This paper employs a weighted network approach to study the empirical properties of the web of trade relationships among world countries, and its evolution over time. We show that most countries are characterized by weak trade links; yet, there exists a group of countries featuring a large number of strong relationships, thus hinting to a core-periphery structure. Also, better-connected countries tend to trade with poorly-connected ones, but are also involved in highly-interconnected trade clusters. Furthermore, rich countries display more intense trade links and are more clustered. Finally, all network properties are remarkably stable across the years and do not depend on the weighting procedure.

The Evolution of the World Trade Web. A Weighted-Network Analysis. 49.ma Riunione Scientifica Annuale. Società Italiana degli Economisti

Tactical match performance depends on the quality of actions of individual players or teams in space and time during match-play in order to be successful. Technological innovations have led to new possibilities to capture accurate spatio-temporal information of all players and unravel the dynamics and complexity of soccer matches. The main aim of this article is to give an overview of the current state of development of the analysis of position data in soccer. Based on the same single set of position data of a high-level 11 versus 11 match (Bayern Munich against FC Barcelona) three different promising approaches from the perspective of dynamic systems and neural networks will be presented: Tactical performance analysis revealed inter-player coordination, inter-team and inter-line coordination before critical events, as well as team-team interaction and compactness coefficients. This could lead to a multi-disciplinary discussion on match analyses in sport science and new avenues for theoretical and practical implications in soccer.

Current Approaches to Tactical Performance Analyses in Soccer Using Position Data

The multi-layer network nature of systemic risk and its implications for the costs of financial crises

While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

https://dl.acm.org/doi/pdf/10.1145/3186727

Graph Summarization Methods and Applications: A Survey

A review of vision-based systems for soccer video analysis

Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications.In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm COARSENET for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of COARSENET, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.

Fast influence-based coarsening for large networks

In order to make good strategies, soccer coaches analyze the archives of matches, which can be effectively considered as a set of trajectories. We can extract several useful information by analyzing the trajectories of moving objects, which consist of 22 players and a ball. Since each moving object interacts with others and produces a trajectory, its trajectory has a certain number of relationships with others, which are a basic type of information to make soccer strategies. In this paper, we propose a model to quantitatively express the performance of soccer players. This model is based on the relationships between trajectories of 22 players and a ball and allows to evaluate the performance of several players in quantitative way.

Trajectory Analysis for Soccer Players

The global financial crisis has reignited interest in models of crisis prediction. It has also raised the question whether financial interconnectedness—a possible source of systemic risk—can serve as an early warning indicator of crises. In this paper, we examine the ability of connectedness in the global network of financial linkages to predict systemic banking crises during the 1978–2010 period. Our results indicate that increases in a country’s own connectedness and decreases in its neighbours’ connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals. Our findings suggest that financial interconnectedness has early warning potential, especially for the 2007–2010 wave of systemic banking crises.

/pdf/does-financial-connectedness-predict-crises-2344nsov6u.pdf

Does financial connectedness predict crises

The global financial crisis has reignited interest in models of crisis prediction. It has also raised the question whether financial connectedness - a possible source of systemic risk - can serve as an early warning indicator of crises. In this paper we examine the ability of connectedness in the global network of financial linkages to predict systemic banking crises. Our results indicate that increases in a country's financial interconnectedness and decreases in its neighbors' connectedness are associated with a higher probability of banking crises after controlling for macroeconomic fundamentals.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2425400_code516363.pdf?abstractid=2326429&mirid=2

Does Financial Connectedness Predict Crises

Though centrality of vertices in social networks has been extensively studied, all past efforts assume that centrality of a vertex solely depends on the structural properties of graphs. However, with the emergence of online "semantic" social networks where vertices have properties (e.g. gender, age, and other demographic data) and edges are labeled with relationships (e.g. friend, follows) and weights (measuring the strength of a relationship), it is essential that we take semantics into account when measuring centrality. Moreover, the centrality of a vertex should be tied to a diffusive property in the network - a Twitter vertex may have high centrality w.r.t. jazz, but low centrality w.r.t. Republican politics. In this paper, we propose a new notion of diffusion centrality (DC) in which semantic aspects of the graph, as well as a diffusion model of how a diffusive property p is spreading, are used to characterize the centrality of vertices. We present a hyper graph based algorithm to compute DC and report on a prototype implementation and experiments showing how we can compute DCs (using real YouTube data) on social networks in a reasonable amount of time. We compare DC with classical centrality measures like degree, closeness, betweenness, eigenvector and stress centrality and show that in all cases, DC produces higher quality results. DC is also often faster to compute than both betweenness, closeness and stress centrality, but slower than degree and eigenvector centrality.

/pdf/diffusion-centrality-in-social-networks-53dymnxr1i.pdf

Chanhyun Kang

Papers

Fast influence-based coarsening for large networks

Trajectory Analysis for Soccer Players

Does financial connectedness predict crises

Does Financial Connectedness Predict Crises

Diffusion Centrality in Social Networks