Community-Affiliation Graph Model for Overlapping Network Community Detection

By Jaewon Yang, Jure Leskovec. 2012 IEEE 12th International Conference on Data Mining, 2012

309 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ab50e6f82dc5029a256800a46b4f61eb4d65f7f0

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Citations in the corpus

Defining and evaluating network communities based on ground-truth (2012, background): “For example, furth er examining the connectivity structure of ground-truth communities could lead to novel community detection methods [30].”
SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, methodology): “SNAP methods for these prediction tasks are based on the multiplicative attribute graph (MAG) model [Kim and Leskovec 2012b].”
MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017, background): “Recently, over-lapping community detection algorithms, like BigClam [39] and AgmFit [38], have also been developed; however, these algorithms are suboptimal because they only use one channel of information and ignore the other.”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “Among the many possible generalizations of this model, some have: considered a deeper hierarchy of groups [221]; include heterogeneities by introducing a group dependent connection probability qr [222]; introduce the possibility of forming a few ‘random’ links with nodes not in the immediate group o”
The ground truth about metadata and community detection in networks (2016, background): “Treating node metadata as ground truth communities for real-world networks is commonly justified via an erroneous belief that the purpose of community detection is to recover groups that match metadata labels [11, 13, 31, 43].”
Community Detection with Graph Neural Networks (2017, methodology): “Jure and his coauthors have done a lot of work in community detection in curating benchmark datasets, quantifying the quality of these datasets(Yang & Leskovec, 2012a), and also in making new algorithms for community detection by ﬁtting data to newly designed generative models (models that exhibit…”
Overlapping Communities Explain Core–Periphery Organization of Networks (2014, methodology): “In order to model communities in a network, we define the AGM [19].”
Community detection in networks: A user guide (2016, methodology): “For Amazon co-purchasing network (Yang and Leskovec, 2012b), in which vertices are products and edges are set between products often purchased by the same customer(s), the similarity is quite a 18 bit higher than for the other networks.”
Supervised Community Detection with Line Graph Neural Networks (2017, methodology): “Yang & Leskovec (2012a) curate benchmark datasets for community detection and quantify the quality of these datasets, while Yang & Leskovec (2012b) develop new algorithms for community detection by fitting to the networks the Affliation Graph Model (AGM), a generative model for graphs with…”
Overlapping community detection at scale: a nonnegative matrix factorization approach (2013, methodology): “In our previous work [33, 34, 35] we found the above definitions of ground-truth to be reliable and robust.”
Discovering social circles in ego networks (2012, background): “…assume disjoint communities [Schaeffer 2007], many authors have made the observation that communities in real-world networks may overlap [Lancichinetti and Fortunato 2009a; Gregory 2010a; Lancichinetti et al. 2009; Yang and Leskovec 2012] or have hierarchical structure [Ravasz and Barabási 2003].”