Learning Community Embedding with Community Detection and Node Embedding on Graphs

By Sandro Cavallari, Vincent Wenchen Zheng, Hongyun Cai, Kevin Chen-Chuan Chang, Erik Cambria. 2017

395 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “Both [1] and [89] consider two nodes belonging to the same community as embedded closer.”
Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling (2018, background): “Sentiment analysis has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from financial [24] and political [25] forecasting, e-health [26] and etourism [27], user profilin”
Continuous-Time Dynamic Network Embeddings (2018, background): “, [15, 18, 26, 38, 56]) for learning more appropriate time-dependent embeddings from temporal networks.”
Embedding Temporal Network via Neighborhood Formation (2018, methodology): “It provides an efficient way to uncover the network structure and perform various network mining tasks such as node classification [20], link prediction [9], community detection [5], etc.”
Topological Recurrent Neural Network for Diffusion Prediction (2017, background): “ComE [34], [35] further considers community-aware high-order proximity for node embedding and community embedding.”
GEMSEC: Graph Embedding with Self Clustering (2018, methodology): “ere has been previous work on using learned representations for community detection, but existing research is limited to clustering the nodes aer creating an embedding [5, 29].”
From Node Embedding To Community Embedding (2016, background): “∗This is a technical report for paper [Cavallari et al., 2017].”
A Comprehensive Survey on Community Detection With Deep Learning (2021, background): “To this end, designing community-aware learning processes to characterize community information can improve the accuracy of community detection [169].”
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, background): “[117] find that there is a closed-loop relationship”
Network Representation Learning: A Survey (2017, background): “Only very recently, a few studies have started to design task-specific NRL algorithms for link prediction [35], community detection [98], [99], [100], [101], class imbalance learning [102], active learning [103], and information retrieval [104].”