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Online Inference of Mixed Membership Stochastic Blockmodels for Network Data Streams

Tomoki KOBAYASHI, Koji EGUCHI
2014 IEICE transactions on information and systems  
Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data.  ...  blockmodels, particle filters, dynamic networks, online inference  ...  Acknowledgements This work was supported in part by the Grant-in-Aid for Scientific Research (#23300039) from JSPS, Japan.  ... 
doi:10.1587/transinf.e97.d.752 fatcat:w5eokdn3tva53ljou76lot6dty

Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts [article]

Santiago Olivella, Tyler Pratt, Kosuke Imai
2021 arXiv   pre-print
To aid the empirical testing of these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups  ...  A primary goal of social science research is to understand how latent group memberships predict the dynamic process of network evolution.  ...  To aid the empirical testing of these theories, we develop a dynamic model of social networks that extends the mixed-membership stochastic blockmodel (MMSBM; Airoldi et al., 2008) .  ... 
arXiv:2103.00702v2 fatcat:s35ypv3n6vgvxj6kvu4ukdrkje

Dynamic Community Detection with Temporal Dirichlet Process

Xuning Tang, Christopher C. Yang
2011 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing  
To overcome these limitations, we propose the Dynamic Stochastic Blockmodel with Temporal Dirichlet Process, which is able to detect communities and track their evolution simultaneously from a network  ...  Research of detecting dynamic communities from network stream has attracted increasingly attention recently due to its broad applications on social media, e-commence, intelligent security, healthcare and  ...  Fu, Song and Xing extended Airoldi's work [1] to model the evolution of mixed membership blockmodel [11] . However, the number of communities is fixed over time in all these works.  ... 
doi:10.1109/passat/socialcom.2011.37 dblp:conf/socialcom/TangY11 fatcat:zaueixgzz5dlpivubzkkvqkhdu

Scalable Inference of Overlapping Communities

Prem Gopalan, David M. Mimno, Sean Gerrish, Michael J. Freedman, David M. Blei
2012 Neural Information Processing Systems  
Our algorithm is based on stochastic variational inference in the mixed-membership stochastic blockmodel (MMSB).  ...  We develop a scalable algorithm for posterior inference of overlapping communities in large networks.  ...  In this paper, we focus on the mixed-membership stochastic blockmodel (MMSB) [2] , a probabilistic model that allows each node of a network to exhibit a mixture of communities.  ... 
dblp:conf/nips/GopalanMGFB12 fatcat:gvvrezzoqbbufigfaskrmdrqja

Role Discovery in Networks

Ryan A. Rossi, Nesreen K. Ahmed
2015 IEEE Transactions on Knowledge and Data Engineering  
Traditionally, the notion of roles were defined based on graph equivalences such as structural, regular, and stochastic equivalences.  ...  We discuss the different possibilities for discovering feature-based roles and the tradeoffs of the many techniques for computing them.  ...  Recently, many types of blockmodels have been proposed such as stochastic blockmodels [10] , generalized blockmodels [8] , and mixed-membership stochastic blockmodels [32] (MMSB).  ... 
doi:10.1109/tkde.2014.2349913 fatcat:3qmmwr3hq5divmr57khcpy6uey

Scalable Overlapping Community Detection

Ismail El-Helw, Rutger Hofman, Wenzhe Li, Sungjin Ahn, Max Welling, Henri Bal
2016 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)  
For example, a social network can be modeled as a graph where the vertices and edges represent individuals and their relationships.  ...  To the best of our knowledge, this is the first time that the problem of deducing overlapping communities has been learned for problems of such a large scale.  ...  ACKNOWLEDGEMENTS The authors thank Kees Verstoep for his excellent maintenance and administration of the VU DAS5 cluster.  ... 
doi:10.1109/ipdpsw.2016.165 dblp:conf/ipps/El-HelwHLAWB16 fatcat:3qmxo3u4ufawbnckpjdisnwxpy

Text mining in computational advertising

Jacopo Soriano, Timothy Au, David Banks
2013 Statistical analysis and data mining  
Anusuya and Katti, 2009, and Indurkhya and Damerau, 2010 , for review articles). In the context of text networks, there is an ongoing effort to build the Semantic Web (cf.  ...  The other uses the bag-of-words model in which only vocabulary matters-all permutations of the words in a document provide equivalent data.  ...  We also thank the year-long program on complex networks that was sponsored by the Statistical and Applied Mathematical Sciences Institute in 2010-2011.  ... 
doi:10.1002/sam.11197 fatcat:ewb4dgiwezafrb2gxq3kaqj6km

Fraud Detection through Graph-Based User Behavior Modeling

Alex Beutel, Leman Akoglu, Christos Faloutsos
2015 Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security - CCS '15  
For each of these techniques we will give an explanation of the algorithms and the intuition behind them.  ...  In particular, we will focus on three data mining techniques: subgraph analysis, label propagation and latent factor models; and their application to static graphs, e.g. social networks, evolving graphs  ...  In Proceedings of the tenth ACM SIGKDD international conference on 5. REFERENCES [1] Edoardo M Airoldi, David M Blei, Stephen E Fienberg, and Eric P Xing. Mixed membership stochastic blockmodels.  ... 
doi:10.1145/2810103.2812702 dblp:conf/ccs/BeutelAF15 fatcat:yfgbw3wkkzcs3jgunwqpqma2t4

Community Level Diffusion Extraction

Zhiting Hu, Junjie Yao, Bin Cui, Eric Xing
2015 Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data - SIGMOD '15  
Our method guarantees high scalability with increasing data size. 1 Weak social ties are responsible for the majority of the information spreading through human networks.  ...  How does online content propagate on social networks? Billions of users generate, consume, and spread tons of information every day.  ...  Although some of its building blocks are inspired by recent successful attempts, including the Mixed Membership Stochastic Blockmodel (MMSB) [?] over networks, and Topics over Time (TOT) [?]  ... 
doi:10.1145/2723372.2723737 dblp:conf/sigmod/HuYCX15 fatcat:e73jxvicovdz5ecmgl3et44tei

A Comprehensive Survey on Community Detection with Deep Learning [article]

Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
2021 arXiv   pre-print
Despite the classical spectral clustering and statistical inference methods, we notice a significant development of deep learning techniques for community detection in recent years with their advantages  ...  in handling high dimensional network data.  ...  Mixed membership stochastic blockmodels Markov Random Fields as a convolutional Graph convolutional networks meet Markov random fields: MRFasGCN layer in Graph Convolutional Networks [10] Semi-supervised  ... 
arXiv:2105.12584v2 fatcat:matipshxnzcdloygrcrwx2sxr4

Clustering and community detection in directed networks: A survey

Fragkiskos D. Malliaros, Michalis Vazirgiannis
2013 Physics reports  
The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications  ...  Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications.  ...  Acknowledgments The authors would like to thank the anonymous reviewer for the valuable and constructive comments, as well as the authors that kindly offered visual content of their articles.  ... 
doi:10.1016/j.physrep.2013.08.002 fatcat:qyj2bq6j5vbhhlcoyqnjgrmgwu

IEEE Access Special Section Editorial: Advanced Data Mining Methods for Social Computing

Yongqiang Zhao, Shirui Pan, Jia Wu, Huaiyu Wan, Huizhi Liang, Haishuai Wang, Huawei Shen
2020 IEEE Access  
., ''Detecting overlapping community structure with node influence,'' designs a posterior probabilistic prediction model under the Mixed-Membership Stochastic Blockmodel framework to accurately detect  ...  The article develops a nonconjugated stochastic variational inference to deduce the link probability prediction model with node influence.  ... 
doi:10.1109/access.2020.3043060 fatcat:qbqk5f4ojvadlazhk2mc343sra

Unsupervised Streaming Feature Selection in Social Media

Jundong Li, Xia Hu, Jiliang Tang, Huan Liu
2015 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management - CIKM '15  
The explosive growth of social media sites brings about massive amounts of high-dimensional data. Feature selection is effective in preparing high-dimensional data for data analytics.  ...  In this paper, we study a novel problem to conduct unsupervised streaming feature selection for social media data.  ...  In this work, we extract the social latent factors for each instance based on the mixed membership stochastic blockmodel [2] .  ... 
doi:10.1145/2806416.2806501 dblp:conf/cikm/LiHTL15 fatcat:pczmffbilngkhdxulbsonwficq

Multiple factor analysis for time-varying two-mode networks

GIANCARLO RAGOZINI, DOMENICO DE STEFANO, MARIA ROSARIA D'ESPOSITO
2015 Network Science  
This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the  ...  Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data  ...  Our contribution lies in defining a internet forum data mining framework and an inferring scheme for extracting social networks from the mined data.  ... 
doi:10.1017/nws.2015.5 fatcat:gslvillycvclnlftygsrd635ju

Static and Dynamic Community Detection Methods that Optimize a Specific Objective Function: A Survey and Experimental Evaluation

Kamal Taha
2020 IEEE Access  
For each method, we survey the different techniques in literature employed by the method.  ...  Finally, we provide fitness metrics for each objective function. INDEX TERMS Clustering, community detection, objective function.  ...  They provide a generalization model of the blockmodel [36] by allowing for data variability for detecting community structures.  ... 
doi:10.1109/access.2020.2996595 fatcat:lf6ghd6afjhi7cls4vnutoytlu
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