A Method for Community Detection in Networks with Mixed Scale Features at Its Nodes
В рамках работы XVIII Апрельской международной научной конференции по проблемам развития экономики и общества 11–12 апреля 2017 г. в Выс- шей школе экономике прошла секция «Сетевой анализ». Уже третий год подряд данная секция собирает социологов, политологов, менеджеров, ма- тематиков, лингвистов и других представителей различных научных и при- кладных дисциплин, которые используют методологию сетевого анализа в своих исследовательских проектах. В этом году тематическое содержание секции отражало междисциплинарный состав участников. В ходе сессий исследователи обсудили развитие математических моделей, используемых в сетевом анализе; изучение сетей взаимодействия и коммуникационных сетей; подходы к оценке влияния, которое сеть оказывает на отдельные элементы; возможности выявления латентных связей и закономерностей; применение сетевого анализа для изучения сетей концептов.
В работе сессий приняли участие и выступили с докладами Е. В. Артю- хова, Г. В. Градосельская, М. Е. Ерофеева, Д. Г. Зайцев (все из НИУ ВШЭ), С. А. Исаев (Adidas), В. А. Калягин (НИУ ВШЭ — Нижний Новгород), И. А. Карпов (НИУ ВШЭ), А. П. Колданов (НИУ ВШЭ — Нижний Новго- род), И. И. Кузнецов (НИУ ВШЭ), С. В. Макрушин (Финансовый универ- ситет при Правительстве РФ), В. Д. Матвеенко (НИУ ВШЭ — Санкт- Петербург), А. А. Милёхина (НИУ ВШЭ), С. П. Моисеев (НИУ ВШЭ), Я. В. Пристли (НИУ ВШЭ), А. В. Семёнов (НИУ ВШЭ), И. Б. Смирнов (НИУ ВШЭ), Д. А. Харкина (НИУ ВШЭ — Санкт-Петербург), К. Фей (Школа биз- неса Университета Аалто — Aalto University School of Business), Ф. Лопес- Иттуриага (Вальядолидский университет — University of Valladolid)
We consider an application of long-range interaction centrality (LRIC) to the problem of the influence assessment in the global retail food network. Firstly, we reconstruct an initial graph into the graph of directed intensities based on individual node’s characteristics and possibility of the group influence. Secondly, we apply different models of the indirect influence estimation based on simple paths and random walks. This approach can help us to estimate node-to-node influence in networks. Finally, we aggregate node-to-node influence into the influence index. The model is applied to the food trade network based on the World International Trade Solution database. The results obtained for the global trade by different product commodities are compared with classical centrality measures.
Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks.
This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.
Using network approach, we propose a new method of identifying key food exporters based on the long-range (LRIC) and short-range interaction indices (SRIC). These indices allow to detect several groups of economies with direct as well as indirect influence on the routes of different levels in the food network.
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering in order to incorporate structural information into predictive model. Nowadays, a family of automated graph feature engineering techniques have 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 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 to 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 embedding and graph properties are connected to 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.
Trading processes is a vital part of human life and any unstable situation results in the change of living conditions of individuals. We study the power of each country in terms of produce trade. Trade relations between countries are represented as a network, where vertices are territories and edges are export flows. As flows of products between participants are heterogeneous we consider various groups of substitute goods (cereals, fish, vegetables). We detect key participants affecting food retail with the use of classical centrality measures. We also perform clustering procedure in order to find communities in networks.
Since 9/11, terrorism has become a global issue of the twenty-first century. Terrorist organizations become important actors of world politics as they gain influence on political process and decision-making. Some organizations compete with each other in order to gain more power and influence. We study the distribution of power among terrorist groups using network approach and applying classic and new centrality indices (Short-Range (SRIC) and Long-Range interactions indices (LRIC)). These indices allow to identify terrorist groups with direct and indirect influence on the terrorist network.