As of 2024, the estimated number of internet users worldwide was 5.5 billion, up from 5.3 billion in the previous year. This share represents 68 percent of the global population. Internet access around the world Easier access to computers, the modernization of countries worldwide, and increased utilization of smartphones have allowed people to use the internet more frequently and conveniently. However, internet penetration often pertains to the current state of development regarding communications networks. As of January 2023, there were approximately 1.05 billion total internet users in China and 692 million total internet users in the United States. Online activities Social networking is one of the most popular online activities worldwide, and Facebook is the most popular online network based on active usage. As of the fourth quarter of 2023, there were over 3.07 billion monthly active Facebook users, accounting for well more than half of the internet users worldwide. Connecting with family and friends, expressing opinions, entertainment, and online shopping are amongst the most popular reasons for internet usage.

Description

An extensive social network of GitHub developers was collected from the public API in June 2019. Nodes are developers who have starred at most minuscule 10 repositories, and edges are mutual follower relationships between them. The vertex features are extracted based on the location; repositories starred, employer and e-mail address. The task related to the graph is binary node classification - one has to predict whether the GitHub user is a web or a machine learning developer. This targeting feature was derived from the job title of each user.

Properties

• Directed: No.
• Node features: Yes.
• Edge features: No.
• Node labels: Yes. Binary-labeled.
• Temporal: No.
• Nodes: 37,700
• Edges: 289,003
• Density: 0.001
• Transitvity: 0.013

Possible Tasks

• Binary node classification
• Link prediction
• Community detection
• Network visualisation

In 1995, only 0.77% of the world’s population was online. Fast forward to 2025, and nearly two-thirds of humanity now live a connected life. Whether it's streaming news on a smart fridge in Texas or running a full business from a beach in Bali, the internet is no longer a...

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

This dataset provides a comprehensive overview of global internet usage as of 2024. It includes the number of internet users in each country, the percentage of the population with internet access, and the total internet traffic generated. This dataset can be used to analyze trends in internet adoption, digital inequality, and the potential impact of the internet on various sectors of the global economy.

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

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.

The USA: Internet users, percent of population: The latest value from 2023 is 93.1 percent, an increase from 92.2 percent in 2022. In comparison, the world average is 72.46 percent, based on data from 177 countries. Historically, the average for the USA from 1990 to 2023 is 56.21 percent. The minimum value, 0.79 percent, was reached in 1990 while the maximum of 93.1 percent was recorded in 2023.

The dataset contains 122 CAIDA AS graphs, from January 2004 to November 2007 - http://www.caida.org/data/active/as-relationships/ . Each file contains a full AS graph derived from a set of RouteViews BGP table snapshots.

Dataset statistics are calculated for the graph with the highest number of
nodes - dataset from November 5 2007. Dataset statistics for graph with
highest number of nodes - 11 5 2007

Nodes 26475
Edges 106762
Nodes in largest WCC 26475 (1.000)
Edges in largest WCC 106762 (1.000)
Nodes in largest SCC 26475 (1.000)
Edges in largest SCC 106762 (1.000)
Average clustering coefficient 0.2082
Number of triangles 36365
Fraction of closed triangles 0.007319
Diameter (longest shortest path) 17
90-percentile effective diameter 4.6

Source (citation)

J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time: Densification
Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International
Conference on Knowledge Discovery and Data Mining (KDD), 2005.

Files
File Description
as-caida20071105.txt.gz CAIDA AS graph from November 5 2007
as-caida.tar.gz 122 CAIDA AS graphs from January 2004 to November 2007

NOTE for UF Sparse Matrix Collection: these graphs are weighted. In the
original SNAP data set, the edge weights are in the set {-1, 0, 1, 2}. Note
that "0" is an edge weight. This can be handled in the UF collection for the
primary sparse matrix in a Problem, but not when the matrices are in a sequence in the Problem.aux MATLAB struct. The entries with zero edge weight would
become lost. To correct for this, the weights are modified by adding 2 to each weight. This preserves the structure of the original graphs, so that edges
with weight zero are not lost. (A non-edge is not the same as an edge with
weight zero in this problem).

old new weights:                              
-1 1                                   
0  2                                   
1  3                                   
2  4                                   

So to obtain the original weights, subtract 2 from each entry.

The primary sparse matrix for this problem is the as-caida20071105 matrix, or
Problem.aux.G{121}, the second-to-the-last graph in the sequence.

The nodes are uniform across all graphs in the sequence in the UF collection.
That is, nodes do not come and go. A node that is "gone" simply has no edges. This is to allow comparisons across each node in the graphs.
Problem.aux.nodenames gives the node numbers of the original problem. So
row/column i in the matrix is always node number Problem.aux.nodenames(i) in
all the graphs.

Problem.aux.G{k} is the kth graph in the sequence.
Problem.aux.Gname(k,:) is the name of the kth graph.

Graph and download economic data for Internet users for Bangladesh (ITNETUSERP2BGD) from 1990 to 2023 about Bangladesh, internet, and persons.

As of 2023, 65 percent of the population in Small Island Developing States (SIDS) used the internet, compared to 35 percent of individuals living in the least Developed Countries (LDCs) while the internet penetration rate for those living in Landlocked Developing Counties (LLDCs) was at 39 percent. The global online access rate was 68 percent.

The number of internet users globally will grow to 5.3 billion by 2023, according to the source. The compound annual growth rate for the whole period from 2018 to 2023 is six percent. The fastest expected growth from this period was in 2019, with 300 million new internet users and a growth rate of 7.7 percent from 2018.

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 December of 2025.

Graph and download economic data for Internet users for the Islamic Republic of Afghanistan (ITNETUSERP2AFG) from 1990 to 2023 about Afghanistan, internet, and persons.

When asked about "Attitudes towards the internet", most Australian respondents pick "It is important to me to have mobile internet access in any place" as an answer. 55 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.

email-EuAll: EU email communication network

The network was generated using email data from a large European research institution. For a period from October 2003 to May 2005 (18 months) we have anonymized information about all incoming and outgoing email of the research institution. For each sent or received email message we know the time, the sender and the recipient of the email. Overall we have 3,038,531 emails between 287,755 different email addresses. Note that we have a complete email graph for only 1,258 email addresses that come from the research institution. Furthermore, there are 34,203 email addresses that both sent and received email within the span of our dataset. All other email addresses are either non-existing, mistyped or spam.

Given a set of email messages, each node corresponds to an email address. We create a directed edge between nodes i and j, if i sent at least one message to j.

email-Enron: Enron email network

Enron email communication network covers all the email communication within a dataset of around half million emails. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. Nodes of the network are email addresses and if an address i sent at least one email to address j, the graph contains an undirected edge from i to j. Note that non-Enron email addresses act as sinks and sources in the network as we only observe their communication with the Enron email addresses.

The Enron email data was originally released by William Cohen at CMU.

wiki-Talk: Wikipedia Talk network

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. Each registered user has a talk page, that she and other users can edit in order to communicate and discuss updates to various articles on Wikipedia. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all user talk page changes and created a network.

The network contains all the users and discussion from the inception of Wikipedia till January 2008. Nodes in the network represent Wikipedia users and a directed edge from node i to node j represents that user i at least once edited a talk page of user j.

comm-f2f-Resistance: Dynamic Face-to-Face Interaction Networks

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

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#email

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

Youtube social network and ground-truth communities

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

Dataset information

Youtube (http://www.youtube.com/) 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.
(http://socialnetworks.mpi-sws.org/data-imc2007.html)

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 (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Network statistics
Nodes 1,134,890
Edges 2,987,624
Nodes in largest WCC 1134890 (1.000)
Edges in largest WCC 2987624 (1.000)
Nodes in largest SCC 1134890 (1.000)
Edges in largest SCC 2987624 (1.000)
Average clustering coefficient 0.0808
Number of triangles 3056386
Fraction of closed triangles 0.002081
Diameter (longest shortest path) 20
90-percentile effective diameter 6.5
Community statistics
Number of communities 8,385
Average community size 13.50
Average membership size 0.10

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-youtube.ungraph.txt.gz Undirected Youtube network
com-youtube.all.cmty.txt.gz Youtube communities
com-youtube.top5000.cmty.txt.gz Youtube communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
1,157,827.

In the SuiteSparse Matrix Collection, Problem.A is the undirected Youtube
network, a matrix of size n-by-n with n=1,134,890, which is the number of
unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 16,386
which represents the communities in the com-youtube.all.cmty.txt file.
The kth line in that file defines the kth community, and is the column
C(:,k), where C(i,k)=1 if person ...

The population share with internet access in the United States was forecast to continuously increase between 2024 and 2029 by in total *** percentage points. After the ninth consecutive increasing year, the internet penetration is estimated to reach ***** percent and therefore a new peak in 2029. Notably, the population share with internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via any means. The shown figures have been derived from survey data that has been processed to estimate missing demographics. 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 more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

This edgelist serves network analyses.