We examine how the structure of the world trade network has been shaped by globalization and recessions over the last 40 years. We show that by treating the world trade network as an evolving system, theory predicts the trade network is more sensitive to evolutionary shocks and recovers more slowly from them now than i…
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169,341 papers · 148 categories
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48 results for “world network”
Highly accurate classification of network categories achieved.
problem Distinguishing between different types of networks (e.g., social vs. web graphs).
method Used a random forest classifier on both real-world and synthetic networks.
result Achieved a 94.2% classification accuracy.
Optimizes control interventions in real-world networks using deep-learning and network science.
problem Optimizing control over socioeconomic networks subject to constraints.
method Integrates optimization tools from deep-learning with network science.
result Characterizes vulnerability of corporate networks to takeovers.
DRL optimizes complex mobile networks with imperfect info.
problem Optimizing mobile networks with scarce data and complex dynamics.
method Sim-to-Real framework using graph CNN, domain randomization, multi-task learning, and self-play.
result First successful transfer of DRL from simulation to real-world mobile networks.
Analysis shows preference for Chinese yuan in global trade network.
problem Analyzing the preference of countries to trade in US dollars or Chinese yuan.
method Mathematical analysis of world trade network using Ising spin interactions.
result Majority of countries prefer trading in Chinese yuan due to network structure.
The study analyzes economic influence and contagion effects in the world network of economic activities.
problem Understanding economic influence and contagion effects in the global network of economic activities.
method Constructed a Google matrix of the world network of economic activities, analyzed using Markov chain transitions and ranking countries and sectors.
result Demonstrated striking increase in German economic activity influence and decrease in Eurozone influence over the period 1995-2009.
A new pruning method reduces DNN size and interconnectivity using brain network principles.
problem Over-parameterization in deep neural networks causes memory and hardware cost issues.
method Structural pruning scheme based on Small-World model, trimming network before training.
result Reduced model size by 2.3% on LeNet-5 for MNIST and 9.02% on VGG-16 for CIFAR-10.
Agent learns policies from world models in simulated environments.
problem Training reinforcement learning agents in diverse environments.
method Generative neural network models environments, compact policies evolved from features.
result Achieves state-of-the-art performance across various environments.
SWNets optimize DL architectures for faster convergence.
problem Excessive training parameters in deep learning models.
method Transforms network topology to reach Small-World Network boundary.
result SWNets achieve faster convergence with fewer parameters.
Ecological systems have a high level of complexity combined with stability and rich biodiversity. Recently, the analysis of their properties and evolution has been pushed forward on a basis of concept of mutualistic networks that provides a detailed understanding of their features being linked to a high nestedness of t…
Google matrix analyzes world economic network, ranking countries and sectors.
problem Ranking countries and sectors in a global economic network.
method Constructs Google matrix from OECD-WTO data, analyzes Markov chain transitions.
result Reliable ranking of countries and sectors, sensitivity to price and labor cost variations.
Among the proposed network models, the hidden variable (or good get richer) one is particularly interesting, even if an explicit empirical test of its hypotheses has not yet been performed on a real network. Here we provide the first empirical test of this mechanism on the world trade web, the network defined by the tr…
Develops PageRank for directed hypergraphs using metabolic network.
problem Lack of directed hypergraph datasets for PageRank algorithm.
method Developed PageRank algorithm for directed hypergraphs and applied it to metabolic network.
result Successfully applied novel PageRank algorithm to metabolic network.
The paper identifies clusters in the World Trade Network using communicability distances.
problem Identifying clusters in the World Trade Network.
method Uses Estrada and vibrational communicability distances to find clusters maximizing a modularity function.
result Identifies specific distance thresholds that maximize modularity, revealing unique relationships between countries.
In this paper, we investigate the statistical features of the weighted international-trade network. By finding the maximum weight spanning trees for this network we make the extraction of the truly relevant connections forming the network's backbone. We discuss the role of large-sized countries (strongest economies) in…
This study applies EMD to MSCI World index and converts IMFs into graphs for GNN modeling.
problem Modeling financial time series with GNNs.
method EMD, CEEMDAN, graph transformations (natural visibility, horizontal visibility, recurrence, transition graphs), topological analysis.
result High-frequency IMFs yield dense, highly connected small-world graphs; low-frequency IMFs produce sparser networks.
DeepMNE learns multi-network node features for better classification.
problem Learning node features across multiple networks.
method Semisupervised autoencoder for multi-network topology.
result DeepMNE outperforms state-of-the-art methods in node classification.
A new neural network model improves reinforcement learning efficiency.
problem Improving sample efficiency in reinforcement learning.
method Proposes a neural network architecture using VQ-VAE and convolutional LSTM for world modeling.
result Shows comparable performance to SimPLe with significantly smaller model.
Study 986 diverse networks to reveal structural diversity across domains.
problem Understanding structural diversity in networks across various domains.
method Machine learning techniques (random forest, confusion matrix) on 986 real-world networks and 575 generated networks.
result Networks in the same partition have similar underlying functions, constraints, and generative mechanisms, regardless of their origins.
Model shows worldwide trade crises can be localized or global, depending on trade balance.
problem Understanding and predicting worldwide trade crises.
method Modeling worldwide trade network using Google matrix analysis and bankruptcy threshold.
result Crisis contagion is localized for high trade balance, global for low trade balance.
HyperGCN applies graph convolutional networks to hypergraphs for complex network learning.
problem Learning with complex relationships in hypergraphs.
method Proposes HyperGCN, a novel GCN for hypergraph semi-supervised learning.
result Demonstrates HyperGCN's effectiveness on real-world hypergraphs.
Central bank influence in Wikipedia analyzed by largest world banks.
problem Analyzing influence and interactions of world banks in Wikipedia.
method Reduced Google matrix algorithm applied to English Wikipedia network.
result Goldman Sachs identified as central bank in Wikipedia network.
The International Trade Networkq-fin.GN
Bilateral trade relationships in the international level between pairs of countries in the world give rise to the notion of the International Trade Network (ITN). This network has attracted the attention of network researchers as it serves as an excellent example of the weighted networks, the link weight being defined …
Trading in Complex Networksq-fin.GN
The study examines price formation in complex networks and finds efficiency varies by network structure.
problem Understanding price formation and efficiency in complex networks.
method Price formation experiments with human subjects in large networks, agent-based model construction.
result Prices are higher and trade less efficient in small-world networks compared to random networks.
cube2net efficiently constructs query-specific networks using data cube technology.
problem Efficiency bottlenecks in network mining for specific queries.
method Data cube organization and reinforcement learning for query-specific network construction.
result cube2net outperforms existing methods in efficiency and effectiveness.
From Bitcoin to Bitcoin Cash: a network analysisphysics.soc-ph
Study analyzes Bitcoin and Bitcoin Cash networks for small-world properties.
problem Understanding the structure and behavior of Bitcoin and Bitcoin Cash networks.
method Network analysis of Bitcoin and Bitcoin Cash networks to evaluate small-world properties.
result Found that networks exhibit small-world behavior, suggesting mechanisms leading to current structure.
Study reveals trade dynamics in dry bulk shipping networks, highlighting their randomness and periodic changes.
problem Understanding the randomness and periodic changes in dry bulk shipping networks.
method Analysis of micro-level trade flow data from 2015 to 2023, focusing on grain, coal, and iron ore networks.
result Dry bulk shipping networks exhibit small-world phenomena and periodic life cycles, influenced by importing ports and global events.
New method identifies common organizational principles in networks.
problem Challenging to cluster networks of different size and density.
method Introduces a new network comparison methodology.
result Identifies common organizational principles in networks.
World Input-Output Networkphysics.soc-ph
Economic systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the multi-regional input-output (MRIO) tables at the global level. By viewing t…
Using the United Nations Commodity Trade Statistics Database [http://comtrade.un.org/db/] we construct the Google matrix of the world trade network and analyze its properties for various trade commodities for all countries and all available years from 1962 to 2009. The trade flows on this network are classified with th…
This paper studies the topological properties of the World Trade Web (WTW) and its evolution over time by employing a weighted network analysis. We show that the WTW, viewed as a weighted network, displays statistical features that are very different from those obtained by using a traditional binary-network approach. I…
C-SWMs learn structured world models from raw data.
problem Learning structured world models from raw sensory data.
method Contrastive learning with graph neural networks.
result C-SWMs outperform typical models in structured environments.
Structure and Evolution of the World Trade Networkphysics.soc-ph
The \emph{World Trade Web} (WTW), the network defined by the international import/export trade relationships, has been recently shown to display some important topological properties which are tightly related to the Gross Domestic Product of world countries. While our previous analysis focused on the static, undirected…
The large-scale organization of the world economies is exhibiting increasingly levels of local heterogeneity and global interdependency. Understanding the relation between local and global features calls for analytical tools able to uncover the global emerging organization of the international trade network. Here we an…
VINE reconstructs networks from diffusion data efficiently.
problem Reconstructing networks from limited diffusion data.
method Variational Inference for Network Reconstruction (VINE).
result VINE accurately recovers connected graphs from diffusion data.
The paper identifies network bottlenecks using minimax paths in stochastic networks.
problem Identifying bottlenecks in networks with stochastic weights.
method Modeling as combinatorial semi-bandit problem, applying combinatorial Thompson Sampling, and approximating the original objective due to computational intractability.
result Established an upper bound on Bayesian regret and evaluated Thompson Sampling performance on real-world networks.
Leveraging advances in machine learning for the robust classification and interpretation of networkscs.SI
Machine learning improves network classification and model selection.
problem Quantifying suitability of generative models for network structures.
method Interpretable machine learning to classify simulated networks based on features and interactions.
result Specific network features and their interactions are crucial for distinguishing generative models.
Predicts path failures in evolving networks using deep learning.
problem Predicting path failures in time-evolving graphs.
method LRGCN, SAPE
result LRGCN outperforms other methods in path failure prediction.
This comment reexamines Simard et al.'s work in [D. Simard, L. Nadeau, H. Kroger, Phys. Lett. A 336 (2005) 8-15]. We found that Simard et al. calculated mistakenly the local connectivity lengths Dlocal of networks. The right results of Dlocal are presented and the supervised learning performance of feedforward neural n…
New embeddings capture local structure in complex networks.
problem Embeddings cannot capture local structure in complex networks.
method Logistic Principal Component Analysis (LPCA) algorithm for exact low-rank representations.
result Exact low-rank representations of real-world networks are possible.
Within the last fifteen years, network theory has been successfully applied both to natural sciences and to socioeconomic disciplines. In particular, bipartite networks have been recognized to provide a particularly insightful representation of many systems, ranging from mutualistic networks in ecology to trade network…
Deep neural network predicts traffic flow on city maps.
problem Short-term traffic flow prediction on high-resolution city maps.
method UNet-based deep convolutional neural network with densely connected layers.
result Best performance on the Traffic4cast challenge 2019.
Unified model detects local and global changes in dynamic networks.
problem Detecting both local and global changes in complex dynamical networks.
method Proposes a unified generative model based on MMSB with SGLD sampler.
result Superior performance on synthetic and real-world data.
Synthetic social networks closely match real-world interactions.
problem Evaluating realism of synthetic social contact networks.
method Used multiple measures of graph complexity to compare synthetic networks with stylized models and empirical data.
result Synthetic networks are more realistic than stylized models.
Neural network learns to solve Black-Scholes for stock options.
problem Stock option pricing using the Black-Scholes Equation.
method Neural Networks applied to solve the Black-Scholes Equation.
result Neural network can accurately forecast stock option prices.
Modeling economic network evolution using Markov chains.
problem Analyzing the evolving world economic network.
method Finite Markov chains analysis of the world input-output database.
result Paradoxical effect of economic slowdown on network flow.
The paper predicts edge weights in weighted directed networks using metric geometry.
problem Predicting edge weights in weighted directed networks.
method Introducing new types of weighted directed networks (AWDNs), constructing metrics, and proposing modified kNN and SVM methods.
result The proposed methods outperform traditional approaches in predicting edge weights.
Study dynamic matching in heterogeneous networks using ODE model.
problem Dynamic matching in heterogeneous networks with compatibility restrictions.
method Introduced an ODE model to evaluate matching algorithms.
result Demonstrated trade-off between quick matching and optimal matching.