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Probabilistic models for discovering e-communities
2006
Proceedings of the 15th international conference on World Wide Web - WWW '06
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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
2012
Proceedings of the Fifth Workshop on Social Network Systems - SNS '12
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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
2007
Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)
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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
2010
International Journal of Data Mining Modelling and Management
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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]
2014
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2013
International Journal of Advanced Computer Science and Applications
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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
2012
Proceedings of the 21st international conference on World Wide Web - WWW '12
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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
2005
Special interest tracks and posters of the 14th international conference on World Wide Web - WWW '05
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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]
2014
Lecture Notes in Computer Science
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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]
2020
arXiv
pre-print
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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
2004
Journal of Computer Security
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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
2006
Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06
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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
2018
Sustainability
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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
2008
2008 IEEE Asia-Pacific Services Computing Conference
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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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