326 graphs

The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

Youtube social network and ground-truth communities Dataset information Youtube is a video-sharing web site that includes a social network. In the Youtube social network, users form friendship each other and users can create groups which other users can join. We consider such user-defined groups as ground-truth communities. This data is provided by Alan Mislove et al.

We regard each connected component in a group as a separate ground-truth community. We remove the ground-truth communities which have less than 3 nodes. We also provide the top 5,000 communities with highest quality which are described in our paper. As for the network, we provide the largest connected component.

more info : https://snap.stanford.edu/data/com-Youtube.html

Internet graph dataset that contains AS-level edges associated with major border router manufactures.

When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Host-level Web Graph - This graph aggregates the page graph by subdomain/host where each node represents a specific subdomain/host and an edge exists between a pair of hosts/subdomains if at least one link was found between pages that belong to a pair of subdomains/hosts. The hyperlink graph was extracted from the Web corpus released by the Common Crawl Foundation in August 2012. The Web corpus was gathered using a web crawler employing a breadth-first-search selection strategy and embedding link discovery while crawling. The crawl was seeded with a large number of URLs from former crawls performed by the Common Crawl Foundation. Also, see web-cc12-firstlevel-subdomain and web-cc12-PayLevelDomain.

We explore the problem of routing in a quantum internet using multi-commodity flow. In this project, we assume that all of the repeaters are based on atomic ensemble and linear optics. Moreover, we assume that any node in the network can act as an end node as well as a repeater node.

Here the file Surfnet.graphml.xml contains the .graphml file corresponding to the SURFnet topology. The file lp_routing_testing.py takes the .graphml file as input and then generates random demands (s,e,l), where s is the source, e is the destination, and l is the upper bound on the length of the path.

On the basis of this demand, we compute the modified network and in order to get a total achievable rate, we construct a linear program (LP). Later we feed this LP formulation to an LP solver and get the optimal total achievable rate for all of the demands.

Content: This dataset contains an integrated snapshot of the Internet Topology (year 2012) with corresponding graph models at the IP, Router, PoP, AS and ISP layers.

Purpose: Despite intensive research during the last two decades, the detailed structural composition of the Internet is still opaque to researchers. Nevertheless, due to the importance of Internet maps for the development of more effective routing algorithms, security mechanisms, and resilience management, more detailed insights are required. This article advances the understanding of the Internet structure by integrating data from different large-scale measurement campaigns into a set of comprehensive Internet graphs at different abstraction levels, and analyzes them in terms of important statistics and graph measures.

Design/methodology/approach: This study follows the topology measurement framework suggested by Gunes and Sarac (2009), involving three phases: topology collection, topology construction, and topology analysis.

Findings: An integrated data set of Internet graphs at different abstraction layers is provided that can serve as a baseline for future research on Internet analytics. Furthermore, results of important graph metrics are presented and power-law relationships for the degree distributions on every level of the current Internet are substantiated.

Research limitations/implications: By necessity, the integrated graphs provide a snapshot of the Internet topology. In future work, repeated measurements and automated data integration could lead to a better understanding of Internet dynamics.

Practical implications: Due to increasing dependency on the Internet as a critical global infrastructure, studying Internet connectivity is more important than ever for both companies and Internet service providers. The data set will be made publically available for network research.

Social implications: Understanding the structure of Internet serves as a fundamental step in improving the robustness, security, and privacy of any online service.

Originality/value: By carefully integrating six different traceroute datasets such as iPlane, CAIDA, Carna, DIMES, RIPE Atlas, and RIPE IPv6L, this paper presents the Internet graphs of a substantially larger and thus solid scale than previously known, at well-established abstraction levels such as the IP interface, router, Point of Presence (PoP), Autonomous System (AS), and Internet Service Provider (ISP). Furthermore, by employing a broad diversity of graph measures, this study creates a more exhaustive snapshot of the global Internet topology than earlier works.

This edgelist serves network analyses.

Graph and download economic data for Internet users for the United States (ITNETUSERP2USA) from 1990 to 2023 about internet, persons, and USA.

As of 2025, approximately 93.1 percent of the United States' population accessed the internet, up from approximately 71 percent in 2013. The United States is one of the biggest online markets worldwide. Additionally, in 2025, over 322 million individuals in the country went online. Furthermore, social media apps were among the most popular category of mobile apps used in the market. Social media usage in the U.S. Social media usage in the United States has seen significant growth in recent years, amassing 310 million as of 2025. By the third quarter of 2024, internet users in the U.S. were spending around two hours on social media out of seven hours of internet usage. The most common activities among U.S. users include sending private messages and liking posts or following people, which highlights widespread engagement with social media platforms among internet users in the United States. TikTok surge in the U.S. TikTok continues to be one of the most popular social media platforms in the United States. As of February 2025, over 135 million individuals or 45 percent of internet users in the country used the social network. This surge in popularity is the result of user’s high engagement with short-form videos and quick entertainment in which TikTok managed to capture users’ attention. Users in the United States spent an average of 45 hours and 37 minutes monthly in 2023.

Graph and download economic data for Internet users for Norway (ITNETUSERP2NOR) from 1990 to 2023 about internet, Norway, and persons.

Internet users for the United States was 93.10000 Per 100 People in January of 2023, according to the United States Federal Reserve. Historically, Internet users for the United States reached a record high of 93.10000 in January of 2023 and a record low of 0.78473 in January of 1990. Trading Economics provides the current actual value, an historical data chart and related indicators for Internet users for the United States - last updated from the United States Federal Reserve on July of 2025.

Internet use in the UK annual estimates by age, sex, disability, ethnic group, economic activity and geographical location, including confidence intervals.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F25663426%2Fef0839f1c6342b2f89b87d08acfb4b74%2Fpeertube_graph(1).png?generation=1746770713374326&alt=media" alt="Peertube "follow" graph">

Above is the Peertube "follow" graph. The colours correspond to the language of the server (purple: unknown, green: French, blue: English, black: German, orange: Italian, grey: others).

Introduction

Decentralized machine learning---where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer messages---is increasingly popular, as it enables better scalability and control over the data. A major challenge in this setting is that learning dynamics depend on the topology of the communication graph, which motivates the use of real graph datasets for benchmarking decentralized algorithms. Unfortunately, existing graph datasets are largely limited to for-profit social networks crawled at a fixed point in time and often collected at the user scale, where links are heavily influenced by the platform and its recommendation algorithms. The Fediverse, which includes several free and open-source decentralized social media platforms such as Mastodon, Misskey, and Lemmy, offers an interesting real-world alternative. We introduce Fedivertex, a new dataset covering seven social networks from the Fediverse, crawled weekly on a weekly basis.

We refer to our paper for a detailed presentation of the graphs: [SOON]

Usage

Python

We implemented a simple Python API to interact easily with the dataset: https://pypi.org/project/fedivertex/

pip3 install fedivertex

This package automatically downloads the dataset and generate NetworkX graphs.

from fedivertex import GraphLoader

loader.list_graph_types("mastodon")
# List available graphs for a given software, here federation and active_user

G = loader.get_graph(software = "mastodon", graph_type = "active_user", index = 0, only_largest_component = True)
# G contains the Networkx graph of the giant component of the active users graph at the 1st date of collection

We also provide a Kaggle notebook demonstrating simple operations using this library: https://www.kaggle.com/code/marcdamie/exploratory-graph-data-analysis-of-fedivertex

Available graphs

The dataset contains graphs crawled on a daily basis on 7 social networks from the Fediverse. Each graph quantifies/characterizes the interaction differently depending on the information provided by the public API of these networks.

We present briefly the graph below (NB: the term "instance" refers to servers on the Fediverse):

• [Bookwyrm/Friendica/Lemmy/Mastodon/Misskey/Pleroma] "federation" graphs: If two instances know each other they are connected in this graph. The federation graph then corresponds to the undirected communication graph between instances.
• Peertube "follow" graphs: On Peertube, an instance X can follow an instance Y to let its users see all the videos posted on Y. This graph is a directed graph with edges of weight 1 for following.
• Lemmy "federation with blocks" graphs: This graph completes the federation graph with negative edges when an instance X blocks instance Y. The graph is directed.
• Lemmy "cross-instance" graphs: two instances are connected as soon as there exists a pair of users who published a message in the same thread, but possibly on a third instance. This is an undirected graph, less sparse than "intra-instance".
• Lemmy "intra-instance" graphs: the instance X is linked to Y if an user of X has published a message on instance Y. This graph is directed and very sparse.
• [Mastodon/Misskey/Pleroma] "active users" graphs: For each instance, we consider the set of the 10K most recently active users. Then, for each user of an instance X, we consider the list of the users they follow, and add 1 to the edge from X to Y where Y is the instance the followed users. The weight of the edge from X to Y thus encodes how much the content seen on instance X is generated in instance Y. Note that this graph thus contains self loops.

These graphs provide diverse perspectives on the Fediverse as they capture more or less subtle phenomenon. For example, "federation" graphs are dense, while "intra-instance" graphs are sparse. We have performed a detailed exploratory data analysis in this notebook.

Gephi

Our CSV files are formatted so that they can be directly imported into Gephi for graph visualization. Find below an example Gephi visualization of the Misskey "active users" graph (without the misskey.io node). The colours correspond to the language of the server (purple:Unknown, red: Japanese, brown: Korean, blue: English, yellow: Chinese).

![Misskey "active users" graph](https://www.go...

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics.Anatomical and functional MRI images of the brain have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains richer structural and positional information that traditional examination methods are unable to capture. However, the lack of brain network data transformed from functional MRI images prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert data from MRI images into brain networks. We bridge this gap by collecting a large amount of available MRI images from existing studies, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 neurodegenerative conditions, and consist of a total of 2,688 subjects.Due to the data protocol, we are unable to release the ADNI dataset here. The data will be released via the ADNI external data submissions within their data system.We test our graph datasets on 5 machine learning models commonly used in neuroscience and on a recent graph-based analysis model to validate the data quality and to provide domain baselines. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our complete preprocessing details, codes, and brain network data: https://github.com/brainnetuoa/data_driven_network_neuroscience.To stay informed about the new updates of the datasets, kindly provide us with your email address:https://forms.gle/KGAajR6LEysXWKvKAUpdated on 10/09/2024:Please note that we have identified 14 subjects in the PPMI (Parkinson's Progression Markers Initiative) dataset, prodromal group, where the time-series images include only 10 time slots. The invalid subjects are:sub-prodromal103857sub-prodromal120622sub-prodromal146573sub-prodromal40737sub-prodromal52874sub-prodromal55560sub-prodromal56680sub-prodromal58027sub-prodromal58680sub-prodromal59390sub-prodromal59483sub-prodromal59503sub-prodromal71658sub-prodromal75422We have removed the invalid images, and updated the dataset by including both the parcellated images (ppmi_v2.zip) and the preprocessed images (Ppmi_Preprocessed_v2.z*).

The MOOC user action dataset represents the actions taken by users on a popular MOOC platform. The actions are represented as a directed, temporal network. The nodes represent users and course activities (targets), and edges represent the actions by users on the targets. The actions have attributes and timestamps. To protect user privacy, we anonimize the users and timestamps are standardized to start from timestamp 0. The dataset is directed, temporal, and attributed.

Additionally, each action has a binary label, representing whether the user dropped-out of the course after this action, i.e., whether this is last action of the user.

This dataset serves as a recommender system dataset and a dynamic network dataset.

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#actions

Weighted graph representation of a road network in selected regions. Derived from Open Street Map https://www.openstreetmap.org. The dataset can be used as input for the betweenness centrality algorithm implemented here: https://code.it4i.cz/ADAS/betweenness.

Archive contents
The archive contains following folders.

CZE
Static graphs of three major cities in the Czech Republic (Praha, Brno, Ostrava) and entire Czech road network. Weighted by length of the road segments in metres.

PT
Static graphs of Lisbon, Porto and entire Portugese road network. Weighted by length of the road segments in metres.

Data format
Standard UTF-8 encoded CSV files, separated by semicolon with the following columns:

id1: (Type: unsigned long) - start node
id2: (Type: unsigned long) - end node
dist: (Type: unsigned long) - weight of the edge (length in metres, unless described otherwise)
edge_id: (Type: unsigned long) - unique edge identifier

Ireland: Internet users, percent of population: The latest value from 2022 is 95.59 percent, an increase from 94.46 percent in 2021. In comparison, the world average is 71.68 percent, based on data from 177 countries. Historically, the average for Ireland from 1990 to 2022 is 46.86 percent. The minimum value, 0 percent, was reached in 1990 while the maximum of 95.59 percent was recorded in 2022.