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Probabilistic models for discovering e-communities

Ding Zhou, Eren Manavoglu, Jia Li, C. Lee Giles, Hongyuan Zha
2006 Proceedings of the 15th international conference on World Wide Web - WWW '06  
In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs.  ...  The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA).  ...  Our models combine the generative probabilistic modeling with community detection.  ... 
doi:10.1145/1135777.1135807 dblp:conf/www/ZhouMLGZ06 fatcat:qt7gtv3fnnei7kgkd6lwr6lp64

Discovering leaders from social network by action cascade

Ming-Feng Tsai, Chih-Wei Tzeng, Arbee L. P. Chen
2012 Proceedings of the Fifth Workshop on Social Network Systems - SNS '12  
This paper proposes an approach to discovering community leaders in social network by means of a probabilistic timebased graph propagation model.  ...  In addition, several baselines are also carried out for comparison, including three naive and one user-involved approaches.  ...  Conclusions The contribution of this work includes the proposition of the probabilistic time-based graph propagation model for community leader discovery.  ... 
doi:10.1145/2181176.2181188 dblp:conf/sns/TsaiTC12 fatcat:yan3rpojx5fzzbrvl65eu7fx7m

HSN-PAM: Finding Hierarchical Probabilistic Groups from Large-Scale Networks

Haizheng Zhang, Wei Li, Xuerui Wang, C. Lee Giles, Henry C. Foley, John Yen
2007 Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)  
This paper describes a hierarchical Bayesian model based scheme, namely HSN-PAM (Hierarchical Social Network-Pachinko Allocation Model), for discovering probabilistic, hierarchical communities in social  ...  The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.  ...  mixture models, to identify and discover probabilistic hierarchical communities in complex, large-scale social networks.  ... 
doi:10.1109/icdmw.2007.115 dblp:conf/icdm/ZhangLWGFY07 fatcat:5rdrj4iyj5eclnacloflvumyxa

Graphical models based hierarchical probabilistic community discovery in large-scale social networks

Haizheng Zhang, N.A. Ke, Wei Li, Xuerui Wang
2010 International Journal of Data Mining Modelling and Management  
This paper describes a hierarchical Bayesian model based scheme namely hierarchical social network-pachinko allocation model (HSN-PAM), for discovering probabilistic, hierarchical communities in social  ...  The experimental results demonstrate that HSN-PAM is effective for discovering hierarchical community structures in large-scale social networks.  ...  This paper describes a hierarchical Bayesian model based scheme, namely HSN-PAM, for discovering probabilistic, hierarchical communities in social networks.  ... 
doi:10.1504/ijdmmm.2010.032144 fatcat:qb3ivs23xzfbnmysingvsggwom

Learning the Latent State Space of Time-Varying Graphs [article]

Nesreen K. Ahmed, Christopher Cole, Jennifer Neville
2014 arXiv   pre-print
In this work, we develop a framework for learning the latent state space of a time-varying email graph.  ...  Within the framework, we compare two different representations of the temporal relationships; discrete vs. probabilistic.  ...  Probabilistic Model: In this representation, we consider the graph G t at time t, as a probabilistic graph that contains any edge e t ′ = (u i , v j ) that occurs at any time t ′ such that t ′ ≤ t, and  ... 
arXiv:1403.3707v1 fatcat:wv3mbwshljgbrc5et2hpl5olwu

Latent Space Temporal Model of Microbial Abundance to Predict Domination and Bacteremia [article]

Ruiqi Zhong, Tyler Joseph, Joao B Xavier, Itsik Pe'er
2018 arXiv   pre-print
In this paper, we introduce a probabilistic model for the dynamics of intestinal microbiomes that takes into account interaction among bacteria as well as external effects such as antibiotics.  ...  We further leverage this framework to validate known links between antibiotics and clinical outcomes, while discovering new ones.  ...  Here we develop a probabilistic time-series model of microbiome community dynamics.  ... 
arXiv:1808.10795v1 fatcat:yqw2asancnhi3njnemo25eqyhy

Correlated Topic Model for Web Services Ranking

Mustapha AZNAG, Mohamed QUAFAFOU, Zahi JARIR
2013 International Journal of Advanced Computer Science and Applications  
In this paper, we explore several probabilistic topic models: Probabilistic Latent Semantic Analysis (PLSA), Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) to extract latent factors  ...  To address the limitation of keywords-based queries, we represent web service description as a vector space and we introduce a new approach for discovering and ranking web services using latent factors  ...  Future work will focus on developing a new probabilistic topic model which will able to tag web services automatically.  ... 
doi:10.14569/ijacsa.2013.040637 fatcat:odopot6tzvgp5hkbsd44p56mj4

Using content and interactions for discovering communities in social networks

Mrinmaya Sachan, Danish Contractor, Tanveer A. Faruquie, L. Venkata Subramaniam
2012 Proceedings of the 21st international conference on World Wide Web - WWW '12  
We propose generative models that can discover communities based on the discussed topics, interaction types and the social connections among people.  ...  In our models a person can belong to multiple communities and a community can participate in multiple topics.  ...  Then the fuzzy modularity Q f is defined as: Q f = 1 2m n k=1 x,y∈S k ( ρ k (x) + ρ k (y) 2 e(x, y) − p E f (x, y)) where p E f (x, y) is the expected probabilistic number of edge e(x, y) with the form  ... 
doi:10.1145/2187836.2187882 dblp:conf/www/SachanCFS12 fatcat:65hzmtchobbunbwtdbka2hkw6m

Clustering for probabilistic model estimation for CF

Qing Li, Byeong Man Kim, Sung Hyon Myaeng
2005 Special interest tracks and posters of the 14th international conference on World Wide Web - WWW '05  
community satisfy a Gaussian distribution, we propose a method of probabilistic model estimation for CF, where objects (user or items) are classified into groups based on the content information and ratings  ...  Observing the fact that in user-based CF each user community is characterized by a Gaussian distribution on the ratings for each item and the fact that in itembased CF the ratings of each user in item  ...  Observing the fact that in user-based CF each user community where users share similar preferences is characterized by a Gaussian distribution on the ratings for each item, we propose a probabilistic model  ... 
doi:10.1145/1062745.1062890 dblp:conf/www/LiKM05 fatcat:paammwwcmvcw7i5is2hsqfppja

An Integrated Model for User Attribute Discovery: A Case Study on Political Affiliation Identification [chapter]

Swapna Gottipati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
2014 Lecture Notes in Computer Science  
Our model achieved an accuracy of 70.1% for user party detection task.  ...  Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks.  ...  Modular communities and nodes in the middle represents small individual communities that are misaligned. Fig. 3 : 3 Our probabilistic matrix factorization model on user stance and social behaviors.  ... 
doi:10.1007/978-3-319-06608-0_36 fatcat:o6xkausj3fg5ngwq2mkimbjpna

Cognitive Amplifier for Internet of Things [article]

Bing Huang, Athman Bouguettaya, Azadeh Ghari Neiat
2020 arXiv   pre-print
The prediction component takes the discovered knowledge as the base for inferring what, when, and where the next activity will happen.  ...  We present a Cognitive Amplifier framework to augment things part of an IoT, with cognitive capabilities for the purpose of improving life convenience.  ...  The knowledge discovery component focuses on mining periodic probabilistic composition patterns and temporal relationships among composition patterns.  ... 
arXiv:2005.06914v1 fatcat:j2u3z4ultnfxrf2e22n7pbmlhi

Probabilistic analysis of an anonymity system

Vitaly Shmatikov, S. Schneider
2004 Journal of Computer Security  
We use the probabilistic model checker PRISM to analyze the Crowds system for anonymous Web browsing.  ...  This case study demonstrates how probabilistic model checking techniques can be used to formally analyze security properties of a peer-to-peer group communication system based on random message routing  ...  It was discovered by automated analysis. Formal Model of Crowds In this section, we describe our probabilistic formal model of the Crowds system.  ... 
doi:10.3233/jcs-2004-123-403 fatcat:coxuyffi5ngvtgwpvhssctgcwa

Mining blog stories using community-based and temporal clustering

Arun Qamra, Belle Tseng, Edward Y. Chang
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
We propose a Content-Community-Time model that can leverage the content of entries, their timestamps, and the community structure of the blogs, to automatically discover stories.  ...  Doing so also allows us to discover hot stories. We demonstrate the effectiveness of our model through several case studies using real-world data collected from the blogosphere.  ...  Probabilistic Models for Content and Relation Analysis Probabilistic models have become popular for text analysis.  ... 
doi:10.1145/1183614.1183627 dblp:conf/cikm/QamraTC06 fatcat:3ap4znr2braethsg4kobyke4am

SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks

Berny Carrera, Jae-Yoon Jung
2018 Sustainability  
A probabilistic dissemination of information among user communities is reflected after discovering topics and sentiments from the user comments.  ...  In this paper, we present a probabilistic approach to discover an information diffusion process based on an extended hidden Markov model (HMM) by analyzing the users and comments from posts on social media  ...  In this research, a new semantic hidden Markov model (HMM) for discovering information diffusion, named SentiFlow, is introduced to discover probabilistic information flow in consideration of topics and  ... 
doi:10.3390/su10082731 fatcat:h2nldirprvhyfay2vy3xi67oqm

Access Gateway Discovery and Selection in Hybrid Multihop Relay Vehicular Network

Shang-Pin Sheng, Ben-Yue Chang, Hung-Yu Wei
2008 2008 IEEE Asia-Pacific Services Computing Conference  
Vehicular ad hoc network protocol with hybrid relay architecture is proposed for improving the success ratio.  ...  Access Gateway Discovery mechanisms and Access Gateway Selection scheme have been shown effective by the significant improvement of success ratio in NS-2 simulation based on realistic vehicular mobility models  ...  E.  ... 
doi:10.1109/apscc.2008.63 dblp:conf/apscc/ShengCW08 fatcat:gxupipfplfhudeasygeu5bkrpq
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