gene Associations (2016, methodology): “We evaluated the proposed methods against state-of-the-art approaches, such as 1) Latent Dirichlet Allocation (LDA) 46 , 2) spectral clustering 47 , and 3) modularity 48 , which have been successfully applied to EHR analysis 49,50 .”
Structural Neighborhood Based Classification of Nodes in a Network (2016, methodology): “SocioDim-EdgeClustering [24]: As an extension to SocioDim-Modularity, this approach performs edge clustering by using incidence matrix of the given graph.”
Homophily, Structure, and Content Augmented Network Representation Learning (2016, background): “Earlier work on NRL has attempted to partition a graph into different communities for creating social dimensions [8, 16, 17].”
Adversarial Network Embedding (2017, methodology): “As demonstrated in the literature, network embedding methods were shown to be more effective in many network analysis tasks than some classical approaches, such as Common Neighbors (Liben-Nowell and Kleinberg 2007) and Spectral Clustering (Tang and Liu 2011).”
Fast Network Embedding Enhancement via High Order Proximity Approximation (2017, methodology): “In this section, we present a unified framework which can cover several representative NRL algorithms include Spectral Clustering [Tang and Liu, 2011], DeepWalk [Perozzi et al., 2014], TADW [Yang et al., 2015], LINE [Tang et al., 2015b] and GraRep [Cao et al., 2015].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “was demonstrated to outperform several multi-label network traffic classification methods such as SpectralClustering [201], Modularity [202], EdgeCluster [203], and the weighted-vote”
Predict Anchor Links across Social Networks via an Embedding Approach (2016, methodology): “There are several well-known methods for network embedding, including Spectral Clustering (SC) [Tang and Liu, 2011], Graph Factorization (GF) [Ahmed et al. , 2013], Deepwalk [Perozzi et al. , 2014].”