22,374 research outputs found
Transforming Graph Representations for Statistical Relational Learning
Relational data representations have become an increasingly important topic
due to the recent proliferation of network datasets (e.g., social, biological,
information networks) and a corresponding increase in the application of
statistical relational learning (SRL) algorithms to these domains. In this
article, we examine a range of representation issues for graph-based relational
data. Since the choice of relational data representation for the nodes, links,
and features can dramatically affect the capabilities of SRL algorithms, we
survey approaches and opportunities for relational representation
transformation designed to improve the performance of these algorithms. This
leads us to introduce an intuitive taxonomy for data representation
transformations in relational domains that incorporates link transformation and
node transformation as symmetric representation tasks. In particular, the
transformation tasks for both nodes and links include (i) predicting their
existence, (ii) predicting their label or type, (iii) estimating their weight
or importance, and (iv) systematically constructing their relevant features. We
motivate our taxonomy through detailed examples and use it to survey and
compare competing approaches for each of these tasks. We also discuss general
conditions for transforming links, nodes, and features. Finally, we highlight
challenges that remain to be addressed
A Primer on Causality in Data Science
Many questions in Data Science are fundamentally causal in that our objective
is to learn the effect of some exposure, randomized or not, on an outcome
interest. Even studies that are seemingly non-causal, such as those with the
goal of prediction or prevalence estimation, have causal elements, including
differential censoring or measurement. As a result, we, as Data Scientists,
need to consider the underlying causal mechanisms that gave rise to the data,
rather than simply the pattern or association observed in those data. In this
work, we review the 'Causal Roadmap' of Petersen and van der Laan (2014) to
provide an introduction to some key concepts in causal inference. Similar to
other causal frameworks, the steps of the Roadmap include clearly stating the
scientific question, defining of the causal model, translating the scientific
question into a causal parameter, assessing the assumptions needed to express
the causal parameter as a statistical estimand, implementation of statistical
estimators including parametric and semi-parametric methods, and interpretation
of our findings. We believe that using such a framework in Data Science will
help to ensure that our statistical analyses are guided by the scientific
question driving our research, while avoiding over-interpreting our results. We
focus on the effect of an exposure occurring at a single time point and
highlight the use of targeted maximum likelihood estimation (TMLE) with Super
Learner.Comment: 26 pages (with references); 4 figure
The Multivariate k-Nearest Neighbor Model for Dependent Variables : One-Sided Estimation and Forecasting
This article gives the asymptotic properties of multivariate k-nearest neighbor regression estimators for dependent variables belonging to Rd, d > 1. The results derived here permit to provide consistent forecasts, and confidence intervals for time series An illustration of the method is given through the estimation of economic indicators used to compute the GDP with the bridge equations. An empirical forecast accuracy comparison is provided by comparing this non-parametric method with a parametric one based on ARIMA modelling that we consider as a benchmark because it is still often used in Central Banks to nowcast and forecast the GDP.Multivariate k-nearest neighbor, asymptotic normality of the regression, mixing time series, confidence intervals, forecasts, economic indicators, euro area.
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
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