This repository is part of the Ph.D. thesis of Isabelle M. van Schilt, Delft University of Technology.

This repository is used to generate a graph of open-source sea and airport data. For this, open-source data of the shipping schedules given by MSC, Maersk, HMM, and Evergreen is used. The data is collected from the websites of the shipping companies (see also https://github.com/EwoutH/shipping-data). The data is then processed to generate a graph of the shipping schedules, including the distributions of the shipping schedules. The graph is used to analyze the shipping schedules and to identify the most important ports in the network. Airport data is collected from the open-source OpenFlights database.

As case study, we collect data on CN-HK to main ports in the USA, and mostly MSC data on South America to NL-BE.

This repository is used for developing various graphs on open-source data and automatically running it as a simulation model in the repository: complex_stylized_supply_chain_model_generator

Numerous types of real-world data can be naturally represented as graphs, such as social networks, trading networks, and biological molecules. This highlights the need for effective graph representations to support various tasks. In recent years, graph neural networks (GNNs) have demonstrated remarkable success in extracting information from graphs and enabling graph-related tasks. However, they still face a series of challenges in solving real-world problems, including scarcity of labeled data, scalability issues, potential bias, etc. These challenges stem from both domain-specific issues and inherent limitations of GNNs. This thesis introduces various strategies to tackle these challenges and empower GNNs on real-world tasks.

For the domain-specific challenges, in this thesis, we especially focus on challenges in the chemistry domain, which plays a pivotal role in the drug discovery process. Considering the significant resources needed for labeling through wet lab experiments, the AI for chemistry domain struggles with the scarcity of labeled datasets. To address this, we present a comprehensive set of strategies that span model-based and data-based strategies alongside a hybrid method. These methods ingeniously utilize the diversity of data, models, and molecular representations to compensate for the lack of labels in individual datasets. For the inherent challenges, this thesis introduces strategies to overcome two main challenges: scalability and degree-based issues, especially in the context of link prediction tasks. Both of these two challenges originate from the mechanism of GNNs, which involves the iterative aggregation of neighboring nodes' information to update each central node. For the scalability issue, our work not only preserves GNNs' prediction performance but also significantly boosts inference speed. Regarding degree bias, our work highly improves the effectiveness of GNNs for underrepresented nodes with very light additional computational costs. These contributions not only address critical gaps in applying GNNs to specific domains but also lay the groundwork for future exploration in the broader field of graph-based real-world tasks.

United States - Population Growth for World was 0.89981 % Chg. at Annual Rate in January of 2023, according to the United States Federal Reserve. Historically, United States - Population Growth for World reached a record high of 2.13312 in January of 1971 and a record low of 0.82796 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Population Growth for World - last updated from the United States Federal Reserve on July of 2025.

Global Semantic Knowledge Graphing market size is expected to reach $2.77 billion by 2029 at 12.7%, segmented as by context-rich knowledge graphs, domain-specific knowledge graphs, enterprise knowledge graphs

Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm’s performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm’s effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős–Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.

World birth rate for 2025 is 17.13, a 0.95% decline from 2024.

<ul style='margin-top:20px;'>

<li>World birth rate for 2024 was <strong>17.30</strong>, a <strong>5.9% increase</strong> from 2023.</li>
<li>World birth rate for 2023 was <strong>16.33</strong>, a <strong>1.34% decline</strong> from 2022.</li>
<li>World birth rate for 2022 was <strong>16.56</strong>, a <strong>1.7% decline</strong> from 2021.</li>
</ul>Crude birth rate indicates the number of live births occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.

The average for 2019 based on 26 countries was 23.59 mammographs per million people. The highest value was in Greece: 66.78 mammographs per million people and the lowest value was in Poland: 10.11 mammographs per million people. The indicator is available from 1980 to 2021. Below is a chart for all countries where data are available.

The USA: Percent of world population: The latest value from 2023 is 4.2 percent, a decline from 4.22 percent in 2022. In comparison, the world average is 0.51 percent, based on data from 196 countries. Historically, the average for the USA from 1960 to 2023 is 4.93 percent. The minimum value, 4.2 percent, was reached in 2023 while the maximum of 6.02 percent was recorded in 1961.

Global Knowledge Graph market size is expected to reach $3.69 Billion by 2029 at 22.9%, rapid growth in data volume and complexity driving market due to need for efficient data organization and analysis

United States - Rest of the World; Currency; Asset, Transactions was -14580.00000 Mil. of $ in October of 2024, according to the United States Federal Reserve. Historically, United States - Rest of the World; Currency; Asset, Transactions reached a record high of 147444.00000 in July of 2020 and a record low of -38752.00000 in July of 2023. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Rest of the World; Currency; Asset, Transactions - last updated from the United States Federal Reserve on June of 2025.

The present dataset stems from the collection in October 2021, through an academic access to the Twitter API, of all tweets ever published that mention any term belonging to a manually defined set of English keywords relevant to the topic of impact investing (II): namely, {impactinvest*, impactbond*, impactfinance, socialfinance, #impinv, #socialimpactbonds}. We gathered a total of 1.89 million “relevant tweets”, published between 2007 and 2021, corresponding to 299 thousand unique users. We then reduced this volume and focused on accounts that were sufficiently active on the topic, or had a minimal audience, in order to capture actual participants to the II social world.

Main associated publications. • Chiapello, E., Roth, C. (in press, to appear in May 2025) Socio-genesis of the impact investing world in France. In: Balsiger, P., Burnier, P., Kabouche, N. (eds) Varieties of Impact Investing. Bristol University Press; ISBN 978-1529238167 — [editor version] • Mangold, L., Roth, C. (in press) Quantifying metadata-structure relationships in networks using description length. Nature Communications Physics — [open-access version: arxiv:2311.18705] • Lobbé, Q., Roth, C.. Mangold, L. (submitted) Chronoblox: Chronophotographic Sequential Graph Visualization — [open-access version: arxiv:2405.07506]

Author: ANN WURST, educator, NGS TEACHER CONSULTANTGrade/Audience: grade 1, grade 2, grade 3, grade 4, grade 5, grade 6, grade 7, grade 8, high school, ap human geography, post secondary, professional developmentResource type: warm_upSubject topic(s): geographic thinkingRegion: worldStandards: (19) Social studies skills. The student applies critical-thinking skills to organize and use information acquired through established research methodologies from a variety of valid sources, including technology. The student is expected to: (A) analyze information by sequencing, categorizing, identifying cause-and-effect relationships, comparing, contrasting, finding the main idea, summarizing, making generalizations and predictions, and drawing inferences and conclusions;

(D) analyze and evaluate the validity of information, arguments, and counterarguments from primary and secondary sources for bias, propaganda, point of view, and frame of reference;

(E) evaluate government data using charts, tables, graphs, and maps. Objectives: Students will keep a list of the toolkit 'helpers' in their notebook and use the elements to process/apply information in various formats such as short answers responses, tickets out the door, setting up writing samples for World Cultures, World Geo, AP Human Geography and other courses involving the study of geographic concepts. Summary: Students can use these 'hooks' in their study of geography, can be applied in every unit where geography is studied. Helps further critical thinking skills. These specific helpers are for reading charts and graphs.

Graph and download economic data for World Uncertainty Index: Global: Simple Average (WUIGLOBALSMPAVG) from Q1 1990 to Q1 2025 about uncertainty, World, average, and indexes.

As post hoc explanations are increasingly used to understand the behavior of Graph Neural Networks (GNNs), it becomes crucial to evaluate the quality and reliability of GNN explanations. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no or unreliable ground-truth explanations for a given task. Here, we introduce a synthetic graph data generator, ShapeGGen, which can generate a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs) accompanied by ground-truth explanations. Further, the flexibility to generate diverse synthetic datasets and corresponding ground-truth explanations allows us to mimic the data generated by various real-world applications. We include ShapeGGen and additional XAI-ready real-world graph datasets into an open-source graph explainability library, GraphXAI. In addition, GraphXAI provides a broader ecosystem of data loaders, data processing functions, synthetic and real-world graph datasets with ground-truth explanations, visualizers, GNN model implementations, and a set of evaluation metrics to benchmark the performance of any given GNN explainer.

Graph and download economic data for Global price of Food index (PFOODINDEXA) from 1992 to 2024 about World, food, indexes, and price.

Sweden: Percent of world GDP: The latest value from 2023 is 0.56 percent, a decline from 0.58 percent in 2022. In comparison, the world average is 0.54 percent, based on data from 184 countries. Historically, the average for Sweden from 1980 to 2023 is 0.85 percent. The minimum value, 0.56 percent, was reached in 2023 while the maximum of 1.31 percent was recorded in 1980.

Knowledge about the general graph structure of the hyperlink graph is important for designing ranking methods for search engines. To amend the ranking calculated by search engines for different websites, search engine optimization agencies focus on linkage structure for their clients. An extreme appearance of ranking manipulation manifests in spam networks, where pages and websites publishing dubious content try to increase their ratings by setting a massive number of links to other pages and retrieve backlinks. The WDC Hyperlink Graph on first level subdomain level has been extracted from the Common Crawl 2012 web corpus and covers 95 million first level subdomains, linked by almost 2 billion connections, which are derived from the hyperlinks of the pages contained by the first level subdomains.

According to Cognitive Market Research, the global semantic knowledge graphing market size is USD 1512.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 14.80% from 2024 to 2031.

North America held the major market of around 40% of the global revenue with a market size of USD 604.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.0% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 453.66 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 347.81 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.8% from 2024 to 2031.
Latin America market of around 5% of the global revenue with a market size of USD 75.61 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.2% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 30.24 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.5% from 2024 to 2031.
The natural language processing knowledge graphing held the highest growth rate in semantic knowledge graphing market in 2024.

Market Dynamics of Semantic Knowledge Graphing Market

Key Drivers of Semantic Knowledge Graphing Market

Growing Volumes of Structured, Semi-structured, and Unstructured Data to Increase the Global Demand

The global demand for semantic knowledge graphing is escalating in response to the exponential growth of structured, semi-structured, and unstructured data. Enterprises are inundated with vast amounts of data from diverse sources such as social media, IoT devices, and enterprise applications. Structured data from databases, semi-structured data like XML and JSON, and unstructured data from documents, emails, and multimedia files present significant challenges in terms of organization, analysis, and deriving actionable insights. Semantic knowledge graphing addresses these challenges by providing a unified framework for representing, integrating, and analyzing disparate data types. By leveraging semantic technologies, businesses can unlock the value hidden within their data, enabling advanced analytics, natural language processing, and knowledge discovery. As organizations increasingly recognize the importance of harnessing data for strategic decision-making, the demand for semantic knowledge graphing solutions continues to surge globally.

Demand for Contextual Insights to Propel the Growth

The burgeoning demand for contextual insights is propelling the growth of semantic knowledge graphing solutions. In today's data-driven landscape, businesses are striving to extract deeper contextual meaning from their vast datasets to gain a competitive edge. Semantic knowledge graphing enables organizations to connect disparate data points, understand relationships, and derive valuable insights within the appropriate context. This contextual understanding is crucial for various applications such as personalized recommendations, predictive analytics, and targeted marketing campaigns. By leveraging semantic technologies, companies can not only enhance decision-making processes but also improve customer experiences and operational efficiency. As industries across sectors increasingly recognize the importance of contextual insights in driving innovation and business success, the adoption of semantic knowledge graphing solutions is poised to witness significant growth. This trend underscores the pivotal role of semantic technologies in unlocking the true potential of data for strategic advantage in today's dynamic marketplace.

Restraint Factors Of Semantic Knowledge Graphing Market

Stringent Data Privacy Regulations to Hinder the Market Growth

Stringent data privacy regulations present a significant hurdle to the growth of the Semantic Knowledge Graphing market. Regulations such as GDPR (General Data Protection Regulation) in Europe and CCPA (California Consumer Privacy Act) in the United States impose strict requirements on how organizations collect, store, process, and share personal data. Compliance with these regulations necessitates robust data protection measures, including anonymization, encryption, and access controls, which can complicate the implementation of semantic knowledge graphing systems. Moreover, concerns about data breach...

Prevalence of undernourishment (% of population) in World was reported at 9.1 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. World - Prevalence of undernourishment (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Global Graph Technology Market Share size & share value expected to touch USD 23.48 billion by 2032, to grow at a CAGR of 21.9% during the forecast period.