The number of snapchat users in the United States was forecast to continuously increase between 2024 and 2028 by in total 5.7 million users (+5.3 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 113.3 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 snapchat users in countries like Canada and Mexico.

The global number of snapchat users in was forecast to continuously increase between 2024 and 2028 by in total 165.7 million users (+27 percent). After the ninth consecutive increasing year, the snapchat user base is estimated to reach 779.3 million users and therefore a new peak in 2028. Notably, the number of snapchat users of was continuously increasing over the past years.The user numbers, depicted here regarding the platform Snapchat, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.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 snapchat users in countries like Africa and the Americas.

As of the first quarter of 2025, photo and video sharing app Snapchat had 460 million daily active users worldwide, up from 460 million global DAU in the fourth quarter of 2024. The app has seen steady increases in daily active users since the beginning of 2019. Snapchat is relevant for teenagers Originally launched in 2011, Snapchat has become one of the most popular social messaging and photo sharing apps worldwide; making its CEO and co-founder Evan Spiegel one of the world’s richest social media entrepreneurs. With almost 800 million active users as of April 2024, Snapchat easily ranks among the most popular social networks worldwide. According to U.S. teenagers in fall 2023, Snapchat is the second most important social network of their generation, ahead of photo sharing competitor Instagram and other networks such as Twitter or Facebook. Overall, 48 percent of U.S. internet users aged 15 to 25 years were reportedly using Snapchat, the highest usage reach among any age group. When it comes to user satisfaction with social media, Snapchat’s performance is fair to middling. According to recent survey data, the social app scored 72 out of 100 points on a consumer satisfaction scale, ranking ahead of Twitter and Facebook but behind Pinterest and eternal rival Instagram.

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

Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets, Jure Leskovec http://snap.stanford.edu/data/index.html email jure at cs.stanford.edu

Citation for the SNAP collection:

@misc{snapnets, author = {Jure Leskovec and Andrej Krevl}, title = {{SNAP Datasets}: {Stanford} Large Network Dataset Collection}, howpublished = {\url{http://snap.stanford.edu/data}}, month = jun, year = 2014 }

The following matrices/graphs were added to the collection in June 2010 by Tim Davis (problem id and name):

2284 SNAP/soc-Epinions1 who-trusts-whom network of Epinions.com 2285 SNAP/soc-LiveJournal1 LiveJournal social network 2286 SNAP/soc-Slashdot0811 Slashdot social network, Nov 2008 2287 SNAP/soc-Slashdot0902 Slashdot social network, Feb 2009 2288 SNAP/wiki-Vote Wikipedia who-votes-on-whom network 2289 SNAP/email-EuAll Email network from a EU research institution 2290 SNAP/email-Enron Email communication network from Enron 2291 SNAP/wiki-Talk Wikipedia talk (communication) network 2292 SNAP/cit-HepPh Arxiv High Energy Physics paper citation network 2293 SNAP/cit-HepTh Arxiv High Energy Physics paper citation network 2294 SNAP/cit-Patents Citation network among US Patents 2295 SNAP/ca-AstroPh Collaboration network of Arxiv Astro Physics 2296 SNAP/ca-CondMat Collaboration network of Arxiv Condensed Matter 2297 SNAP/ca-GrQc Collaboration network of Arxiv General Relativity 2298 SNAP/ca-HepPh Collaboration network of Arxiv High Energy Physics 2299 SNAP/ca-HepTh Collaboration network of Arxiv High Energy Physics Theory 2300 SNAP/web-BerkStan Web graph of Berkeley and Stanford 2301 SNAP/web-Google Web graph from Google 2302 SNAP/web-NotreDame Web graph of Notre Dame 2303 SNAP/web-Stanford Web graph of Stanford.edu 2304 SNAP/amazon0302 Amazon product co-purchasing network from March 2 2003 2305 SNAP/amazon0312 Amazon product co-purchasing network from March 12 2003 2306 SNAP/amazon0505 Amazon product co-purchasing network from May 5 2003 2307 SNAP/amazon0601 Amazon product co-purchasing network from June 1 2003 2308 SNAP/p2p-Gnutella04 Gnutella peer to peer network from August 4 2002 2309 SNAP/p2p-Gnutella05 Gnutella peer to peer network from August 5 2002 2310 SNAP/p2p-Gnutella06 Gnutella peer to peer network from August 6 2002 2311 SNAP/p2p-Gnutella08 Gnutella peer to peer network from August 8 2002 2312 SNAP/p2p-Gnutella09 Gnutella peer to peer network from August 9 2002 2313 SNAP/p2p-Gnutella24 Gnutella peer to peer network from August 24 2002 2314 SNAP/p2p-Gnutella25 Gnutella peer to peer network from August 25 2002 2315 SNAP/p2p-Gnutella30 Gnutella peer to peer network from August 30 2002 2316 SNAP/p2p-Gnutella31 Gnutella peer to peer network from August 31 2002 2317 SNAP/roadNet-CA Road network of California 2318 SNAP/roadNet-PA Road network of Pennsylvania 2319 SNAP/roadNet-TX Road network of Texas 2320 SNAP/as-735 733 daily instances(graphs) from November 8 1997 to January 2 2000 2321 SNAP/as-Skitter Internet topology graph, from traceroutes run daily in 2005 2322 SNAP/as-caida The CAIDA AS Relationships Datasets, from January 2004 to November 2007 2323 SNAP/Oregon-1 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2324 SNAP/Oregon-2 AS peering information inferred from Oregon route-views between March 31 and May 26 2001 2325 SNAP/soc-sign-epinions Epinions signed social network 2326 SNAP/soc-sign-Slashdot081106 Slashdot Zoo signed social network from November 6 2008 2327 SNAP/soc-sign-Slashdot090216 Slashdot Zoo signed social network from February 16 2009 2328 SNAP/soc-sign-Slashdot090221 Slashdot Zoo signed social network from February 21 2009

Then the following problems were added in July 2018. All data and metadata from the SNAP data set was imported into the SuiteSparse Matrix Collection.

2777 SNAP/CollegeMsg Messages on a Facebook-like platform at UC-Irvine 2778 SNAP/com-Amazon Amazon product network 2779 SNAP/com-DBLP DBLP collaboration network 2780 SNAP/com-Friendster Friendster online social network 2781 SNAP/com-LiveJournal LiveJournal online social network 2782 SNAP/com-Orkut Orkut online social network 2783 SNAP/com-Youtube Youtube online social network 2784 SNAP/email-Eu-core E-mail network 2785 SNAP/email-Eu-core-temporal E-mails between users at a research institution 2786 SNAP/higgs-twitter twitter messages re: Higgs boson on 4th July 2012. 2787 SNAP/loc-Brightkite Brightkite location based online social network 2788 SNAP/loc-Gowalla Gowalla location based online social network 2789 SNAP/soc-Pokec Pokec online social network 2790 SNAP/soc-sign-bitcoin-alpha Bitcoin Alpha web of trust network 2791 SNAP/soc-sign-bitcoin-otc Bitcoin OTC web of trust network 2792 SNAP/sx-askubuntu Comments, questions, and answers on Ask Ubuntu 2793 SNAP/sx-mathoverflow Comments, questions, and answers on Math Overflow 2794 SNAP/sx-stackoverflow Comments, questions, and answers on Stack Overflow 2795 SNAP/sx-superuser Comments, questions, and answers on Super User 2796 SNAP/twitter7 A collection of 476 million tweets collected between June-Dec 2009 2797 SNAP/wiki-RfA Wikipedia Requests for Adminship (with text) 2798 SNAP/wiki-talk-temporal Users editing talk pages on Wikipedia 2799 SNAP/wiki-topcats Wikipedia hyperlinks (with communities)

The following 13 graphs/networks were in the SNAP data set in July 2018 but have not yet been imported into the SuiteSparse Matrix Collection. They may be added in the future:

amazon-meta ego-Facebook ego-Gplus ego-Twitter gemsec-Deezer gemsec-Facebook ksc-time-series memetracker9 web-flickr web-Reddit web-RedditPizzaRequests wiki-Elec wiki-meta wikispeedia

The 2010 description of the SNAP data set gave these categories:

• Social networks: online social networks, edges represent interactions between people

• Communication networks: email communication networks with edges representing communication

• Citation networks: nodes represent papers, edges represent citations

• Collaboration networks: nodes represent scientists, edges represent collaborations (co-authoring a paper)

• Web graphs: nodes represent webpages and edges are hyperlinks

• Blog and Memetracker graphs: nodes represent time stamped blog posts, edges are hyperlinks [revised below]

• Amazon networks : nodes represent products and edges link commonly co-purchased products

• Internet networks : nodes represent computers and edges communication

• Road networks : nodes represent intersections and edges roads connecting the intersections

• Autonomous systems : graphs of the internet

• Signed networks : networks with positive and negative edges (friend/foe, trust/distrust)

By July 2018, the following categories had been added:

• Networks with ground-truth communities : ground-truth network communities in social and information networks

• Location-based online social networks : Social networks with geographic check-ins

• Wikipedia networks, articles, and metadata : Talk, editing, voting, and article data from Wikipedia

• Temporal networks : networks where edges have timestamps

• Twitter and Memetracker : Memetracker phrases, links and 467 million Tweets

• Online communities : Data from online communities such as Reddit and Flickr

• Online reviews : Data from online review systems such as BeerAdvocate and Amazon

https://sparse.tamu.edu/SNAP

High Frequency Indicator: The dataset contains year- and month-wise compiled data from the year 2021 to till date on the number of different types of grievances (complaints) received from the users by Snapchat and the action taken by it. The data compiled is based on the monthly transparency reports published by Snapchat in accordance with Rule 4(1)(d) of the Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 (IT Rules, 2021).

The types of grievances received by Snapchat include Sexual Content, Harassment and Bullying, Violence, False Information, Weapons, Drugs, etc. and the action taken includes number of content and unique accounts enforced

The SNAP participation rate shows how many households in Champaign County receive SNAP benefits, as a percentage of the total number of households in the county. The SNAP participation rate can serve as an indicator of poverty and need in the area, as income-based thresholds establish SNAP eligibility. However, not every household in poverty receives SNAP benefits, as can be determined by comparing the poverty rate between 2005 and 2023 and the percentage of households receiving SNAP benefits between 2005 and 2023.

The number of households and the percentage of households receiving SNAP benefits was higher in 2023 than in 2005, but we cannot establish a trend based on year-to-year changes, as in many years these changes are not statistically significant.

SNAP participation data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Receipt of Food Stamps/SNAP in the Past 12 Months by Presence of Children Under 18 Years for Households.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (26 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (5 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S2201; generated by CCRPC staff; using American FactFinder; (16 March 2016).

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

Epinions social network

Dataset information

This is who-trust-whom online social network of a a general consumer review
site Epinions.com. Members of the site can decide whether to ''trust'' each
other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user.

Dataset statistics

Nodes 131828
Edges 841372
Nodes in largest WCC 119130 (0.904)
Edges in largest WCC 833695 (0.991)
Nodes in largest SCC 41441 (0.314)
Edges in largest SCC 693737 (0.825)
Average clustering coefficient 0.2424
Number of triangles 4910076
Fraction of closed triangles 0.08085
Diameter (longest shortest path) 14
90-percentile effective diameter 4.9

Source (citation)

J. Leskovec, D. Huttenlocher, J. Kleinberg: Signed Networks in Social Media.
28th ACM Conference on Human Factors in Computing Systems (CHI), 2010.
http://cs.stanford.edu/people/jure/pubs/triads-chi10.pdf

Files
File Description
soc-sign-epinions.txt.gz Directed Epinions signed social network

The Supplemental Nutrition Assistance Program (SNAP) is the largest of the domestic nutrition assistance programs administered by the Food and Nutrition Service (FNS) of the U.S. Department of Agriculture (USDA). SNAP provides millions of Americans with the means to purchase food for a nutritious diet. During fiscal year (FY) 2021, SNAP served an average of 41.6 million people monthly and paid out $108 billion in benefits, including emergency allotments to supplement SNAP benefits during the COVID-19 public health emergency.The characteristics of SNAP participants and households and the size of the SNAP caseload change over time in response to changes in program rules as well as economic and demographic trends. To quantify these changes or estimate the effect of adjustments to program rules on the current SNAP caseload, FNS relies on data from the SNAP Quality Control (QC) Database. This database is an edited version of the raw data file of monthly case reviews that are conducted by State SNAP agencies to assess the accuracy of eligibility determinations and benefit calculations for their SNAP caseloads. These data cover the last three months of FY 2021.

soc-RedditHyperlinks: Social Network: Reddit Hyperlink Network

The hyperlink network represents the directed connections between two subreddits (a subreddit is a community on Reddit). We also provide subreddit embeddings. The network is extracted from publicly available Reddit data of 2.5 years from Jan 2014 to April 2017.

Subreddit Hyperlink Network: the subreddit-to-subreddit hyperlink network is extracted from the posts that create hyperlinks from one subreddit to another. We say a hyperlink originates from a post in the source community and links to a post in the target community. Each hyperlink is annotated with three properties: the timestamp, the sentiment of the source community post towards the target community post, and the text property vector of the source post. The network is directed, signed, temporal, and attributed.

Note that each post has a title and a body. The hyperlink can be present in either the title of the post or in the body. Therefore, we provide one network file for each.

Subreddit Embeddings: We have also provided embedding vectors representing each subreddit. These can be found in this dataset link: subreddit embedding dataset. Please note that some subreddit embeddings could not be generated, so this file has 51,278 embeddings.

soc-sign-bitcoin-otc: Bitcoin OTC trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-bitcoin-alpha: Bitcoin Alpha trust weighted signed network

This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin Alpha. Since Bitcoin users are anonymous, there is a need to maintain a record of users' reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin Alpha rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

soc-sign-epinions: Epinions social network

This is who-trust-whom online social network of a a general consumer review site Epinions.com. Members of the site can decide whether to ''trust'' each other. All the trust relationships interact and form the Web of Trust which is then combined with review ratings to determine which reviews are shown to the user.

wiki-Elec: Wikipedia adminship election data

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. A small part of Wikipedia contributors are administrators, who are users with access to additional technical features that aid in maintenance. In order for a user to become an administrator a Request for adminship (RfA) is issued and the Wikipedia community via a public discussion or a vote decides who to promote to adminship. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all administrator elections and vote history data. This gave us nearly 2,800 elections with around 100,000 total votes and about 7,000 users participating in the elections (either casting a vote or being voted on). Out of these 1,200 elections resulted in a successful promotion, while about 1,500 elections did not result in the promotion. About half of the votes in the dataset are by existing admins, while the other half comes from ordinary Wikipedia users.

Dataset has the following format:

• E: did the elector result in promotion (1) or not (0)
• T: time election was closed
• U: user id (and screen name) of editor that is being considered for promotion
• N: user id (and screen name) of the nominator
• V: vote(1:support, 0:neutral, -1:oppose) user_id time screen_name

wiki-RfA: Wikipedia Requests for Adminship (with text)

For a Wikipedia editor to become an administrator, a request for adminship (RfA) must be submitted, either by the candidate or by another community member. Subsequently, any Wikipedia member may cast a supporting, neutral, or opposing vote.

We crawled and parsed all votes since the adoption of the RfA process in 2003 through May 2013. The dataset contains 11,381 users (voters and votees) forming 189,004 distinct voter/votee pairs, for a total of 198,275 votes (this is larger than the number of distinct voter/votee pairs because, if the same user ran for election several times, the same voter/votee pair may contribute several votes).

This induces a directed, signed network in which nodes represent Wikipedia members and edges represent votes. In this sense, the...

This dataset consists of circles from Google+. Google+ data was collected from users who had manually shared their circles using the share circle feature. The dataset includes node features (profiles), circles, and ego networks. Data is also available from Facebook and Twitter. Dataset statistics Nodes 107614 Edges 13673453 Nodes in largest WCC 107614 (1.000) Edges in largest WCC 13673453 (1.000) Nodes in largest SCC 69501 (0.646) Edges in largest SCC 9168660 (0.671) Average clustering coefficient 0.4901 Number of triangles 1073677742 Fraction of closed triangles 0.6552 Diameter (longest shortest path) 6 90-percentile effective diameter 3 Source (citation) J. McAuley and J. Leskovec. Learning to Discover Social Circles in Ego Networks. NIPS, 2012.

Orkut social network and ground-truth communities

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

Dataset information

Orkut (http://www.orkut.com/) is a free on-line social network where users form friendship each other. Orkut also allows users form a group which
other members can then join. We consider such user-defined groups as
ground-truth communities. We provide the Orkut friendship social network
and 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.

Dataset statistics
Nodes 3,072,441
Edges 117,185,083
Nodes in largest WCC 3072441 (1.000)
Edges in largest WCC 117185083 (1.000)
Nodes in largest SCC 3072441 (1.000)
Edges in largest SCC 117185083 (1.000)
Average clustering coefficient 0.1666
Number of triangles 627584181
Fraction of closed triangles 0.01414
Diameter (longest shortest path) 9
90-percentile effective diameter 4.8

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-orkut.ungraph.txt.gz Undirected Orkut network
com-orkut.all.cmty.txt.gz Orkut communities
com-orkut.top5000.cmty.txt.gz Orkut 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
3,072,626.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
Orkut network, a matrix of size n-by-n with n=3,072,441, 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 15,301,901 which represents the same number communities in the com-orkut.all.cmty.txt file. The kth line in that file defines the kth community, and is the
column C(:,k), where where C(i,k)=1 if person nodeid(i) is in the kth
community. Row C(i,:) and row/column i of the A matrix thus refer to the
same person, nodeid(i).

Ctop = Problem.aux.Communities_to...

Cash Assistance and SNAP case reopenings with missed benefits ordered by NYS Welfare Management System (WMS) closing reason code. The total number of instances during the specified quarter and year in which a Cash Assistance case was terminated and at least one disbursement date passed before such case was reopened for the same type of ongoing Cash Assistance or SNAP. This data is ordered by the NYS Welfare Management System (WMS) closing reason code that was used to close the case prior to the case reopening. Each record is one case which may include one or many recipients. NOTE: Because asterisks represent values between 1 and 10, users should not sum values across columns or rows to determine the total number of cases within a quarter. Accurate totals are represented in the rows and columns labeled “Total”.

SNAP (Soil Nutrient Assessment Program), a component of the USDA/ARS Soil and Water Hub, is a web-based tool that provides an estimate of plant-available nutrients that the soil naturally provides. Soil test fertilizer recommendations have long been predicated upon response curves generated from fertility trials across the country. These response curves have been compared to relative yield which provide probability ranges for a response to varying fertilizer inputs. Category responses include very low, low, adequate, high or very high inversely related to probability of a response to various inputs of nitrogen, phosphate, and potassium (N, P, and K). New soil test methods, increases in computing power and access to the internet have enabled development of an interactive tool that is based on plant available NPK from both the inorganic fraction and organic pool of the soil. The new methods provide an estimate of plant available nutrients that the soil naturally provides, which has largely been ignored for decades. Since we have access to large datasets we can calculate the amounts of NPK required growing crops in lbs NPK per bu of the desired crop. For example, it requires 100 lbs of N, 50 lbs P2O5, 50 lbs K2O to grow 100 bu corn. These are the base numbers from which we subtract the soil test data after converting from the analytical ppm to Lbs P2O5 or lbs K2O. This is a straight subtraction. It also eliminates the need for "calibration data" since the soil tests reflect the soils inherent fertility. Using the example above, of 100, 50, 50 of N, P, and K required and soil test results of 25, 35, 45 then the fertilizer needed would be 75 N, 15 P2O5 and 5 K2O. This is a simple approach that doesn't get lost in relative yield-crop response curves that have been used for decades from differing geographical areas. This tool will include current fertilizer prices, soil test inputs, and crop based county averages for the last 15 years that will predict the chances of making the yield goal the user inputs compared to historical yield data for their county and calculate the fertilizer cost with and without soil testing compared to user input yield goal and county average. This tool will allow the user via the internet to produce a more straightforward approach to realistically planning next year's fertilizer inputs and associated cost. It will also show the benefits of soil testing for increased fertilizer efficiency and reduced environmental impact. Resources in this dataset:Resource Title: Website Pointer to SNAP - Soil Nutrient Assessment Program. File Name: Web Page, url: https://snap.brc.tamus.edu/Home/Index The web dashboard interface for estimating local yield based on field location (state/county), crop (, area, and yield goal; and soil NPK test results (lb/acre), Results returned illustrate local yield, fertilizer cost/acre, fertilizer needed (lb/acre), and overall chance of success (%).

A tutorial to quantify BAM earthquake using SNAP Differential InSAR is a satellite-based remote sensing technique that can be used to quantify small displacements of the Earth's surface. This is due to the interferometric phase being much more sensitive to the ground motion than to the elevation difference. This practical session will explain how to apply it to real-world Envisat ASAR images, with user-oriented open-source SNAP software. The main goal is able to generate ground motion from a pair of SAR images to map the Earthquake of 2003 in BAM city. More information can be found here: Video: https://youtu.be/Uc-5F9Vz04w https://github.com/BAMInSAR https://www.facebook.com/groups/RadarInterferometry The original Single Look Complex SAR format is licensed by European Space Agency.

The Program Access Index (PAI) is one of the measures FNS uses to reward states for high performance in the administration of the Supplemental Nutrition Assistance Program (SNAP). Performance awards were authorized by the Farm Security and Rural Investment Act of 2002 (also known as the 2002 Farm Bill). The PAI is designed to indicate the degree to which low-income people have access to SNAP benefits. The purpose of this step-by-step guide is to describe the calculation of the Program Access Index (PAI) in detail. It includes all of the data, adjustments, and calculations used in determining the PAI for every state.

Cash Assistance and SNAP case closings ordered by NYS Welfare Management System (WMS) closing reason code. When an active Cash Assistance or SNAP case is closed, NYS Welfare Management System (WMS) records the reason for the closure using a reason code. This file includes the count of all Cash Assistance and SNAP case closings during the specified quarter and year, with the different NYS WMS closing reason codes in the rows. Each record is one case which may include one or many recipients. NOTE: Because asterisks represent values between 1 and 10, users should not sum values across columns or rows to determine the total number of cases within a quarter. Accurate totals are represented in the rows and columns labeled “Total”.

Gowalla is a location-based social networking website where users share their locations by checking-in. The friendship network is undirected and was collected using their public API, and consists of 196,591 nodes and 950,327 edges. We have collected a total of 6,442,890 check-ins of these users over the period of Feb. 2009 - Oct. 2010.

Brightkite was once a location-based social networking service provider where users shared their locations by checking-in. The friendship network was collected using their public API, and consists of 58,228 nodes and 214,078 edges. The network is originally directed but we have constructed a network with undirected edges when there is a friendship in both ways. We have also collected a total of 4,491,143 checkins of these users over the period of Apr. 2008 - Oct. 2010.

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.

https://snap.stanford.edu/data/index.html

This public dataset published by USDA summarizes the total number of enrollees in the Supplemental Nutrition Assistance Program (SNAP) by region. SNAP provides nutrition benefits to supplement the food budget of families and persons meeting eligibility criteria related to monthly income. Program enrollment data offers a direct look into some of the most important underlying social determinants of health (SDoH) by county, including financial insecurity and food insecurity. Analysis of this data can also provide information about the characteristics of the subsidy program’s reach and market penetration over time. As an objective marker of the welfare benefit program’s utilization, these data also offer a complementary view of these SDoH alongside the survey-based questions about SNAP that are included in the ACS dataset. States report these administrative data to the USDA twice a year. The dataset includes total count of people, households and issuance of SNAP benefits by county or county/program. For more information, please refer to the USDA’s SNAP website (link )