Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes

For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss, and/or 2) they identify groups of antagonistic voters, but not the context associated to their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset.Comment: Social Networks, 2020, 60, 83 - 10

COMMUNITY DETECTION IN GRAPHS

Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided

The citizen-user and the crowd-mediated politics of the Five Star Movement

This thesis described the trajectory of the Italy’s Five Star Movement (M5S, 2005- 2014) from the perspective of the citizens who, as Internet users, participated in in the political enterprise. Citizen-users, enabled and empowered by Internet and mobile technologies, shaped and sustained the identity and evolution of the movement that became the M5S. The case study selected for this research, the M5S, is exceptional due to the magnitude of its success; but its features (Internet-centered and fluid ideology) are becoming more common among political organisations in Western democracies. The goal of the thesis is to assess the impact of the Internet on the political process, through its connecting, mobilising, organising, and to characterise the shape of political talk among citizens. This is achieved by applying quantitative methods, including network analysis and natural language processing, on 10 years of user-generated data collected mainly from four sources: the blog of the Movement’s founder, the M5S official forum, Facebook and Meetup.com. The thesis finds that the online discussion fora fostered diversity without fragmentation, and contributed on at least one occasion to shape the policy agenda of the M5S. Furthermore, the meetups of the Movement maintained their capacity to attract and mobilise users, and their territorial distribution clearly correlate with local results of the M5S in two elections, suggesting a positive impact of Internet-enabled mobilisation. Finally, given the votes received in the 2013 general election, the political communication generated over the Internet offset the low attention dedicated by TV news broadcast to the Movement during the electoral campaign. As Internet and mobile technologies are routinised, it is easy to see how their importance in political organisation and deliberation will grow. By studying the application of ICTs in the case of the M5S, this thesis offers insights into their use in practice, as well as pointing to possible democratic risks if online deliberation is non controlled to guarantee its fairness and openness but instead steered by the leadership, turning a deliberating community of citizen-users into a noisy crowd

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Multimodal biometrics score level fusion using non-confidence information

Multimodal biometrics refers to automatic authentication methods that depend on multiple modalities of measurable physical characteristics. It alleviates most of the restrictions of single biometrics. To combine the multimodal biometrics scores, three different categories of fusion approaches including rule based, classification based and density based approaches are available. When choosing an approach, one has to consider not only the fusion performance, but also system requirements and other circumstances. In the context of verification, classification errors arise from samples in the overlapping region (or non- confidence region) between genuine users and impostors. In score space, a further separation of the samples outside the non-confidence region does not result in further verification improvements. Therefore, information contained in the non-confidence region might be useful for improving the fusion process. Up to this point, no attempts are reported in the literature that tries to enhance the fusion process using this additional information. In this work, the use of this information is explored in rule based and density based approaches mentioned above

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

Evolving Bitcoin Custody

The broad topic of this thesis is the design and analysis of Bitcoin custody systems. Both the technology and threat landscape are evolving constantly. Therefore, custody systems, defence strategies, and risk models should be adaptive too. We introduce Bitcoin custody by describing the different types, design principles, phases and functions of custody systems. We review the technology stack of these systems and focus on the fundamentals; key-management and privacy. We present a perspective we call the systems view. It is an attempt to capture the full complexity of a custody system, including technology, people, and processes. We review existing custody systems and standards. We explore Bitcoin covenants. This is a mechanism to enforce constraints on transaction sequences. Although previous work has proposed how to construct and apply Bitcoin covenants, these require modifying the consensus rules of Bitcoin, a notoriously difficult task. We introduce the first detailed exposition and security analysis of a deleted-key covenant protocol, which is compatible with current consensus rules. We demonstrate a range of security models for deleted-key covenants which seem practical, in particular, when applied in autonomous (user-controlled) custody systems. We conclude with a comparative analysis with previous proposals. Covenants are often proclaimed to be an important primitive for custody systems, but no complete design has been proposed to validate that claim. To address this, we propose an autonomous custody system called Ajolote which uses deleted-key covenants to enforce a vault sequence. We evaluate Ajolote with; a model of its state dynamics, a privacy analysis, and a risk model. We propose a threat model for custody systems which captures a realistic attacker for a system with offline devices and user-verification. We perform ceremony analysis to construct the risk model.Comment: PhD thesi