Many relational domains contain temporal information and dynamics that are important to model (e.g., social networks, protein networks). However, past work in relational learning has focused primarily on modeling static “snapshots” of the data and has largely ignored the temporal dimension of these data. In this work, we extend relational techniques to temporally-evolving domains and outline a representational framework that is capable of modeling both temporal and relational dependencies in the data. We develop efficient learning and inference techniques within the framework by considering a restricted set of temporalrelational dependencies and using parameter-tying methods to generalize across relationships and entities. More specifically, we model dynamic relational data with a twophase process, first summarizing the temporal-relational information with kernel smoothing, and then moderating attribute dependencies with the summarized relational information. We develop a number of novel temporal-relational models using the framework and then show that the current approaches to modeling static relational data are special cases within the framework. We compare the new models to the competing static relational methods on three real-world datasets and show that the temporal-relational models consistently outperform the relational models that ignore temporal information—achieving significant reductions in error ranging from 15 % to 70%. 1

We propose the Schema-Model Framework, which characterizes algorithms that learn probabilistic models from relational data as having two parts: a schema that identifies sets of related data items and groups them into relevant categories; and a model that allows probabilistic inference about those data items. The framework

Many data sets routinely captured by organizations are relational in nature - from marketing and sales transactions, to scientific observations and medical records. Relational data record characteristics of heterogeneous objects and persistent relationships among those objects (e.g., citation graphs, the World Wide Web, genomic structures). These data offer unique opportunities to improve model accuracy, and thereby decision-making, if machine learning techniques can effectively exploit the relational information. This work focuses on how to learn accurate statistical models of complex, relational data sets and develops two novel probabilistic models to represent, learn, and reason about statistical dependencies in these data. Relational dependency networks are the first relational model capable of learning general autocorrelation dependencies, an important class of statistical dependencies that are ubiquitous in relational data. Latent group models are the first relational model to generalize about the properties of underlying group structures to improve inference accuracy and efficiency. Not only do these two models offer performance gains over current relational models, but they also offer efficiency gains which will make relational modeling feasible for large, relational datasets where current methods are computationally intensive, if not intractable. We also formulate of a novel analysis framework to analyze relational model performance and ascribe errors to model learning and inference procedures. Within this framework, we explore the effects of data characteristics and representation choices on inference accuracy and investigate the mechanisms behind model performance. In particular, we show that the inference process in relational models can be a significant source of error and that relative model performance varies significantly across different types of relational data.

We describe an application of relational knowledge discovery to a key regulatory mission of the National Association of Securities Dealers (NASD). NASD is the world’s largest private-sector securities regulator, with responsibility for preventing and discovering misconduct among securities brokers. Our goal was to help focus NASD’s limited regulatory resources on the brokers who are most likely to engage in securities violations. Using statistical relational learning algorithms, we developed models that rank brokers with respect to the probability that they would commit a serious violation of securities regulations in the near future. Our models incorporate organizational relationships among brokers (e.g., past coworker), which domain experts consider important but have not been easily used before now. The learned models were subjected to an extensive evaluation using more than 18 months of data unseen by the model developers and comprising over two person weeks of effort by NASD staff. Model predictions were found to correlate highly with the subjective evaluations of experienced NASD examiners. Furthermore, in all performance measures, our models performed as well as or better than the handcrafted rules that are currently in use at NASD.

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