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Overlapping Communities and Roles in Networks with Node Attributes: probabilistic graphical Modeling, Bayesian Formulation and Variational Inference 31

Overlapping Communities and Roles in Networks with Node Attr...

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31st international Joint conference on Artificial Intelligence (IJCAI)

作者： Costa, Gianni Ortale, Riccardo ICAR CNR Arcavacata Di Rende Italy

ISBN: (纸本)9781956792003

We study the seamless integration of community discovery and behavioral role analysis, in the domain of networks with node attributes. In particular, we focus on unifying the two tasks, by explicitly harnessing node attributes and behavioral role patterns in a principled manner. To this end, we propose two Bayesian probabilistic generative models of networks, whose novelty consists in the interrelationship of overlapping communities, roles, their behavioral patterns and node attributes. The devised models allow for a variety of exploratory, descriptive and predictive tasks. These are carried out through mean-field variational inference, which is in turn mathematically derived and implemented into a coordinate-ascent algorithm. A wide spectrum of experiments is designed, to validate the devised models against three classes of state-of-the-art competitors using various real-world benchmark data sets from different social networking services.

关键词： Bayesian networks

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Developing probabilistic models for Identifying Semantic Patterns in Texts

Developing Probabilistic Models for Identifying Semantic Pat...

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5th Annual IEEE international conference on Semantic Computing (ICSC)

作者： Huang, Minhua Haralick, Robert M. CUNY Grad Sch Dept Comp Sci New York NY 10016 USA CUNY Univ Ctr New York NY 10016 USA

ISBN: (纸本)9780769544922

We present a probabilistic graphical model that finds a sequence of optimal categories for a sequence of input symbols. Based on this mode, three algorithms are developed for identifying semantic patterns in texts. They are the algorithm for extracting semantic arguments of a verb, the algorithm for classifying the sense of an ambiguous word, and the algorithm for identifying noun phrases from a sentence. Experiments conducted on standard data sets show good results. For example, our method achieves an average precision of 92.96% and an average recall of 94.94% for extracting semantic argument boundaries of verbs on WSJ data from Penn Treebank and PropBank;an average accuracy of 81.12% for recognizing the six sense word 'line';and an average precision of 97.7% and an average recall of 98.8% for recognizing noun phrases on WSJ data from Penn Treebank.

关键词： Accuracy Classification algorithms Educational institutions graphical models Hidden Markov models probabilistic logic Semantics

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Guided Open Story Generation Using probabilistic graphical models 19

Guided Open Story Generation Using Probabilistic Graphical M...

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14th international conference on the Foundations of Digital Games (FDG)

作者： Gandhi, Sagar Harrison, Brent Univ Kentucky Lexington KY 40506 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781450372176

In this work, we present an approach for performing computational storytelling in open domain based on Author Goals. Author Goals are constraints placed on a story event directed by the author of the system. There are two challenges present in this type of story generation: (1) automatically acquiring a model of story progression, and (2) guiding the progress of story progression in light of different goals. We propose a novel approach to story generation based on probabilistic graphical models and Loopy Belief Propagation (LBP) that addresses both of these problems. We show the applicability of our technique through a case study on the Visual Storytelling (VIST) 2017 dataset. We use image descriptions as author goals. This empirical analysis suggests that our approach is able to utilize goals information to better automatically generate stories.

关键词： Belief Propagation Computational Storytelling Natural Language Generation probabilistic graphical models

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STRUDEL: Learning Structured-Decomposable probabilistic Circuits 10

STRUDEL: Learning Structured-Decomposable Probabilistic Circ...

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10th international conference on probabilistic graphical models (PGM)

作者： Dang, Meihua Vergari, Antonio Van den Broeck, Guy Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90024 USA

probabilistic circuits (PCs) represent a probability distribution as a computational graph. Enforcing structural properties on these graphs guarantees that several inference scenarios become tractable. Among these properties, structured decomposability is a particularly appealing one: it enables the efficient and exact computations of the probability of complex logical formulas, and can be used to reason about the expected output of certain predictive models under missing data. This paper proposes Strudel, a simple, fast and accurate learning algorithm for structured-decomposable PCs. Compared to prior work for learning structured-decomposable PCs, Strudel delivers more accurate single PC models in fewer iterations, and dramatically scales learning when building ensembles of PCs. It achieves this scalability by exploiting another structural property of PCs, called determinism, and by sharing the same computational graph across mixture components. We showthese advantages on standard density estimation benchmarks and challenging inference scenarios.

关键词： probabilistic circuits structure learning structured decomposability

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A hybrid random field model for scalable statistical learning

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NEURAL NETWORKS 2009年第5-6期22卷 603-613页

作者： Freno, A. Trentin, E. Gori, M. Univ Siena Dipartimento Ingn Informaz I-53100 Siena SI Italy

This paper introduces hybrid random fields, which are a class of probabilistic graphical models aimed at allowing for efficient Structure learning in high-dimensional domains. Hybrid random fields, along with the learning algorithm we develop for them, are especially useful as a pseudo-likelihood estimation technique (rather than a technique for estimating strict joint probability distributions). In order to assess the generality of the proposed model, we prove that the class of pseudo-likelihood distributions representable by hybrid random fields Strictly includes the class of joint probability distributions representable by Bayesian networks. Once we establish this result, we develop a scalable algorithm for learning the Structure of hybrid random fields, which we call 'Markov Blanket Merging'. On the one hand, we characterize some complexity properties of Markov Blanket Merging both from a theoretical and from the experimental point of view, using a series of synthetic benchmarks. On the other hand. we evaluate the accuracy of hybrid random fields (as learned via Markov Blanket Merging) by comparing them to various alternative statistical models in a number of pattern classification and link-prediction applications. As the results show, learning hybrid random fields by the Markov Blanket Merging algorithm not only reduces significantly the computational Cost of structure learning with respect to several considered alternatives, but it also leads to models that are highly accurate as compared to the alternative ones. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Hybrid random fields Bayesian networks Markov random fields probabilistic graphical models Structure learning Pseudo-likelihood estimation

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Hawkesian graphical Event models 10

Hawkesian Graphical Event Models

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10th international conference on probabilistic graphical models (PGM)

作者： Yu, Xiufan Shanmugam, Karthikeyan Bhattacharjya, Debarun Gao, Tian Subramanian, Dharmashankar Xue, Lingzhou Penn State Univ University Pk PA 16802 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

graphical event models (GEMs) provide a framework for graphical representation of multivariate point processes. We propose a class of GEMs named Hawkesian graphical event models (HGEMs) for representing temporal dependencies among different types of events from either a single event stream or multiple independent streams. In our proposed model, the intensity function for an event label is a linear combination of time-shifted kernels where time shifts correspond to prior occurrences of causal event labels in the history, as in a Hawkes process. The number of parameters in our model scales linearly in the number of edges in the graphical model, enabling efficient estimation and inference. This is in contrast to many existing GEMs where the number of parameters scales exponentially in the edges. We use two types of kernels: exponential and Gaussian kernels, and propose a two-step algorithm that combines strengths of both kernels and learns the structure for the underlying graphical model. Experiments on both synthetic and real-world data demonstrate the efficacy of the proposed HGEM, and exhibit expressive power of the two-step learning algorithm in characterizing self-exciting event patterns and reflecting intrinsic Granger-causal relationships.

关键词： Asymptotic consistency Event streams Granger causality graphical event models Hawkes processes Point processes Temporal dependencies

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Document Image Matching Using probabilistic graphical models

Document Image Matching Using Probabilistic Graphical Models

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21st international conference on Pattern Recognition (ICPR)

作者： Liu, Li Lu, Yue Suen, Ching Y. East China Normal Univ Dept Comp Sci & Technol Shanghai Peoples R China Shanghai Res Inst SRI ECNU Joint Lab China Post Grp Shanghai Peoples R China Concordia Univ Ctr Pattern Recognit & Machine Intelligence Montreal PQ Canada

ISBN: (纸本)9784990644109;9781467322164

A document image matching approach making use of probabilistic graphical models is proposed. The document image is first represented by a tree with the nodes in the tree corresponding to the regions in the image and the edges indicating the parent-child relationships between them, transforming the problem to tree matching. A graphical model, i.e. pairwise Markov Random Field is defined on the tree, in which sense the nodes are considered as random variables and the edges encode the relations among these variables in the probability domain. The tree matching problem is then formulated as Maximum a Posterior (MAP) inference over the graphical model and solved by belief propagation. Since the underlying graphical model is tree-structured, the exact inference can be obtained. With properly defined potential functions in the joint probability represented by the graphical model, the disparity in tree representations caused by different image capturing conditions can be tolerated as demonstrated in the encouraging experimental results.

关键词： Belief propagation

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OpenMarkov, an Open-Source Tool for probabilistic graphical models 28

OpenMarkov, an Open-Source Tool for Probabilistic Graphical ...

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28th international Joint conference on Artificial Intelligence

作者： Arias, Manuel Perez-Martin, Jorge Luque, Manuel Diez, Francisco J. Univ Nacl Educ Distancia UNED Madrid Spain

ISBN: (纸本)9780999241141

OpenMarkov is a Java open-source tool for building and evaluating probabilistic graphical models, including Bayesian networks, influence diagrams, and some Markov models. With more than 100,000 lines of code, it offers some features for interactive learning, explanation of reasoning, and cost-effectiveness analysis that are not available in any other tool. OpenMarkov has been used in universities, research centers, and large companies in more than 30 countries on four continents. Several models, some of them for real-world medical applications, built with OpenMarkov, are publicly available on Internet.

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graphical models FOR RELATIONS Modeling Relational Context

GRAPHICAL MODELS FOR RELATIONS <i>Modeling Relational Contex...

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international conference on Knowledge Discovery and Information Retrieval

作者： Tresp, Volker Huang, Yi Jiang, Xueyan Rettinger, Achim Siemens AG Corp Technol Munich Germany Ludwig Maximilians Univ Munchen Munich Germany Karlsruhe Inst Technol Karlsruhe Germany

ISBN: (纸本)9789898425799

We derive a multinomial sampling model for analyzing the relationships between two or more entities, The parameters in the multinomial model are derived from factorizing multi-way contingency tables. We show how contextual information can be included and propose a graphical representation of model dependencies. The graphical representation allows us to decompose a multivariate domain into interactions involving only a small number of variables. The approach formulates a probabilistic generative model for a single relation. By construction, the approach can easily deal with missing relations. We apply our approach to a social network domain where we predict the event that a user watches a movie. Our approach permits the integration of both information about the last movie watched by a user and a general temporal preference for a movie.

关键词： Relational learning probabilistic relational models Relational context information Recommendation systems graphical models

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A TWO-WAY APPROACH FOR probabilistic graphical models STRUCTURE LEARNING AND ONTOLOGY ENRICHMENT

A TWO-WAY APPROACH FOR PROBABILISTIC GRAPHICAL MODELS STRUCT...

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international conference on Knowledge Engineering and Ontology Development

作者： Ben Ishak, Mouna Leray, Philippe Ben Amor, Nahla Univ Tunis LARODEC Lab ISG Tunis 2000 Tunisia Polytech Nantes Knowledge & Decis Team LINA Lab UMR 6241 Nantes France

ISBN: (纸本)9789898425805

Ontologies and probabilistic graphical models are considered within the most efficient frameworks in knowledge representation. Ontologies are the key concept in semantic technology whose use is increasingly prevalent by the computer science community. They provide a structured representation of knowledge characterized by its semantic richness. probabilistic graphical models (PGMs) are powerful tools for representing and reasoning under uncertainty. Nevertheless, both suffer from their building phase. It is well known that learning the structure of a PGM and automatic ontology enrichment are very hard problems. Therefore, several algorithms have been proposed for learning the PGMs structure from data and several others have led to automate the process of ontologies enrichment. However, there was not a real collaboration between these two research directions. In this work, we propose a two-way approach that allows PGMs and ontologies cooperation. More precisely, we propose to harness ontologies representation capabilities in order to enrich the building process of PGMs. We are in particular interested in object oriented Bayesian networks (OOBNs) which are an extension of standard Bayesian networks (BNs) using the object paradigm. We first generate a prior OOBN by morphing an ontology related to the problem under study and then, we describe how the learning process carried out with the OOBN might be a potential solution to enrich the ontology used initially.

关键词： Ontologies probabilistic graphical models (PGMs) Machine learning Ontology enrichment

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