A Bayesian alternative to mutual information for the hierarchical clustering of dependent random variables

The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering

Query on Knowledge Graphs with Hierarchical Relationships

The dramatic popularity of graph database has resulted in a growing interest in graph queries. Two major topics are included in graph queries. One is based on structural relationship to find meaningful results, such as subgraph pattern match and shortest-path query. The other one focuses on semantic-based query to find question answering from knowledge bases. However, most of these queries take knowledge graphs as flat forms and use only normal relationship to mine these graphs, which may lead to mistakes in the query results. In this thesis, we find hierarchical relationship in the knowledge on their semantic relations and make use of hierarchical relationship to query on knowledge graphs; and then we propose a meaningful query and its corresponding efficient query algorithm to get top-k answers on hierarchical knowledge graphs. We also design algorithms on distributed frameworks, which can improve its performance. To demonstrate the effectiveness and the efficiency of our algorithms, we use CISCO related products information that we crawled from official websites to do experiments on distributed frameworks

Dynamic Programming on Nominal Graphs

Many optimization problems can be naturally represented as (hyper) graphs, where vertices correspond to variables and edges to tasks, whose cost depends on the values of the adjacent variables. Capitalizing on the structure of the graph, suitable dynamic programming strategies can select certain orders of evaluation of the variables which guarantee to reach both an optimal solution and a minimal size of the tables computed in the optimization process. In this paper we introduce a simple algebraic specification with parallel composition and restriction whose terms up to structural axioms are the graphs mentioned above. In addition, free (unrestricted) vertices are labelled with variables, and the specification includes operations of name permutation with finite support. We show a correspondence between the well-known tree decompositions of graphs and our terms. If an axiom of scope extension is dropped, several (hierarchical) terms actually correspond to the same graph. A suitable graphical structure can be found, corresponding to every hierarchical term. Evaluating such a graphical structure in some target algebra yields a dynamic programming strategy. If the target algebra satisfies the scope extension axiom, then the result does not depend on the particular structure, but only on the original graph. We apply our approach to the parking optimization problem developed in the ASCENS e-mobility case study, in collaboration with Volkswagen. Dynamic programming evaluations are particularly interesting for autonomic systems, where actual behavior often consists of propagating local knowledge to obtain global knowledge and getting it back for local decisions.Comment: In Proceedings GaM 2015, arXiv:1504.0244

The use and value of hierarchical governance and modeling in infrastructure network planning

This paper discusses the seemingly inespacable tension between two dominant approaches to governance that are implemented with regard to the planning, design and development of infrastructure networks, such as roads and railways. Roughly, these approaches can be framed in two styles of governance, the hierarchical and the consensual style (Smits, 1995). In today’s (western) societies, hierarchical approaches to governance seem to become more and more obsolete. This is especially the case with regard to ´problematic situations´ that have a large spatial and environmental impact. For example, the planning, design and development of infrastructure networks have such a widespread, trans-sectoral impact, that a top down approach is considered to be no longer viable. The impact of large scale projects (such as the Betuwelijn, the development of the Tweede Maasvlakte or the High Speed Train Network) spreads into spatial, environmental, financial, legal, economic (with regard to exploitation and maintenance) and social aspects of everyday life. As a consequence, it is more and more to be considered as ´a normal procedure´ to at least consult the stakeholders concerned. The almost inevitable involvement of large groups of stakeholders with different characteristics cannot be achieved with hierarchical approaches to governance. Top down is assumed to be inappropriate and thus, un-called for in these types of policy processes. Following this widely accepted assumption, consensual approaches are designed and implemented, under various appealing banners, such as co-production, open planning processes and participatory policy making. These appealing names lead to innovative forms of interaction and participation of stakeholders. Stakeholders are enticed to participate in design workshops, brainstorms, coffee table talks, internet based discussions and surveys and market consultations. Stakeholders are invited to information centres and travelling exhibitions. All these efforts are undertaken based on the assumption that (this time) ´government will really listen and make effective use of all ideas, concerns and energy´. The question arises how effective and efficient these participatory efforts have been thus far. Is a consensual style of governance a solution for the ever increasing complexity of the impact of large scale infrastructural projects? Or has the hierarchical style still have some value for this type of policy processes? And if so, what kind value is this? And in addition, can hierarchical and consensual styles of governance simultaneously be helpful in planning, design and development of infrastructural networks, and if so, how and to what extend? Or must they be considered to be ´natural enemies´ with regard to designing and implementing policy processes? In this paper these questions will be addressed by assessing a (virtual) case study, the further advancement of the road infrastructure network around the city of Rotterdam (also known as the Rotterdamse Ruit). Subsequently we will discuss the two dominant styles of governance, the hierarchical and consensual style. Second, we will describe the role and value of hierarchical (top down) and consensual (bottom up) approaches in planning, designing and development of (road) infrastructural networks and projects. Third, we will make an attempt to combine both approaches, into a hybrid, cross over like, approach that incorporates both hierarchical and consensual approaches in governance. And fourth, we will apply (test) our hybrid, cross over approach to our (virtual) case study, thus proposing an approach for future governance to support the further advancement of the (road) infrastructure network in and around Rotterdam.

An Algebra of Hierarchical Graphs and its Application to Structural Encoding

We define an algebraic theory of hierarchical graphs, whose axioms characterise graph isomorphism: two terms are equated exactly when they represent the same graph. Our algebra can be understood as a high-level language for describing graphs with a node-sharing, embedding structure, and it is then well suited for defining graphical representations of software models where nesting and linking are key aspects. In particular, we propose the use of our graph formalism as a convenient way to describe configurations in process calculi equipped with inherently hierarchical features such as sessions, locations, transactions, membranes or ambients. The graph syntax can be seen as an intermediate representation language, that facilitates the encodings of algebraic specifications, since it provides primitives for nesting, name restriction and parallel composition. In addition, proving soundness and correctness of an encoding (i.e. proving that structurally equivalent processes are mapped to isomorphic graphs) becomes easier as it can be done by induction over the graph syntax

A Statistical Model of Abstention under Compulsory Voting

Invalid voting and electoral absenteeism are two important sources of abstention in compulsory voting systems. Previous studies in this area have not considered the correlation between both variables and ignored the compositional nature of the data, potentially leading to unfeasible results and discarding helpful information from an inferential standpoint. In order to overcome these problems, this paper develops a statistical model that accounts for the compositional and hierarchical structure of the data and addresses robustness concerns raised by the use of small samples that are typical in the literature. The model is applied to analyze invalid voting and electoral absenteeism in Brazilian legislative elections between 1945 and 2006 via MCMC simulations. The results show considerable differences in the determinants of both forms of non-voting; while invalid voting was strongly positively related both to political protest and to the existence of important informational barriers to voting, the influence of these variables on absenteeism is less evident. Comparisons based on posterior simulations indicate that the model developed in this paper fits the dataset better than several alternative modeling approaches and leads to different substantive conclusions regarding the effect of different predictors on the both sources of abstention

XML document design via GN-DTD

Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples