Tri-Party Deep Network Representation

By Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, Yang Wang. 2016

438 citations recorded by the source corpus.

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Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “Graph visualization generates visualizations of a graph on a low dimensional space [20], [23], [48], [55], [58], [73].”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “Similar to augmenting graph structure with node attributes for factorization based methods, GenVector [53], Discriminative Deep Random Walk (DDRW) [54], Tri-party Deep Network Representation (TriDNR) [55] and [56] extend random walks to jointly learn network structure and node attributes.”
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking (2017, methodology): “Furthermore, for attributed graphs by leveraging both sources of information (network structure and attributes) one is able to learn more useful representations compared to approaches that only consider the graph (Yang et al., 2015; Pan et al., 2016; Ganguly & Pudi, 2017).”
A Survey on Network Embedding (2017, methodology): “Citeseer is used in (Yang et al. 2015; Pan et al. 2016; Tu et al. 2016).”
Heterogeneous Information Network Embedding for Recommendation (2017, background): “Besides leaning network embedding from only the topology, there are alsomanyworks [51], [52], [53] leveraging node content information and other available graph information for the robust representations.”
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, background): “TriDNR [27] jointly learns node embedding via tri-party information sources including node’s structure, attributes and labels.”
MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017, background): “DeepRepresentationforGraphClustering: Deeplearningsheds light on modeling nonlinear or complex relationships, which has been successfully applied in many domains, such as speech recognition [5], computer vision [17], and network representations [25].”
Embedding Temporal Network via Neighborhood Formation (2018, background): “network model, which jointly models node structures, contents and labels [19].”
Learning Graph Embedding With Adversarial Training Methods (2019, background): “TriDNR [39] captures structure, node content, and label information via a tri-party neural-network architecture.”
Under review as a conference paper at ICLR 2019 MILE : A Multi-Level Framework for Scalable Graph Embedding (2018, unknown): “This also led to the proliferation of network embedding methods for information-rich graphs, including heterogeneous information networks (Chang et al., 2015; Dong et al., 2017) and attributed graphs (Pan et al., 2016; Liang et al., 2018; Yang et al., 2015; Kipf & Welling, 2017).”
Network Representation Learning: A Survey (2017, methodology): “Using a coupled neural network framework, TriDNR [50] learns vertex representations from three information sources: network structure, vertex content and vertex labels.”
SNE: Signed Network Embedding (2017, background): “The heterogeneous network embedding models are studied in [4, 25, 29, 37].”
Attributed Social Network Embedding (2017, methodology): “shown that neural network models such as [30], [31] can”