Donatella Firmani

Fast Enumeration of Large k-Plexes

K-plexes are a formal yet flexible way of defining communities in networks. They generalize the notion of cliques and are more appropriate in most real cases: while a node of a clique C is connected to all other nodes of C, a node of a k-plex may miss up to k connections. Unfortunately, computing all maximal k-plexes is a gruesome task and state-of-the-art algorithms can only process small-size networks. In this paper we propose a new approach for enumerating large k-plexes in networks that speeds up the search by several orders of magnitude, leveraging on (i) methods for strongly reducing the search space and (ii) efficient techniques for the computation of maximal cliques. Several experiments show that our strategy is effective and is able to increase the size of the networks for which the computation of large k-plexes is feasible from a few hundred to several hundred thousand nodes.

Towards Knowledge Discovery from the Vatican Secret Archives. In Codice Ratio - Episode 1: Machine Transcription of the Manuscripts.

In Codice Ratio is a research project to study tools and techniques for analyzing the contents of historical documents conserved in the Vatican Secret Archives (VSA). In this paper, we present our efforts to develop a system to support the transcription of medieval manuscripts. The goal is to provide paleographers with a tool to reduce their efforts in transcribing large volumes, as those stored in the VSA, producing good transcriptions for significant portions of the manuscripts. We propose an original approach based on character segmentation. Our solution is able to deal with the dirty segmentation that inevitably occurs in handwritten documents. We use a convolutional neural network to recognize characters, and statistical language models to compose word transcriptions. Our approach requires minimal training effort, making the transcription process more scalable, as the production of training sets requires a few pages and can be easily crowdsourced. We have conducted experiments on manuscripts from the Vatican Registers, an unreleased corpus containing the correspondence of the popes. With training data produced by 120 high school students, our system has been able to produce good transcriptions that can be used by paleographers as a solid basis to speedup the transcription process at a large scale.

Robust Entity Resolution using Random Graphs

Entity resolution (ER) seeks to identify which records in a data set refer to the same real-world entity. Given the diversity of ways in which entities can be represented, matched and distinguished, ER is known to be a challenging task for automated strategies, but relatively easier for expert humans. In our work, we abstract the knowledge of experts with the notion of a binary oracle. Our oracle can answer questions of the form do records u and v refer to the same entity? under a flexible error model, allowing for some questions to be more difficult to answer correctly than others. Our contribution is a general error correction tool that can be leveraged by a variety of hybrid-human machine ER algorithms, based on a formal way for selecting indirect control queries. In our experiments we demonstrate that correction-less ER algorithms equipped with our tool can perform even better than recent ER algorithms specifically designed for correcting errors. Our control queries are selected among those that provide strongest connectivity between records of each cluster, based on the concept ofgraph expanders (which are sparse graphs with formal connectivity properties). We give formal performance guarantees for our toolkit and provide experiments on real and synthetic data.