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In this chapter, we discuss computational approaches to identify and rank lurkers in online social networks. We begin with a formal definition of topology-driven lurking and a detailed description of a family of centrality methods specifically conceived for ranking lurkers solely based on network topology, namely LurkerRank. To better model dynamics of user behaviors, the Time-Aware LurkerRank models are also described. The chapter ends with the description of a learning-to-rank framework for lurker prediction and classification.

The number of datasets available to legal practitioners, policy makers, scientists, and many other categories of citizens is growing at an unprecedented rate. Ethics-aware data processing has become a pressing need, considering that data are often used within critical decision processes (e.g., staff evaluation, college admission, criminal sentencing). The goal of this paper is to propose a vision for the injection of ethical principles (fairness, non-discrimination, transparency, data protection, diversity, and human interpretability of results) into the data analysis lifecycle (source selection, data integration, and knowledge extraction) so as to make them first-class requirements. In our vision, a comprehensive checklist of ethical desiderata for data protection and processing needs to be developed, along with methods and techniques to ensure and verify that these ethically motivated requirements and related legal norms are fulfilled throughout the data selection and exploration processes. Ethical requirements can then be enforced at all the steps of knowledge extraction through a unified data modeling and analysis methodology relying on appropriate conceptual and technical tools.

This SpringerBrief brings order to the wealth of research studies that contribute to shape our understanding of on-line social networks (OSNs) lurking phenomena. This brief also drives the development of computational approaches that can be effectively applied to answer questions related to lurking behaviors, as well as to the engagement of lurkers in OSNs. All large-scale online social networks (OSNs) are characterized by a participation inequality principle, i.e., the crowd of an OSN does not actively contribute, rather it takes on a silent role. Silent users are also referred to as lurkers, since they gain benefit from others' information without significantly giving back to the community. Nevertheless, lurkers acquire knowledge from the OSN, therefore a major goal is to encourage them to more actively participate. Lurking behavior analysis has been long studied in social science and human-computer interaction fields, but it has also matured over the last few years in social network analysis and mining. While the main target audience corresponds to computer, network, and web data scientists, this brief might also help increase the visibility of the topic by bridging different closely related research fields. Practitioners, researchers and students interested in social networks, web search, data mining, computational social science and human-computer interaction will also find this brief useful research material.

In this chapter, we describe the Lurker Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. A study carried out on different complex network models shows how the Lurker Game is suitable to model lurking dynamics, and how the adoption of rewarding mechanisms combined with the modeling of hypothetical heterogeneity of users’ interests may lead users in an online community towards a cooperative behavior.

This paper overviews recent research findings concerning a new challenging problem in information networks, namely identifying and ranking silent nodes. We present three case studies which show how silent nodes’ behavior maps to different situations in computer networks, online social networks, and online collaboration networks, and we discuss major benefits in identifying and ranking silent nodes in such networks. We also provide an overview of our proposed approach, which relies on a new eigenvector- centrality graph-based ranking method built on a silent-oriented network model.

International audience; The problem of local community detection in graphs refers to the identification of a community that is specific to a query node and relies on limited information about the network structure. Existing approaches for this problem are defined to work in dynamic network scenarios, however they are not designed to deal with complex real-world networks, in which multiple types of connectivity might be considered. In this work, we fill this gap in the literature by introducing the first framework for local community detection in multilayer networks (ML-LCD). We formalize the ML-LCD optimization problem and provide three definitions of the associated objective function, which correspond to different ways to incorporate within-layer and across-layer topo-logical features. We also exploit our framework to generate multilayer global community structures. We conduct an extensive experimentation using seven real-world multilayer networks, which also includes comparison with state-of-the-art methods for single-layer local community detection and for multilayer global community detection. Results show the significance of our proposed methods in discovering local communities over multiple layers, and also highlight their ability in producing global community structures that are better in modularity than those produced by native global community detection approaches .

Social interactions assume a central role in everyday life of people strongly connected through online social networks and web communities. Trust can be used as a filter to the increasing flow of information, experiences and opinions exchanged among users. In the absence of direct relations, trust inference algorithms can be exploited in order to estimate missing trust values. However, controversial situations can easily arise from the subjectivity of trust opinions, when the lack of agreement leads to ambiguity and uncertainty in the inferred trust. In this work, we propose a novel trust inference algorithm, named TrustZic, which aims at improving the performance of trust inference in controversial conditions by combining local and global trust aspects. The proposed approach is based on the use of global reputation values as node weights biasing a local trust inference process. Experimental evaluation has shown the significance of our approach and its effectiveness in supporting trust decisions in critical situations characterized by a high level of controversy.

The problem of node-centric, or local, community detection in information networks refers to the identification of a community for a given input node, having limited information about the network topology. Existing methods for solving this problem, however, are not conceived to work on complex networks. In this paper, we propose a novel framework for local community detection based on the multilayer network model. Our approach relies on the maximization of the ratio between the community internal connection density and the external connection density, according to multilayer similarity-based community relations. We also define a biasing scheme that allows the discovery of local communities characterized by different degrees of layer-coverage diversification. Experimental evaluation conducted on real-world multilayer networks has shown the significance of our approach., Accepted at 8th International Conference on Complex Networks (CompleNet'17)

Lurkers are silent members of a social network (SN) who gain benefit from others’ information without significantly giving back to the community. The study of lurking behaviors in SNs is nonetheless important, since these users acquire knowledge from the community, and as such they can be social capital holders. Within this view, a major goal is to delurk such users, i.e., to encourage them to more actively be involved in the SN. Despite the main strategies have been conceptualized in social science and human–computer interaction, no computational approach has been so far defined to turn lurkers into active participants in the SN. In this work we fill this gap by presenting a delurking-oriented targeted influence maximization problem under the linear threshold (LT) model. We define a novel objective function, in terms of the lurking scores associated with the nodes in the final active set, and we show it is monotone and submodular. We provide an approximate solution by developing a greedy algorithm, named DEvOTION, which computes a k-node set that maximizes the value of the delurking-capital-based objective function, for a given lurking threshold. Results on SN datasets of different sizes have demonstrated the significance of our delurking approach via LT-based targeted influence maximization. A comparative evaluation with state-of-the-art algorithms for non-targeted and targeted LT-based influence maximization has also shown the superiority of DEvOTION in terms of delurking capital that is obtained.

Summary Objective and subjects Goitre prevalence in school-age children is an indicator of the severity of iodine deficiency disorders (IDD) in an endemic area. The aims of the present study were (i) to provide ultrasound thyroid volume (TV) reference values in a healthy population of school-children aged 11–14 year living in iodine-sufficient areas of Calabria region (ii) to assess both goitre prevalence and urinary iodine (UI) concentration in all children aged 11–14 year from four mildly iodine-deficient areas in which we have carried out a program of salt iodization and (iii) to evaluate the efficacy of the iodoprophylaxis in an adult population living in a small village of the same endemic area. Design Cross-sectional and prospective studies. Methods TV was assessed by ultrasonography and iodine intake was estimated by measuring iodine excretion in spot urine samples. Results We provided the ultrasound normal reference values as a function of age and body surface area, which displayed significant differences from those recommended by the World Health Organization. By adopting local criteria, the prevalence of goitre in children ranged from 23·4% to 27·7% normalized for age and body surface area, respectively, while the UI excretion was

The increasing availability of text information coded in many different languages poses new challenges to modern information retrieval and mining systems in order to discover and exchange knowledge at a larger world-wide scale. The 1st International Workshop on Modeling, Learning and Mining for Cross/Multilinguality (dubbed MultiLingMine 2016) provides a venue to discuss research advances in cross-/multilingual related topics, focusing on new multidisciplinary research questions that have not been deeply investigated so far (e.g., in CLEF and related events relevant to CLIR). This includes theoretical and experimental on-going works about novel representation models, learning algorithms, and knowledge-based methodologies for emerging trends and applications, such as, e.g., cross-view cross-/multilingual information retrieval and document mining, (knowledge-based) translation-independent cross-/multilingual corpora, applications in social network contexts, and more.

6th Italian Information Retrieval Workshop, Cagliari, ITA, 25-/05/2015 - 26/05/2015; International audience; We present a transductive learning based framework for multilingual document classification, originally proposed in [7]. A key aspect in our approach is the use of a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. Results on real-world multilingual corpora have highlighted the superiority of the proposed document model against existing language-dependent representation approaches, and the significance of the transductive setting for multilingual document classification.

The purpose of this technical note is to introduce the problems of similarity detection and summarization in uncertain data. We provide the essential arguments that make the problems relevant to the data-mining and machine-learning community, stating major issues and summarizing our contributions in the field. Further challenges and directions of research are also issued. © 2014 Springer-Verlag.

An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of the primary data used to solve the problem. Most existing works on the topic have focused on a specific application domain (e.g., travel package recommendation), thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we propose a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for each package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. the target user's preferences, possibly including a limited budget. Our developed method has been tested on a TripAdvisor dataset and compared with a recently proposed method for learning composite recommendations.

Personalization is aimed at adapting content delivery to users’ profiles: namely, their expectations, preferences and requirements. This chapter surveys some well-known Web mining techniques that can be profitably exploited in order to address the problem of providing personalized access to the contents of Usenet communities. We provide a rationale for the inadequacy of current Usenet services, given the actual scenario in which an increasing number of users with heterogeneous interests look for information scattered over different communities. We discuss how the knowledge extracted from Usenet sites (from the content, the structure and the usability viewpoints) can be suitably adapted to the specific needs and expectations of each user.

Document disponible en ligne : http://sigir.org/files/forum/2011J/2011j_sigirforum_B_alexander.pdf; International audience; INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organiza- tions to compare their results. This paper reports on the INEX 2010 evaluation campaign, which consisted of a wide range of tracks: Ad Hoc, Book, Data Centric, Interactive, QA, Link the Wiki, Relevance Feedback, Web Service Discovery and XML Mining.

Handling microarray data is particularly challenging mainly due to the high dimensionality of such data, which demands for computer-aided methods, and to the intrinsic difficulty of devising notions of proximity between spots of array traps. In this paper, we propose a new approach to modeling the probe-level uncertainty in microarray data that allows for a more expressive representation of the data and a more accurate processing. This approach is essentially based on a recently proposed method for uncertain data clustering. This method lies in a centroid-linkage-based agglomerative hierarchical algorithm, named U-AHC, and an information-theoretic-based distance measure between uncertain data [8]. We have conducted experiments on four large microarray datasets, in order to assess effectiveness of the proposed clustering method. Experimental results have shown high quality results in terms of compactness of the clustering solutions.

The ability of providing a “standardized, extensible means of coupling semantic information within documents describing semistructured data” (Chaudhri, Rashid, & Zicari, 2003) has led to a steady growth of XML (extensible markup language) data sources, so that XML is touted as the driving force for representing and exchanging data on the Web. The motivation behind any clustering problem is to find an inherent structure of relationships in the data and expose this structure as a set of clusters where the objects within the same cluster are each to other highly similar but very dissimilar from objects in different clusters. The clustering problem finds in text databases a fruitful research area. Since today semistructured text data has become more prevalent on the Web, and XML is the de facto standard for such data, clustering XML documents has increasingly attracted great attention. Any application domain that needs organization of complex document structures (e.g., hierarchical structures with unbounded nesting, object-oriented hierarchies) as well as data containing a few structured fields together with some largely unstructured text components can be profitably assisted by an XML document clustering task.

In recent years there has been a growing interest in clustering uncertain data. In contrast to traditional, "sharp" data representation models, uncertain data objects can be represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In this context, the focus has been mainly on partitional and density-based approaches, whereas hierarchical clustering schemes have drawn less attention. We propose a centroid-linkage-based agglomerative hierarchical algorithm for clustering uncertain objects, named U-AHC. The cluster merging criterion is based on an information-theoretic measure to compute the distance between cluster prototypes. These prototypes are represented as mixture densities that summarize the pdfs of all the uncertain objects in the clusters. Experiments have shown that our method outperforms state-of-the-art clustering algorithms from an accuracy viewpoint while achieving reasonably good efficiency.

Preprocessing mass spectrometry (MS) data has been recognized as a crucial preliminary phase in order to perform data management and knowledge discovery tasks on mass spectra. The huge dimensionality and heterogeneity of MS data make mandatory the use of tools that are able to guide the user in the MS preprocessing task. However, most MS preprocessing tools are typically designed to perform only some preprocessing steps and are strictly coupled with MS data analysis modules. In this paper, we present mass spectra preprocessing tool (MSPtool), a user-friendly versatile tool for preprocessing MS data. MSPtool provides the user with a wide set of MS preprocessing steps by means of an easy-to-use graphical interface. Also, this tool has been embedded in a time-series-based framework for MS data clustering.

The PDF format represents the de facto standard for print-oriented documents. In this paper we address the problem of wrapping PDF documents, which raises new challenges in the information extraction field. The proposal is based on a novel bottom-up wrapping approach to extract information tokens and integrate them into groups related according to the logical structure of a document. A PDF wrapper is defined by specifying a set of group type definitions which impose a target structure to token groups containing the required information. Due to the intrinsic uncertainty on the structure and presentation of PDF documents, we devise constraints on token groupings as fuzzy logic conditions. We define a formal semantics for PDF wrappers and propose an algorithm for wrapper evaluation working in polynomial time with respect to the size of a PDF document.

Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitional clustering has been particularly studied and a number of algorithms have been developed. While existing proposals differ mainly in the notions of cluster centroid and clustering objective function, little attention has been given to an analysis of their characteristics and limits. In this work, we theoretically investigate major existing methods of partitional clustering, and alternatively propose a well-founded approach to clustering uncertain data based on a novel notion of cluster centroid. A cluster centroid is seen as an uncertain object defined in terms of a random variable whose realizations are derived based on all deterministic representations of the objects to be clustered. As demonstrated theoretically and experimentally, this allows for better representing a cluster of uncertain objects, thus supporting a consistently improved clustering performance while maintaining comparable efficiency with existing partitional clustering algorithms., Comment: VLDB2012

A main challenge in wrapping web data is to make wrappers robust w.r.t. variations in HTML sources, reducing human effort as much as possible. In this paper we develop a new approach to speed up the specification of robust wrappers, allowing the wrapper designer to not care about detailed definition of extraction rules. The key-idea is to enable a schema-based wrapping system to automatically generalize an original wrapper w.r.t. a set of example HTML documents. To accomplish this objective, we propose to exploit the notions of extraction rule and wrapper subsumption for computing a most general wrapper which still shares the extraction schema with the original wrapper, while maximizes the generalization of extraction rules w.r.t. the set of example documents.

EMNLP, Conference on Empirical Methods in Natural Language Processing , Doha, QAT, 25-/10/2014 - 29/10/2014; International audience; A major challenge in document clustering research arises from the growing amount of text data written in different languages. Previous approaches depend on language-specific solutions (e.g., bilingual dictionaries, sequential machine translation) to evaluate document similarities, and the required transformations may alter the original document semantics. To cope with this issue we propose a new document clustering approach for multilingual corpora that (i) exploits a large-scale multilingual knowledge base, (ii) takes advantage of the multi-topic nature of the text documents, and (iii) employs a tensor-based model to deal with high dimensionality and sparseness. Results have shown the significance of our approach and its better performance w.r.t. classic document clustering approaches, in both a balanced and an unbalanced corpus evaluation.

International audience; Multilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification.

Indexing sequence data is important in the context of Precision Medicine, where large amounts of "omics"data have to be daily collected and analyzed in order to categorize patients and identify the most effective therapies. Here we propose an algorithm for the computation of Burrows Wheeler transform relying on Big Data technologies, i.e., Apache Spark and Hadoop. Our approach is the first that distributes the index computation and not only the input dataset, allowing to fully benefit of the available cloud resources. Copyright © 2020 for this paper by its authors.

This chapter deals with data mining in uncertain XML data models, whose uncertainty typically comes from imprecise automatic processes. We first review the literature on modeling uncertain data, starting with well-studied relational models and moving then to their semistructured counterparts. We focus on a specific probabilistic XML model, which allows representing arbitrary finite distributions of XML documents, and has been extended to also allow continuous distributions of data values. We summarize previous work on querying this uncertain data model and show how to apply the corresponding techniques to several data mining tasks, exemplified through use cases on two running examples.