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vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
[article]
2019
arXiv
pre-print
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We propose a probabilistic generative model called vGraph to learn community membership and node representation collaboratively. ...
Experimental results on multiple real-world graphs show that vGraph is very effective in both community detection and node representation learning, outperforming many competitive baselines in both tasks ...
In this paper, we propose a novel probabilistic generative model called vGraph for joint community detection and node representation learning. vGraph assumes that each node v can be represented as a mixture ...
arXiv:1906.07159v2
fatcat:emvhbxiuf5gvdcg6y77dhnqf4a
Variational Embeddings for Community Detection and Node Representation
[article]
2021
arXiv
pre-print
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We propose an efficient generative model called VECoDeR for jointly learning Variational Embeddings for Community Detection and node Representation. ...
A joint learning framework leverages community-aware node embeddings for better community detection. ...
Our main contributions are summarized below: • We propose an efficient generative model called VE-CODER for joint community detection and node representation learning. • We adopt a novel approach and argue ...
arXiv:2101.03885v1
fatcat:lxu5zjpdgzh6xp5rw2ceylju44
Deep Graph Clustering via Mutual Information Maximization and Mixture Model
[article]
2022
arXiv
pre-print
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Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. ...
In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding. ...
The authors are with the Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran (e-mail: m.ahmadi@ec.iut.ac.ir) ...
arXiv:2205.05168v1
fatcat:o6xb72h5fvhp7kp5wbwqwcmeqy
Community Detection in Partially Observable Social Networks
[article]
2021
arXiv
pre-print
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To solve this problem, we introduce KroMFac, a new framework that conducts community detection via regularized nonnegative matrix factorization (NMF) based on the Kronecker graph model. ...
In this paper, we address a new problem of detecting overlapping community structures in the context of such an incomplete network, where communities in the network are allowed to overlap since nodes belong ...
presents a generative model for joint community detection and node representation learning. 4 In this case, we first recover the matrix A ( ) via the function GraphRecv and then obtain via alternative ...
arXiv:1801.00132v8
fatcat:lcylh3shrzdgfn3i6bnei5fd5i
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
[article]
2021
arXiv
pre-print
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model and deep learning. ...
utilize deep learning and convert networked data into low dimensional representation. ...
[117] propose a probabilistic generative model, i.e., vGraph, to jointly detect overlapping (and nonoverlapping) communities and learn node (and community) representation. vGraph represents each node ...
arXiv:2101.01669v3
fatcat:p2lkjuslmzd6hc6kpum3sz5xwq
Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory
2021
Information
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We also implement the framework and propose two node embedding methods that use game theory for detecting partial community information. ...
Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. ...
Data Availability Statement: The data and code presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy reasons. ...
doi:10.3390/info12050186
doaj:bfe1626a2ca24dde9dc9137dbcb4ba4f
fatcat:vo32hudvmrg3pl4tomm6gwptyy
Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding
[article]
2021
arXiv
pre-print
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steps, node feature extraction steps and node embedding model training for a NRL task such as link prediction and node clustering. ...
developing the next generation of network representation learning algorithms and systems. ...
Specifically, for each node v, vGraph first draws a community assignment from ( |v) and then generates an edge e vu by drawing another node u according to distribution ( |C). ...
arXiv:2110.07582v1
fatcat:gbjn3evwwzf4xkeobrsfo6hope
Self-supervised Learning: Generative or Contrastive
[article]
2021
arXiv
pre-print
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In this survey, we take a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning. ...
As an alternative, self-supervised learning attracts many researchers for its soaring performance on representation learning in the last several years. ...
ACKNOWLEDGMENTS The work is supported by the National Key R&D Program of China (2018YFB1402600), NSFC for Distinguished Young Scholar (61825602), and NSFC (61836013). ...
arXiv:2006.08218v5
fatcat:t324amt3lzaehfa262xbn5hkqe