This dataset consists of circles (or lists ) from Twitter. Twitter data was crawled from public sources. The dataset includes node features (profiles), circles, and ego networks. Data is also available from Facebook and Google+. ##Dataset statistics |Attribute| Value| |————-|————| |Nodes| 81306| |Edges| 1768149| |Nodes in largest WCC |81306 (1.000)| |Edges in largest WCC| 1768149 (1.000)| |Nodes in largest SCC| 68413 (0.841)| |Edges in largest SCC |1685163 (0.953)| |Average clustering coefficient| 0.5653| |Number of triangles| 13082506| |Fraction of closed triangles| 0.06415| |Diameter (longest shortest path)| 7| |90-percentile effective diameter| 4.5|

Note: w(ej) refers to the weight of edge ej, refers to the strength of node ) and randi{1..9} refers to a randomly selected integers between 1 and 9 (inclusive).Summary of weighted network experiments.

Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users. The United States and China account for the most high-profile social platforms Most top ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network: a little app called TikTok. How many people use social media? The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2025, social networking sites are estimated to reach 5.42 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

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.

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

In 2024, ** percent of respondents to a survey in the United States said that they used Facebook for news. Facebook remains the leading social media network for news consumption among U.S. consumers. In second place was YouTube, with ** percent, marking a jump from the previous year.

The Data Center Networking Market Can Be Segmented by Product (ethernet Switches, Storage Area Network, Routers, and Other Products), End-User Vertical (BFSI, Healthcare, Retail, Government, and Other End-User Verticals), and Geography (North America, Europe, Asia-Pacific Latin America, and Middle East & Africa). The Market Size and Forecasts are Provided in Terms of Value (USD ) for all the Above Segments.

interaction networks, interaction graphs, enron email communications, university email graph, reality mining graph data, Facebook messages data, escort graph, wiki-Talk, human contact network, proximity networks, Infectious contact networks

The U.S.-based telecommunications company Verizon has registered a significant increase in the usage of data on its networks in the United States on March 19 compared to March 12 due to restrictions in place triggered by the coronavirus (COVID-19) pandemic. VPN traffic for example was up by 25 percent during this small sample time period.

For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Fact and Figures page.

.R files providing weights for networks trained on the simulated BSM, Black and Heston model data as well as the market data provided in this collection. There are four networks for each dataset relating to the standard network (MLP), soft-contrained network (SC) and the two hard constained networks HC1 and HC2.

Network datasets used as examples for network cards.

This repository contains network graphs and network metadata from Moviegalaxies, a website providing network graph data from about 773 films (1915–2012). The data includes individual network graph data in Graph Exchange XML Format and descriptive statistics on measures such as clustering coefficient, degree, density, diameter, modularity, average path length, the total number of edges, and the total number of nodes.

Poland Wire Telephone Networks: Local Fixed Network: Length data was reported at 812,452.000 km in 2016. This records an increase from the previous number of 740,190.000 km for 2015. Poland Wire Telephone Networks: Local Fixed Network: Length data is updated yearly, averaging 710,688.000 km from Dec 1999 (Median) to 2016, with 17 observations. The data reached an all-time high of 812,452.000 km in 2016 and a record low of 540,834.000 km in 1999. Poland Wire Telephone Networks: Local Fixed Network: Length data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.TB004: Wire Telephone Networks Statistics.

Twitter Reply Networks We apply our techniques to Twitter reply networks. These networks are constructed from tweets we collected via Twitter gardenhose API service between September 9, 1998 and November 17, 1998. Each network is weighted and directed, whereby entries in the (i,j) cell of the adjacency matrix represent the number of replies directed from node i to node j. Note that there is no correlation between node labels from week to week. For example, the individual represented by node 1 in Week 1 is not the same individual represented by node 1 in Weeks 2, 3 and so forth. Each network is presented as a Matlab (.mat) file. Please cite as: Bliss, C. A., Danforth, C. M. & P. S. Dodds. (2014). Estimation of Global Network Statistics from Incomplete Data. PLOSONE (Accepted). For additional data, see: http://www.uvm.edu/~storylab/share/papers/bliss2014a/data.html

This report documents the acquisition of source data, and calculation of land cover summary statistics datasets for six National Park Service Klamath Network park units and seven custom areas of analysis: Crater Lake National Park, Lassen Volcanic National Park, Lava Beds National Monument, Oregon Caves National Monument, Redwood National and State Parks, Whiskeytown National Recreation Area, and the seven custom areas of analysis. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and the United States Geological Survey’s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the six Klamath Network park units and seven custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

The Portuguese region with the highest social network usage in 2024 was Greater Lisbon, with 75 percent of its population participating in social networks. Following, Península de Setúbal had a share of 73.43 percent. The region with the lowest social network use was Central Portugal, with 66.21 percent.

trophic dynamics, food web cohesion data, ecology graph data, ecology network data, download ecology network data

Introduction

UK Power Network maintains the 132kV voltage level network and below. An important part of the distribution network is distributing this electricity across our regions through circuits. Electricity enters our network through Super Grid Transformers at substations shared with National Grid we call Grid Supply Points. It is then sent at across our 132 kV Circuits towards our grid substations and primary substations. From there, electricity is distributed along the 33 kV circuits to bring it closer to the home. These circuits can be viewed on the single line diagrams in our Long-Term Development Statements (LTDS) and the underlying data is then found in the LTDS tables.

This dataset provides half-hourly current and power flow data across these named circuits from 2021 through to the previous month in our South Eastern Power Networks (SPN) licence area. The data are aligned with the same naming convention as the LTDS for improved interoperability.

Care is taken to protect the private affairs of companies connected to the 33 kV network, resulting in the redaction of certain circuits. Where redacted, we provide monthly statistics to continue to add value where possible. Where monthly statistics exist but half-hourly is absent, this data has been redacted.

To find which circuit you are looking for, use the ‘ltds_line_name’ that can be cross referenced in the 33kV Circuits Monthly Data, which describes by month what circuits were triaged, if they could be made public, and what the monthly statistics are of that site.

If you want to download all this data, it is perhaps more convenient from our public sharepoint: Sharepoint

This dataset is part of a larger endeavour to share more operational data on UK Power Networks assets. Please visit our Network Operational Data Dashboard for more operational datasets.

Methodological Approach The dataset is not derived, it is the measurements from our network stored in our historian. The measurement devices are taken from current transformers attached to the cable at the circuit breaker, and power is derived combining this with the data from voltage transformers physically attached to the busbar. The historian stores datasets based on a report-by-exception process, such that a certain deviation from the present value must be reached before logging a point measurement to the historian. We extract the data following a 30-min time weighted averaging method to get half-hourly values. Where there are no measurements logged in the period, the data provided is blank; due to the report-by-exception process, it may be appropriate to forward fill this data for shorter gaps. We developed a data redactions process to protect the privacy or companies according to the Utilities Act 2000 section 105.1.b, which requires UK Power Networks to not disclose information relating to the affairs of a business. For this reason, where the demand of a private customer is derivable from our data and that data is not already public information (e.g., data provided via Elexon on the Balancing Mechanism), we redact the half-hourly time series, and provide only the monthly averages. This redaction process considers the correlation of all the data, of only corresponding periods where the customer is active, the first order difference of all the data, and the first order difference of only corresponding periods where the customer is active. Should any of these four tests have a high linear correlation, the data is deemed redacted. This process is not simply applied to only the circuit of the customer, but of the surrounding circuits that would also reveal the signal of that customer. The directionality of the data is not consistent within this dataset. Where directionality was ascertainable, we arrange the power data in the direction of the LTDS "from node" to the LTDS "to node". Measurements of current do not indicate directionality and are instead positive regardless of direction. In some circumstances, the polarity can be negative, and depends on the data commissioner's decision on what the operators in the control room might find most helpful in ensuring reliable and secure network operation. Quality Control Statement The data is provided "as is".
In the design and delivery process adopted by the DSO, customer feedback and guidance is considered at each phase of the project. One of the earliest steers was that raw data was preferable. This means that we do not perform prior quality control screening to our raw network data. The result of this decision is that network rearrangements and other periods of non-intact running of the network are present throughout the dataset, which has the potential to misconstrue the true utilisation of the network, which is determined regulatorily by considering only by in-tact running arrangements. Therefore, taking the maximum or minimum of these measurements are not a reliable method of correctly ascertaining the true utilisation. This does have the intended added benefit of giving a realistic view of how the network was operated. The critical feedback was that our customers have a desire to understand what would have been the impact to them under real operational conditions. As such, this dataset offers unique insight into that. Assurance StatementCreating this dataset involved a lot of human data imputation. At UK Power Networks, we have differing software to run the network operationally (ADMS) and to plan and study the network (PowerFactory). The measurement devices are intended to primarily inform the network operators of the real time condition of the network, and importantly, the network drawings visible in the LTDS are a planning approach, which differs to the operational. To compile this dataset, we made the union between the two modes of operating manually. A team of data scientists, data engineers, and power system engineers manually identified the LTDS circuit from the single line diagram, identified the line name from LTDS Table 2a/b, then identified the same circuit in ADMS to identify the measurement data tags. This was then manually inputted to a spreadsheet. Any influential customers to that circuit were noted using ADMS and the single line diagrams. From there, a python code is used to perform the triage and compilation of the datasets. There is potential for human error during the manual data processing. These issues can include missing circuits, incorrectly labelled circuits, incorrectly identified measurement data tags, incorrectly interpreted directionality. Whilst care has been taken to minimise the risk of these issues, they may persist in the provided dataset. Any uncertain behaviour observed by using this data should be reported to allow us to correct as fast as possible. Additional Information Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary. Download dataset information: Metadata (JSON)We would be grateful if you find this dataset useful to submit a “reuse” case study to tell us what you did and how you used it. This enables us to drive our direction and gain better understanding for how we improve our data offering in the future. Click here for more information:Open Data Portal Reuses — UK Power Networks

Contains the benchmark Bayesian network dataset

Poland Wire Telephone Networks: Local Fixed Network: Cable: Pipe Cable data was reported at 35,390,745.000 km in 2016. This records an increase from the previous number of 33,063,235.000 km for 2015. Poland Wire Telephone Networks: Local Fixed Network: Cable: Pipe Cable data is updated yearly, averaging 33,286,078.500 km from Dec 1999 (Median) to 2016, with 18 observations. The data reached an all-time high of 35,390,745.000 km in 2016 and a record low of 25,528,732.000 km in 1999. Poland Wire Telephone Networks: Local Fixed Network: Cable: Pipe Cable data remains active status in CEIC and is reported by Central Statistical Office. The data is categorized under Global Database’s Poland – Table PL.TB004: Wire Telephone Networks Statistics.