Relational learning via latent social dimensions

By Lei Tang, Huan Liu. 2009

834 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Representation & Self-Supervised Learning

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Citations in the corpus

DeepWalk: online learning of social representations (2014, methodology): “To facilitate the comparison between our method and the relevant baselines, we use the exact same datasets and experimental procedure as in [39, 40].”
A Comprehensive Survey on Graph Neural Networks (2019, neutral): “5 19 2 [25], [26], [46], [52], [57] QM9 [125] 133885 [27], [69] Alchemy [126] 119487 Social Networks Reddit [42] 1 232965 11606919 602 41 [42], [48], [49], [50], [51], [56] BlogCatalog [127] 1 10312 333983 39 [18], [55], [60], [64]”
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).”
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017, methodology): “Following a few pioneer works such as SocDim [36], Deep-Walk [28], LINE [35], a growing number of literature has paid the attention to this area, for example, heterogeneous network embedding [7, 11, 17, 34], direct network embedding [27], semi-supervised network embedding [18, 42, 49], network…”
Multi-label relational neighbor classification using social context features (2013, result): “For instance, in previous work by Tang and Liu [22, 24], combining labeled node features and relational features aggregated from neighbors in a link-based classiﬁer performed poorly.”
Structural Deep Network Embedding (2016, methodology): “For the multi-label classiﬁcation task, we adopt micro-F 1 and macro-F 1 as many other works do [27].”
Tri-Party Deep Network Representation (2016, background): “Early works aim to learn social dimensions [Tang and Liu, 2009] or knowledge bases [Bordes et al., 2011; Socher et al., 2013] to embed the entities in a network.”
Cross-domain collaboration recommendation (2012, background): “Researchers have studied various issues in social networks, such as network properties [6, 11] and generation processes [18], link predictions [19, 20, 21, 32] and recommendations [2, 7, 17].”
Learning Image and User Features for Recommendation in Social Networks (2015, background): “, σ(i, j) = 1 or 0) is NP-hard [5], a relaxed approximation of the problem can be solved efficiently [29] when we relax the membership indicator σ(i, j) = p (i|j) =”
Scalable learning of collective behavior based on sparse social dimensions (2009, methodology): “To address the heterogeneity presented in connections, we have proposed a framework ( SocDim ) [18] for collective behavior learning.”
Social Network Data Analytics (2011, methodology): “[64] compute the trailing eigenvectors of L to reconstruct L+.”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “…the deep architecture was demonstrated to outperform several multi-label network trafﬁc classiﬁcation methods such as SpectralClustering [201], Modularity [202], EdgeCluster [203], and the weighted-vote Relational Neighbor (wvRN) [204] for social networks like BlogCatalog, Flickr, and YouTube.”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, positive): “Flickr 31 32 : The Flickr dataset [213] is obtained from a network of users on a photo-sharing website.”
Discriminative Deep Random Walk for Network Classification (2016, methodology): “(Tang and Liu, 2009b) propose an edge-centric clustering scheme to extract sparse social dimensions for collective behavior prediction.”
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, methodology): “The datasets include two social network datasets (BlogCatalog and Flickr) and ﬁve citation network datasets (Cora, Citeseer, Pubmed, ACM and ogbn-arxiv) [61], [2], [62].”