As of the second quarter of 2025, photo and video sharing app Snapchat had 469 million daily active users worldwide, up from 460 million global DAU in the first quarter of 2025. 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 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.

Gowalla Dataset

The Gowalla dataset, sourced from the Stanford Network Analysis Project (SNAP), contains user check-ins and social network information from the now-defunct location-based social networking platform Gowalla.

  Key features:

Check-in data: records of user check-ins at various locations with timestamps and geographical coordinates (latitude, longitude). Social graph: user relationships represented as a graph, where edges denote friendships between users.… See the full description on the dataset page: https://huggingface.co/datasets/habedi/gowalla-dataset.

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

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).

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

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 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_top5000 is n-by-5000, with the same
structure as the C array above, with the content of the
com-youtube.top5000.cmty.txt.gz file.

Ebitda Time Series for Snap Inc. Snap Inc. operates as a technology company in North America, Europe, and internationally. The company offers Snapchat, a visual messaging application with various tabs, such as camera, visual messaging, snap map, stories, and spotlight that enable people to communicate visually through short videos and images. It also provides Snapchat+, a subscription service that provides subscribers access to exclusive, experimental, and pre-release features; Spectacles, an eyewear product; and advertising products, including AR ads and Snap ads comprises a single image or video ads, collection ads, dynamic ads, story ads, commercials, sponsored snaps, and promoted places. The company was formerly known as Snapchat, Inc. and changed its name to Snap Inc. in September 2016. Snap Inc. was founded in 2010 and is headquartered in Santa Monica, California.

Investments Time Series for Snap Inc. Snap Inc. operates as a technology company in North America, Europe, and internationally. The company offers Snapchat, a visual messaging application with various tabs, such as camera, visual messaging, snap map, stories, and spotlight that enable people to communicate visually through short videos and images. It also provides Snapchat+, a subscription service that provides subscribers access to exclusive, experimental, and pre-release features; Spectacles, an eyewear product; and advertising products, including AR ads and Snap ads comprises a single image or video ads, collection ads, dynamic ads, story ads, commercials, sponsored snaps, and promoted places. The company was formerly known as Snapchat, Inc. and changed its name to Snap Inc. in September 2016. Snap Inc. was founded in 2010 and is headquartered in Santa Monica, California.

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 (%).

96 million memes from Memetracker

96 million memes from the Memetracker. Memetracker tracks the quotes and phrases that appear most frequently over time across this entire online news spectrum. This makes it possible to see how different stories compete for news and blog coverage each day, and how certain stories persist while others fade quickly.

Overall Memetracker tracks more than 17 million different phrases and about 54% of the total phrase/quote mentions appear on blos and 46% in news media.

For each document (blog post or news media article): - URL (author) - Time - Memes - Links

Volume Time Series of Memetracker Phrases and Twitter Hashtags

Data contains the time series of the volume (the number of mention per hour) of 1,000 Memetracker phrases and 1,000 Twitter hashtags. Memetracker phrases are the 1,000 highest total volume phrases among 343 million phrases collected from Sep 2008 to Aug 2009. Twitter hashtags are the 1,000 highest total volume hashtags among 6 million hashtags from Jun to Dec 2009.

Higgs Twitter Dataset

The Higgs dataset has been built after monitoring the spreading processes on Twitter before, during and after the announcement of the discovery of a new particle with the features of the elusive Higgs boson on 4th July 2012. The messages posted in Twitter about this discovery between 1st and 7th July 2012 are considered.

The four directional networks made available here have been extracted from user activities in Twitter as: 1. re-tweeting (retweet network) 2. replying (reply network) to existing tweets 3. mentioning (mention network) other users 4. friends/followers social relationships among user involved in the above activities 5. information about activity on Twitter during the discovery of Higgs boson

It is worth remarking that the user IDs have been anonimized, and the same user ID is used for all networks. This choice allows to use the Higgs dataset in studies about large-scale interdependent/interconnected multiplex/multilayer networks, where one layer accounts for the social structure and three layers encode different types of user dynamics .

Note that this dataset has been updated on Mar 31 2015. If you downloaded a previous version, please update it, results could differ.

For more information about data collection, please refer to our paper.

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#onlinecoms

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) 2022, SNAP served an average of 41.2 million people monthly and paid out $114 billion in benefits, including emergency allotments to supplement SNAP benefits due to 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 FY 2022.

loc-Brightkite

https://snap.stanford.edu/data/loc-Brightkite.html

Dataset information

Brightkite (http://www.brightkite.com/) 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.

Dataset statistics
Nodes 58,228
Edges 214,078
Nodes in largest WCC 56739 (0.974)
Edges in largest WCC 212945 (0.995)
Nodes in largest SCC 56739 (0.974)
Edges in largest SCC 212945 (0.995)
Average clustering coefficient 0.1723
Number of triangles 494728
Fraction of closed triangles 0.03979
Diameter (longest shortest path) 16
90-percentile effective diameter 6
Checkins 4,491,143

Source (citation)
E. Cho, S. A. Myers, J. Leskovec. Friendship and Mobility: Friendship and
Mobility: User Movement in Location-Based Social Networks ACM SIGKDD
International Conference on Knowledge Discovery and Data Mining (KDD),
2011. http://cs.stanford.edu/people/jure/pubs/mobile-kdd11.pdf

Files
File Description
loc-brightkite_edges.txt.gz Friendship network of Brightkite users
loc-brightkite_totalCheckins.txt.gz
Time and location information of check-ins made by users

Example of check-in information

[user][check-in time]   [latitude] [longitude] [location id]    
58186 2008-12-03T21:09:14Z 39.633321 -105.317215 ee8b88dea22411    
58186 2008-11-30T22:30:12Z 39.633321 -105.317215 ee8b88dea22411    
58186 2008-11-28T17:55:04Z -13.158333 -72.531389 e6e86be2a22411    
58186 2008-11-26T17:08:25Z 39.633321 -105.317215 ee8b88dea22411    
58187 2008-08-14T21:23:55Z 41.257924 -95.938081 4c2af967eb5df8    
58187 2008-08-14T07:09:38Z 41.257924 -95.938081 4c2af967eb5df8    
58187 2008-08-14T07:08:59Z 41.295474 -95.999814 f3bb9560a2532e    
58187 2008-08-14T06:54:21Z 41.295474 -95.999814 f3bb9560a2532e    
58188 2010-04-06T06:45:19Z 46.521389  14.854444 ddaa40aaa22411    
58188 2008-12-30T15:30:08Z 46.522621  14.849618 58e12bc0d67e11    
58189 2009-04-08T07:36:46Z 46.554722  15.646667 ddaf9c4ea22411    
58190 2009-04-08T07:01:28Z 46.421389  15.869722 dd793f96a22411    

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

The SNAP data set is 0-based, with nodes numbered 0 to 58,227.

In the SuiteSparse Matrix Collection the graph is converted to 1-based.
The Problem.A matrix is the undirected friendship network, where
A(i,j)=1 if person 1+i and person...

Die Studie über Facebook-Nutzer wurde von infratest dimap im Auftrag der Konrad-Adenauer-Stiftung durchgeführt. Im Erhebungszeitraum 26. November bis 4. Dezember 2018 wurden 2.041 Facebook-Nutzer in Onlineinterviews (CAWI) zu folgenden Themen befragt: Internetnutzung, Facebook-Gruppen, Facebooknutzung, politische Inhalte auf Facebook, Reaktion auf Inhalte, Bildexperiment und Sonntagsfrage. Die Auswahl der Befragten erfolgte durch eine Quotenstichprobe aus einem Online-Access-Panel. Nutzung verschiedener Internetangebote (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, Online-Zeitungen, nichts davon); Nutzung offener oder geschlossener Facebook-Gruppen; Facebook-Inhalte zu politischen Themen, zu berufsbezogenen Themen, zu Hobbies, zur Unterhaltung bzw. zu anderen Themen; Art der Facebook-Nutzung (lese/ like/ teile Inhalte, schreibe Kommentare, verbreite eigene Inhalte); politische Facebook-Nutzung (lese/ like/ teile politische Inhalte, schreibe Kommentare zu politischen Themen, verbreite eigenen Inhalt zu politischen Themen); Reaktion auf Facebook- Inhalte bzw. Kommentare (fühle mich informiert, unterhalten, verärgert, provoziert); Zustimmung zu verschiedenen Aussagen zu Facebook (auf Facebook regen mich andere auf, zeige ich anderen ihre Grenzen, kann ich anonym meine Meinung sagen, finde ich viele verschiedene Meinungen, finde ich Meinungen, die sonst unterdrückt werden, traue ich mich Dinge zu sagen/ teilen, die ich sonst nicht sagen würde); Parteipräferenz (Sonntagsfrage); Kommentar (offen) zu einem provozierenden Bild (Split A: Flüchtlinge, Split B: Pegida). Demographie: Geschlecht; Alter (Geburtsjahr); Bildung; Erwerbstätigkeit; berufliche Stellung; Haushaltsnettoeinkommen (gruppiert); Bundesland. Zusätzlich verkodet wurde: lfd. Nummer; Gewichtungsfaktor. The study on Facebook users was conducted by infratest dimap on behalf of the Konrad Adenauer Foundation. During the survey period from November 26 to December 4, 2018, 2,041 Facebook users were surveyed in online interviews (CAWI) on the following topics: internet use, Facebook groups, Facebook use, political content on Facebook, reaction to content, image experiment and Sunday question. Respondents were selected by quota sampling from an online access panel. Use of various internet services (Tinder, Facebook, Twitter, snapchat, Instagram, YouTube, online newspapers, none of the above); use of open or closed Facebook groups; Facebook content on political topics, on job-related topics, on hobbies, on entertainment or on other topics; type of Facebook use (read/ like/ share content, write comments, disseminate own content); political Facebook use (read/ like/ share political content, write comments on political topics, disseminate own content on political topics); reaction to Facebook content or comments (do I feel informed, entertained, annoyed, provoked, etc.). comments (feel informed, entertained, annoyed, provoked); agreement with various statements on Facebook (on Facebook others upset me, I show others their limits, I can speak my mind anonymously, I find many different opinions, I find opinions that are otherwise suppressed, I dare to say/ share things I would not otherwise say); party preference (Sunday question); comment (open) on a provocative image (split A: refugees, split B: Pegida). Demography: sex; age (year of birth); education; employment; occupational status; net household income (grouped); federal state. Additionally coded: serial number; weighting factor.

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”.

Ask Ubuntu temporal network

https://snap.stanford.edu/data/sx-askubuntu.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Ask Ubuntu (http://askubuntu.com/). There are three different types of
interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph sx-askubuntu-a2q) user u commented on user v's question at time t (in the graph
sx-askubuntu-c2q) user u commented on user v's answer at time t (in the
graph sx-askubuntu-c2a)

The graph sx-askubuntu contains the union of these graphs. These graphs
were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-askubuntu)
Nodes 159,316
Temporal Edges 964,437
Edges in static graph 596,933
Time span 2613 days

Dataset statistics (sx-askubuntu-a2q)
Nodes 137,517
Temporal Edges 280,102
Edges in static graph 262,106
Time span 2613 days

Dataset statistics (sx-askubuntu-c2q)
Nodes 79,155
Temporal Edges 327,513
Edges in static graph 198,852
Time span 2047 days

Dataset statistics (sx-askubuntu-c2a)
Nodes 75,555
Temporal Edges 356,822
Edges in static graph 178,210
Time span 2418 days

Source (citation)
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal Networks." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017.

Files
File Description
sx-askubuntu.txt.gz All interactions
sx-askubuntu-a2q.txt.gz Answers to questions
sx-askubuntu-c2q.txt.gz Comments to questions
sx-askubuntu-c2a.txt.gz Comments to answers

Data format

SRC DST UNIXTS                             

where edges are separated by a new line and

SRC: id of the source node (a user)                  
TGT: id of the target node (a user)                  
UNIXTS: Unix timestamp (seconds since the epoch)            

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

The SNAP graph is 1-based, with nodes in all graphs numbered 1 to
n=515,280.

In the SuiteSparse Matrix Collection, the primary matrix, Problem.A, is
the overall static graph, with 596,993 edges, of size n-by-n with
n=159,316. These edges represent the 964,437 temporal edges. A(i,j) is
the number of times person u=nodeid(i) interacted with person v=nodeid(j), with a temporal edge (u,v,t), with any kind of interaction.
Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set.

A2Q = Problem.aux.Q2A is the static sx-askubuntu-a2q graph.
C2Q = Problem.aux.C2Q is the static sx-askubuntu-c2q graph.
C2A = Problem.aux.C2A is the static sx-askubuntu-c2a graph.
These sum together to give the the overall graph. That is,
A = A2Q + C2Q + C2A.

A2Q(u,v) is the number of times person u answered v's questions.
C2Q(u,v) is the number of times person u commented on v's question.
C2A(u,v) is the number of times person u commented on v's answer.

The temporal edges are held in:
Problem.aux.temporal_edges: [964437x3]
Problem.aux.temporal_edges_a2q: [280102x3]
Problem.aux.temporal_edges_c2q: [327513x3]
Problem.aux.temporal_edges_c2a: [356822x3]

Each row in these matrices is a single temporal edge, (u,v,t). Summing up all entries in A gives 964,437, and likewise the sum of entries in the
other graphs gives the number of temporal edges they represent.

Math Overflow temporal network

https://snap.stanford.edu/data/sx-mathoverflow.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Math Overflow (http://mathoverflow.net/). There are three different types
of interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph
sx-mathoverflow-a2q) user u commented on user v's question at time t (in
the graph sx-mathoverflow-c2q) user u commented on user v's answer at time t (in the graph sx-mathoverflow-c2a)

The graph sx-mathoverflow contains the union of these graphs. These graphs were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-mathoverflow)
Nodes 24,818
Temporal Edges 506,550
Edges in static graph 239,978
Time span 2350 days

Dataset statistics (sx-mathoverflow-a2q)
Nodes 21,688
Temporal Edges 107,581
Edges in static graph 90,489
Time span 2350 days

Dataset statistics (sx-mathoverflow-c2q)
Nodes 16,836

Total-Asset-Turnover Time Series for Snap Inc. Snap Inc. operates as a technology company in North America, Europe, and internationally. The company offers Snapchat, a visual messaging application with various tabs, such as camera, visual messaging, snap map, stories, and spotlight that enable people to communicate visually through short videos and images. It also provides Snapchat+, a subscription service that provides subscribers access to exclusive, experimental, and pre-release features; Spectacles, an eyewear product; and advertising products, including AR ads and Snap ads comprises a single image or video ads, collection ads, dynamic ads, story ads, commercials, sponsored snaps, and promoted places. The company was formerly known as Snapchat, Inc. and changed its name to Snap Inc. in September 2016. Snap Inc. was founded in 2010 and is headquartered in Santa Monica, California.

Stock Price Time Series for Snap Inc. Snap Inc. operates as a technology company in North America, Europe, and internationally. The company offers Snapchat, a visual messaging application with various tabs, such as camera, visual messaging, snap map, stories, and spotlight that enable people to communicate visually through short videos and images. It also provides Snapchat+, a subscription service that provides subscribers access to exclusive, experimental, and pre-release features; Spectacles, an eyewear product; and advertising products, including AR ads and Snap ads comprises a single image or video ads, collection ads, dynamic ads, story ads, commercials, sponsored snaps, and promoted places. The company was formerly known as Snapchat, Inc. and changed its name to Snap Inc. in September 2016. Snap Inc. was founded in 2010 and is headquartered in Santa Monica, California.

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