Mining and Analyzing the Italian Parliament: Party Structure and Evolution

The roll calls of the Italian Parliament in the XVI legislature are studied by employing multidimensional scaling, hierarchical clustering, and network analysis. In order to detect changes in voting behavior, the roll calls have been divided in seven periods of six months each. All the methods employed pointed out an increasing fragmentation of the political parties endorsing the previous government that culminated in its downfall. By using the concept of modularity at different resolution levels, we identify the community structure of Parliament and its evolution in each of the considered time periods. The analysis performed revealed as a valuable tool in detecting trends and drifts of Parliamentarians. It showed its effectiveness at identifying political parties and at providing insights on the temporal evolution of groups and their cohesiveness, without having at disposal any knowledge about political membership of Representatives.Comment: 27 pages, 14 figure

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Data Portraits and Intermediary Topics: Encouraging Exploration of Politically Diverse Profiles

In micro-blogging platforms, people connect and interact with others. However, due to cognitive biases, they tend to interact with like-minded people and read agreeable information only. Many efforts to make people connect with those who think differently have not worked well. In this paper, we hypothesize, first, that previous approaches have not worked because they have been direct -- they have tried to explicitly connect people with those having opposing views on sensitive issues. Second, that neither recommendation or presentation of information by themselves are enough to encourage behavioral change. We propose a platform that mixes a recommender algorithm and a visualization-based user interface to explore recommendations. It recommends politically diverse profiles in terms of distance of latent topics, and displays those recommendations in a visual representation of each user's personal content. We performed an "in the wild" evaluation of this platform, and found that people explored more recommendations when using a biased algorithm instead of ours. In line with our hypothesis, we also found that the mixture of our recommender algorithm and our user interface, allowed politically interested users to exhibit an unbiased exploration of the recommended profiles. Finally, our results contribute insights in two aspects: first, which individual differences are important when designing platforms aimed at behavioral change; and second, which algorithms and user interfaces should be mixed to help users avoid cognitive mechanisms that lead to biased behavior.Comment: 12 pages, 7 figures. To be presented at ACM Intelligent User Interfaces 201

A Survey on Visual Analytics of Social Media Data

The unprecedented availability of social media data offers substantial opportunities for data owners, system operators, solution providers, and end users to explore and understand social dynamics. However, the exponential growth in the volume, velocity, and variability of social media data prevents people from fully utilizing such data. Visual analytics, which is an emerging research direction, ha..

The Ubiquity of Large Graphs and Surprising Challenges of Graph Processing: Extended Survey

Graph processing is becoming increasingly prevalent across many application domains. In spite of this prevalence, there is little research about how graphs are actually used in practice. We performed an extensive study that consisted of an online survey of 89 users, a review of the mailing lists, source repositories, and whitepapers of a large suite of graph software products, and in-person interviews with 6 users and 2 developers of these products. Our online survey aimed at understanding: (i) the types of graphs users have; (ii) the graph computations users run; (iii) the types of graph software users use; and (iv) the major challenges users face when processing their graphs. We describe the participants' responses to our questions highlighting common patterns and challenges. Based on our interviews and survey of the rest of our sources, we were able to answer some new questions that were raised by participants' responses to our online survey and understand the specific applications that use graph data and software. Our study revealed surprising facts about graph processing in practice. In particular, real-world graphs represent a very diverse range of entities and are often very large, scalability and visualization are undeniably the most pressing challenges faced by participants, and data integration, recommendations, and fraud detection are very popular applications supported by existing graph software. We hope these findings can guide future research

An Ontology based Text-to-Picture Multimedia m-Learning System

Multimedia Text-to-Picture is the process of building mental representation from words associated with images. From the research aspect, multimedia instructional message items are illustrations of material using words and pictures that are designed to promote user realization. Illustrations can be presented in a static form such as images, symbols, icons, figures, tables, charts, and maps; or in a dynamic form such as animation, or video clips. Due to the intuitiveness and vividness of visual illustration, many text to picture systems have been proposed in the literature like, Word2Image, Chat with Illustrations, and many others as discussed in the literature review chapter of this thesis. However, we found that some common limitations exist in these systems, especially for the presented images. In fact, the retrieved materials are not fully suitable for educational purposes. Many of them are not context-based and didn’t take into consideration the need of learners (i.e., general purpose images). Manually finding the required pedagogic images to illustrate educational content for learners is inefficient and requires huge efforts, which is a very challenging task. In addition, the available learning systems that mine text based on keywords or sentences selection provide incomplete pedagogic illustrations. This is because words and their semantically related terms are not considered during the process of finding illustrations. In this dissertation, we propose new approaches based on the semantic conceptual graph and semantically distributed weights to mine optimal illustrations that match Arabic text in the children’s story domain. We combine these approaches with best keywords and sentences selection algorithms, in order to improve the retrieval of images matching the Arabic text. Our findings show significant improvements in modelling Arabic vocabulary with the most meaningful images and best coverage of the domain in discourse. We also develop a mobile Text-to-Picture System that has two novel features, which are (1) a conceptual graph visualization (CGV) and (2) a visual illustrative assessment. The CGV shows the relationship between terms associated with a picture. It enables the learners to discover the semantic links between Arabic terms and improve their understanding of Arabic vocabulary. The assessment component allows the instructor to automatically follow up the performance of learners. Our experiments demonstrate the efficiency of our multimedia text-to-picture system in enhancing the learners’ knowledge and boost their comprehension of Arabic vocabulary

A Comprehensive Bibliometric Analysis on Social Network Anonymization: Current Approaches and Future Directions

In recent decades, social network anonymization has become a crucial research field due to its pivotal role in preserving users' privacy. However, the high diversity of approaches introduced in relevant studies poses a challenge to gaining a profound understanding of the field. In response to this, the current study presents an exhaustive and well-structured bibliometric analysis of the social network anonymization field. To begin our research, related studies from the period of 2007-2022 were collected from the Scopus Database then pre-processed. Following this, the VOSviewer was used to visualize the network of authors' keywords. Subsequently, extensive statistical and network analyses were performed to identify the most prominent keywords and trending topics. Additionally, the application of co-word analysis through SciMAT and the Alluvial diagram allowed us to explore the themes of social network anonymization and scrutinize their evolution over time. These analyses culminated in an innovative taxonomy of the existing approaches and anticipation of potential trends in this domain. To the best of our knowledge, this is the first bibliometric analysis in the social network anonymization field, which offers a deeper understanding of the current state and an insightful roadmap for future research in this domain.Comment: 73 pages, 28 figure