Використання векторних представлень графів для прогнозування зв'язків у Wikipedia

Link prediction is an important area of study in network analysis and graph theory which tries to answer the question of whether two nodes in the graph might have an association in the future. Nowadays, graphs are ubiquitously present in our lives (social networks, circuits, roads etc.), which is why the problem is crucial to the development of intelligent applications. In the past, there have been proposed methods of solving link prediction problem through algebraic formulations and heuristics, however, their expressive power and transferability fell short. Recently, graph embedding methods have risen to popularity because of their effectiveness and the ability to transfer knowledge between tasks. Inspired by the famous in machine learning and natural language processing research Word2Vec approach, these methods try to learn a distributed vector representation, called an embedding, of graph nodes. After that a binary classifier given a pair of embeddings predicts the probability of the existence of a link between the encoded nodes. In this paper, we review several graph embedding approaches for the problem of Wikipedia link prediction, namely Wikipedia2vec, Role2vec, AttentionWalk and Walkets. Wikipedia link prediction tries to find pages that should be interlinked due to some semantic relation. We evaluate prediction accuracy on a hold-out set of links and show which one proves to be better at mining associations between Wikipedia concepts. The results include qualitative (principal component analysis dimensionality reduction and visualization) and quantitative (accuracy) differences between the proposed methods. As a part of the conclusion, further research questions are provided, including new embedding architectures and the creation of a graph embedding algorithms benchmark.Прогнозування зв'язків є важливою областю дослідження в аналізі мереж та теорії графів, яка намагається відповісти на питання, чи можуть два вузли у графі в майбутньому мати зв'язок. На сьогоднішній день графи повсюдно присутні у нашому житті (соціальні мережі, електротехніка, дороги і т. д.), тому проблема має вирішальне значення для розвитку інтелектуальних додатків. У минулому були запропоновані методи вирішення задачі прогнозування зв'язків за допомогою алгебраїчних формулювань і евристик, однак їхня виразність і переносимість не були задовільними. Останнім часом методи побудови векторних представлень зросли у популярності через їх ефективність і здатність передавати знання між завданнями. Натхненний знаменитим в машинному навчанні та обробці природних мов дослідницьким підходом Word2Vec, ці методи намагаються вивчити розподілене векторне представлення. Після цього бінарний класифікатор, заданий парою таких векторів, прогнозує ймовірність існування зв'язку між закодованими вузлами. У даній роботі ми розглянемо декілька підходів до вбудовування графіків для проблеми прогнозування зв'язків у Wikipedia, а саме Wikipedia2vec, Role2vec, AttentionWalk та Walkets. Прогнозування посилань у контексті Wikipedia – це знаходження сторінок, які пов'язані через певні смислові відносини. Ми оцінюємо точність прогнозування на відокремленому наборі зв'язків і показуємо, який з методів краще знаходить асоціації між сутностями у Вікіпедії. Отримані результати включають якісні (метод головних компонентів для зменшення розмірності та візуалізації) і кількісні (точність) відмінності між запропонованими методами. У рамках висновку наводяться подальші дослідницькі питання, включаючи нові архітектури побудови векторних представлень та створення загальноприйнятого тесту ефективності таких представлень

Cryptocurrency Constellations across the Three-Dimensional Space: Governance Decentralization, Security, and Scalability

In the post-Bitcoin era, many cryptocurrencies with a variety of goals and purposes have emerged in the digital arena. This article aims to map cryptocurrency protocols across three main defining dimensions, which are governance decentralization, security, and scalability. We theorize about the organizational and technological features that impact these three dimensions. Such features encompass roles permissiveness, validation network size, resource expenditure, and number of transactions per second. We map the different cryptocurrency constellations based on their consensus mechanisms, discussing the organizational and technological features of the various protocols applications and how they experience and play with the tradeoffs among governance decentralization, security, and scalability

Volunteer entry into hospital culture : relationships among socialization, P-O fit, organizational commitment, and job satisfaction.

This dissertation examines the entry of volunteers into the culture of hospitals paying particular attention to the relationships among organizational socialization tactics and the outcomes of person-organization fit (P-O fit), organizational commitment, and job satisfaction. Using a correlation study design, the researcher collected data from hospital volunteers in Western Kentucky. The survey was distributed to 230 volunteers at six different hospitals in Western Kentucky. Of the 230 volunteers who received the survey at various volunteer meetings, the researcher collected 180 usable surveys, yielding a 78.2% return rate. The investigation\u27s survey used items selected from three different scales measuring organization socialization tactics (Jones, 1986), organization commitment (Meyer, Allen & Smith, 1993), and volunteer satisfaction (Galindo-Kuhn & Guzley, 2001). Items measuring perceived P-O fit were modeled after the works of others (Cable & De Rue, 2002; Cable & Judge, 1996). The research question framing the investigation was the following: What impact do organization socialization activities have on volunteer (i.e., unpaid) organization member perceptions of P-O fit, organization commitment, and job satisfaction. An examination of research findings suggest when the hospitals in this investigation used collective, formal, investiture, sequential, and serial socialization tactics, a positive relationship existed between these institutionalized socialization tactics and volunteer perceptions of P-O fit, organization commitment, and job satisfaction. Hence, when these organizations provided socialization experiences in which new volunteers experienced common learning experiences, were separated from other organization members while learning their new role, confirmed volunteer values and characteristics, provided identifiable phases of learning, and allowed experienced volunteers to act as role models, these socialization tactics positively related to volunteer perceptions of value congruence (i.e., P-O fit), organization commitment, and job satisfaction

Graph Cellular Automata with Relation-Based Neighbourhoods of Cells for Complex Systems Modelling: A Case of Traffic Simulation

A complex system is a set of mutually interacting elements for which it is possible to construct a mathematical model. This article focuses on the cellular automata theory and the graph theory in order to compare various types of cellular automata and to analyse applications of graph structures together with cellular automata. It proposes a graph cellular automaton with a variable configuration of cells and relation-based neighbourhoods (r–GCA). The developed mechanism enables modelling of phenomena found in complex systems (e.g., transport networks, urban logistics, social networks) taking into account the interaction between the existing objects. As an implementation example, modelling of moving vehicles has been made and r–GCA was compared to the other cellular automata models simulating the road traffic and used in the computer simulation process