The current worldwide refugee crisis is often referred to as the worst humanitarian crisis since World War II. Using Insights for ArcGIS, you'll look at data from 1951 to 2017 and find patterns in the global movement of refugees and asylum seekers.

First, you'll use link analysis to map the movement of refugees from their country of origin to their country of residence. Then, you'll create supplemental charts and tables and dig deeper into the data and the patterns that emerge over time.

In this lesson you will build skills in the these areas:

• Creating a link map
• Filtering data cards, tables, and charts
• Using link analysis to find patterns

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

ns4:pThe Sequence Distance Graph (SDG) framework works with genome assembly graphs and raw data from paired, linked and long reads. It includes a simple deBruijn graph module, and can import graphs using the graphical fragment assembly (GFA) format. It also maps raw reads onto graphs, and provides a Python application programming interface (API) to navigate the graph, access the mapped and raw data and perform interactive or scripted analyses. Its complete workspace can be dumped to and loaded from disk, decoupling mapping from analysis and supporting multi-stage pipelines. We present the design and/ns4:pns4:p implementation of the framework, and example analyses scaffolding a short read graph with long reads, and navigating paths in a heterozygous graph for a simulated parent-offspring trio dataset./ns4:pns4:p SDG is freely available under the MIT license at

map.social is a fun and engaging map-based outreach platform that allows users to individually or collectively create maps in a common map gallery. map.social allows residents, constituents, community stakeholders, and others to provide map referenced comments – a way for anyone to create a map of "their" community in a gallery that can be viewed by fellow community members. Individual maps can be collectively analyzed or brought into GIS for deeper analysis.

This thesis explores graph-based approaches for prediction and similarity analysis problems within networks and hypergraphs. While existing algorithms for link prediction in networks predominantly target the existence or weights of edges, our study expands the scope by delving into the prediction of both vertex and edge weights using metric geometry and machine learning approaches. Additionally, our investigation extends into weight prediction in higher-order networks, often referred to as hypergraphs. We propose a novel notion of neighborhood for hyperedges, utilizing the topological structures of hypergraphs and weights of hyperedges from a given training set. We construct metric spaces on the set of hyperedges based on the neighborhood information. Furthermore, we explore the practical application of graph similarity algorithms in DNA sequence analysis, introducing an accurate and computationally efficient approach to analyze the similarities among DNA sequences. Our proposed methods were tested on diverse real-world datasets and yielded promising results. The main implication of our research is offering a more comprehensive framework for prediction tasks in networks and hypergraphs, providing alternative avenues to gain a deeper understanding of the intricate relationships within complex networks.

Results from cumulative link models in predicting PIRS ratings.

The global I/O-Link market is projected to grow significantly from USD 10.8 billion in 2025 to USD 43.6 billion by 2035, reflecting a CAGR of 15% over the forecast period.

Semi Annual Market Update

Country-wise Insights

Category-wise Insights

Network of 44 papers and 100 citation links related to "Statistical tools for linkage analysis and genetic association studies".

Deezer Ego Nets

The ego-nets of Eastern European users collected from the music streaming service Deezer in February 2020. Nodes are users and edges are mutual follower relationships. The related task is the prediction of gender for the ego node in the graph.

Github Stargazers

The social networks of developers who starred popular machine learning and web development repositories (with at least 10 stars) until 2019 August. Nodes are users and links are follower relationships. The task is to decide whether a social network belongs to web or machine learning developers. We only included the largest component (at least with 10 users) of graphs.

Reddit Threads

Discussion and non-discussion based threads from Reddit which we collected in May 2018. Nodes are Reddit users who participate in a discussion and links are replies between them. The task is to predict whether a thread is discussion based or not (binary classification).

Twitch Ego Nets

The ego-nets of Twitch users who participated in the partnership program in April 2018. Nodes are users and links are friendships. The binary classification task is to predict using the ego-net whether the ego user plays a single or multple games. Players who play a single game usually have a more dense ego-net.

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#disjointgraphs

Network of 41 papers and 64 citation links related to "Efficient construction of high-density linkage map and its application to QTL analysis in barley".

Various Weather Charts - Surface Weather Charts *The download link has been changed since September 15, 2023. Please update before December 31, 2023, as the old version link will expire. For those who need to download a large amount of data, please apply for membership on the Meteorological Data Open Platform: https://opendata.cwa.gov.tw/index

24-hour wave forecast map *From September 15, 2023, the download link will be changed. Please switch before December 31, 2023, otherwise the old version link will become invalid. If you need to download a large amount of data, please apply for membership at the Meteorological Data Open Platform https://opendata.cwa.gov.tw/index

Network of 31 papers and 46 citation links related to "Interference and Link Budget Analysis in Integrated Satellite and Terrestrial Mobile System".

Network of 44 papers and 70 citation links related to "Linkage Analysis with Multiplexed Short Tandem Repeat Polymorphisms Using Infrared Fluorescence and M13 Tailed Primers".

Weekly Weather Forecast Map (Day 1) *The download link was updated on September 15, 2023. Please update it before December 31, 2023, and the old version link will expire. If you need to download a large amount of data, please apply for membership at the Meteorological Data Open Platform. https://opendata.cwa.gov.tw/index

Identifying the essential characteristics and forecasting carbon prices is significant in promoting green transformation. This study transforms the time series into networks based on China’s pilots by using the visibility graph, mining more information on the structure features. Then, we calculate nodes’ similarity to forecast the carbon prices by link prediction. To improve the predicted accuracy, we notice the node distance to introduce the weight coefficient, measuring the impact of different nodes on future nodes. Finally, this study divides eight pilots into different communities by hierarchical clustering to study the similarities between these pilots. The results show that eight pilots are the “small world” networks except for Chongqing and Shenzhen pilots, all of which are “scale-free” networks except for Shanghai and Tianjin pilots. Compared with other predicted methods, the proposed method in this study has good predicted performance. Moreover, these eight pilots are divided into three clusters, indicating a higher similarity in their price-setting schemes in the same community. Based on the analysis of China’s pilots, this study provides references for carbon trading and related enterprises.

Network of 42 papers and 68 citation links related to "Familial aggregation and linkage analysis with covariates for metabolic syndrome risk factors".

Global Market Share by Key Players (2025)

Tier-Wise Company Classification (2025)

Network of 42 papers and 107 citation links related to "Linkage and association analysis of susceptibility regions on chromosomes 5 and 6 in 106 Scandinavian sibling pair families with multiple sclerosis".

This project is about the Datenkompetenzzentren (DKZ) / Data Competence Centers. It analysis its connections between entities and visualizes those on a map.

Please note the link to the repository: https://git.rwth-aachen.de/dl/best-practices/dkz-network-analysis/

Data

The data used for the network has been taken from Wikidata using two queries:
- `wikidata-edges.sparql`
- `wikidata-nodes.sparql`

The results from the Wikidata querries are stored in:

- `dkz-network-analysis-edges.csv`
- `dkz-network-analysis-nodes.csv`

Visualization

We used `Gephi 0.10.1` for the visualization. The `.gephi`-file is also provided. You find all maps as layers in `dkz-network-analysis-map.svg`.

Licence

The maps are licenced under CC-BY:

CC-BY Lukas C. Bossert @ RWTH Aachen & DKZ.2R