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Survey on graph embeddings and their applications to machine learning problems on graphs
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.
Journal Article
Understanding criminal networks : a research guide
\"Understanding Criminal Networks is a short methodological primer for those interested in studying illicit, deviant, covert, or criminal networks using social network analysis (SNA). Accessibly written by Gisela Bichler, a leading expert in SNA for dark networks, the book is chock-full of graphics, checklists, software tips, step-by-step guidance, and straightforward advice. Covering all the essentials, each chapter highlights three themes: the theoretical basis of networked criminology; methodological issues and useful analytic tools; and producing professional analysis. Unlike any other book on the market, the book combines conceptual and empirical work with advice on designing networking studies, collecting data, and analysis. Relevant, practical, theoretical, and methodologically innovative, Understanding Criminal Networks promises to jumpstart readers' understanding of how to cross over from conventional investigations of crime to the study of criminal networks\"-- Provided by publisher.
Book
The expanding horizons of network neuroscience: From description to prediction and control
•Network neuroscience combines elements from many disciplines.•We discuss three key areas of inquiry: descriptive, predictive, and perturbative.•Descriptive approaches employ advanced tools from graph theory.•Predictive approaches employ machine learning to predict behavior from features.•Perturbative approaches employ network control theory to explain system energetics.
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives—including machine learning and systems engineering—that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
Journal Article
Security without obscurity : a guide to cryptographic architectures
Information security has a major gap when cryptography is implemented. Cryptographic algorithms are well defined, key management schemes are well known, but the actual deployment is typically overlooked, ignored, or unknown. Cryptography is everywhere. Application and network architectures are typically well-documented but the cryptographic architecture is missing. This book provides a guide to discovering, documenting, and validating cryptographic architectures. Each chapter builds on the next to present information in a sequential process. This approach not only presents the material in a structured manner, it also serves as an ongoing reference guide for future use-- Provided by the publisher.
Book
Social cognitive network neuroscience
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks—collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach—which leverages methods from the field of network neuroscience and graph theory—can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
Journal Article
Bruno Latour in pieces : an intellectual biography
\"Bruno Latour stirs things up. Latour began as a lover of science and technology, co-founder of actor-network theory, and philosopher of a modernity that had \"never been modern.\" In the meantime he is regarded not just as one of the most intelligent and also popular exponents of science studies but also as a major innovator of the social sciences, an exemplary wanderer who walks the line between the sciences and the humanities. This book provides the first comprehensive overview of the Latourian oeuvre, from his early anthropological studies in Abidjan (Ivory Coast), to influential books like Laboratory Life and Science in Action, and his most recent reflections on an empirical metaphysics of \"modes of existence.\" In the course of this enquiry it becomes clear that the basic problem to which Latour's work responds is that of social tradition, the transmission of experience and knowledge. What this empirical philosopher constantly grapples with is the complex relationship of knowledge, time, and culture\"-- Provided by publisher.
Book
Fusion of text and graph information for machine learning problems on networks
Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.
Journal Article
Social media, parties, and political inequalities
\"How have social media transformed politics in Western democracies? This book examines this question focusing on the power balance between and within parties in the Netherlands from a comparative perspective. Jacobs and Spierings also investigates topics such as local/European politics and the impact on women and ethnic-minorities\"-- Provided by publisher.
Book
Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media
Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets’ structure (in Latin forma mentis ) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals’ opinions at the beginning of posts. “Gender” and “gap” elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of “woman” highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science “sacrificing personal skills for success”. The semantic frame of “man” highlighted awareness of the myth of male superiority in science. No anger was detected around “person”, suggesting that tweets got less tense around genderless terms. No stereotypical perception of “scientist” was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users’ replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.
Journal Article