The Intelligent Road Network dataset provided by the Transport Department includes traffic directions, turning restrictions at road junctions, stopping restrictions, on-street parking spaces and other road traffic data for supporting the development of intelligent transport system, fleet management system and car navigation etc. by the public.

Esri China (HK) has prepared this File Geodatabase containing a Network Dataset for the Intelligent Road Network to support Esri GIS users to use the dataset in ArcGIS Pro without going through long configuration steps. Please refer to this guideline to use the Road Network Dataset in ArcGIS Pro for routing analysis. This network dataset has been configured and deployed the following restrictions:

Speed LimitTurnIntersectionTraffic FeaturesPedestrian ZoneTraffic Sign of ProhibitionVehicle RestrictionThe coordinate system of this dataset is Hong Kong 1980 Grid.The objectives of uploading the network dataset to ArcGIS Online platform are to facilitate our Hong Kong ArcGIS users to utilize the data in a spatial ready format and save their data conversion effort.For details about the schema and information about the content and relationship of the data, please refer to the data dictionary provided by Transport Department at https://data.gov.hk/en-data/dataset/hk-td-tis_15-road-network-v2.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.Dataset last updated on: 2021 July

This water flow network dataset is a route feature class rather than a simple polyline. The geometry is generated by merging the river lines of individual geometric network datasets. This layer contains an integrated flow network that includes known flow connections through rivers, lakes and groundwater aquifers. In places where the network is depicted flowing through lakes or through underground channels, the flow channels are schematic only, and do not represent the precise location of these flow channels. The appropriate Geological Survey Ireland data sets should be consulted where underground flows or connections are known or suspected.

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 global social media penetration rate in was forecast to continuously increase between 2024 and 2028 by in total 11.6 (+18.19 percent). After the ninth consecutive increasing year, the penetration rate is estimated to reach 75.31 and therefore a new peak in 2028. Notably, the social media penetration rate of was continuously increasing over the past years.

Note.n = number of nodes; m = number of edges; nCC = number of nodes of the largest (strongly) connected component; ⟨k⟩ = average node degree; D = diameter of the network; L = average shortest path length; C = clustering coefficient; DKS = Kolmogorov–Smirnov statistic between the model and the data; r = correlation coefficient between local clustering coefficient C(k) and node degree k on a log-log plot; β = hierarchical exponent; Lrandom = average shortest path length of the random network with the same size and density; Crandom = clustering coefficient of the random network with the same size and density.Statistics for the simulated network generated by the proposed network models.

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.

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 2022, Social networking sites are estimated to reach 3.96 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.

The National Network dataset is as of December 22, 2020 and is from the Bureau of Transportation Statistics (BTS) along with the Federal Highway Administration (FHWA), and part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Network was authorized by the Surface Transportation Assistance Act of 1982 (P.L. 97-424) and specified in the U.S. Code of Federal Regulations (23 CFR 658) to require states to allow conventional combinations on "the Interstate System and those portions of the Federal-aid Primary System serving to link principal cities and densely developed portions of the states on high volume routes utilized extensively by large vehicles for interstate commerce which do not have any unusual characteristics causing current or anticipated safety problems. “The National Network (NN) includes almost all of the Interstate Highway System and other, specified non-Interstate highways. The network comprises more than 200,000 miles of highways. The National Network supports interstate commerce by regulating the size of trucks. This file is a geospatial representation of the National Network as described in 23 CFR 658 Appendix A and should not be interpreted as the official National Network and should not be used for truck size and weight enforcement purposes or for navigation.

This file includes the locations and nature of street closures, one-way conversions and reversals, and two-way conversions within the five boroughs of New York City. It does NOT contain information on closures or temporary re-routing due to construction or special events. NYCDOT estimates these changes occur less than once a month. The New York City Department of Transportation makes no warranty of accuracy or completeness of this data. Drivers should always comply with signals, signs and other existing regulations.

The Freight Analysis Framework (FAF5) - Network Nodes dataset was created from 2017 base year data and was published on April 11, 2022 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The FAF (Version 5) Network Nodes contains 348,498 node features. All node features are topologically connected to permit network pathbuilding and vehicle assignment using a variety of assignment algorithms. The FAF Node and the FAF Link datasets can be used together to create a network. The link features in the FAF Network dataset include all roads represented in prior FAF networks, and all roads in the National Highway System (NHS) and the National Highway Freight Network (NHFN) that are currently open to traffic. Other included links provide connections between intersecting routes, and to select intermodal facilities and all U.S. counties. The network consists of over 588,000 miles of equivalent road mileage. The dataset covers the 48 contiguous States plus the District of Columbia, Alaska, and Hawaii.

Collecting network information on political elites using conventional methods such as surveys and text records is challenging in authoritarian and/or conflict-ridden states. I introduce a data collection method for elite networks using scraping algorithms to capture public co-appearances at political and social events. Validity checks using existing data show the method effectively replicates interaction-based networks but not networks based on behavioral similarities; in both cases, measurement error remains a concern. Applying the method to Nigeria illustrates that patronage---measured in terms of public connectivity---does not drive national-oil-company appointments. Given that theories of elite behavior aim to understand individual-level interactions, the applicability of data using this technique is well-suited to situations where intrusive data collection is costly or prohibitive.

In most existing low voltage network datasets, the impedance data is approximate, with the following two approximations being commonly applied: 1. a Kron-reduction, which assumes that the neutral wire is grounded everywhere; 2. discarding off-diagonal elements of the series impedance matrix in sequence coordinates.

This dataset, contains four-wire variants of low voltage networks for power flow simulation purposes. This means the impedance matrices associate with the lines have size 4x4 and are defined in the wire coordinate space.

Furthermore, it contains approximations derived from the four-wire network models, with 3x3 impedance matrices, including Kron-reduced, phase-to-neutral, and modified-phase-to-neutral transformations. Lineage: We first compiled a library of detailed line models. Next, for each line in the datatset, we assign the four-wire line model which matches the original one most closely in terms of positive sequence impedance.

This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.

The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap.

Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.

Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.

Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.

Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 When using this dataset, please cite also the above-mentioned paper.

The FTC produces the Consumer Sentinel Network Data Book annually using a data set of fraud, identity theft, and other reports from consumers received by the Consumer Sentinel Network. These include reports made directly by consumers to the FTC, as well as reports received by federal, state, local, and international law enforcement agencies and other non-governmental organizations. This data set includes national statistics, as well as a state-by-state listing of top report categories in each state and a listing of metropolitan areas that generated the most complaints per capita, for calendar year 2016.

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.

The North American Rail Network (NARN) Rail Nodes dataset was created in 2016 and was updated on January 29, 2025 from the Federal Railroad Administration (FRA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The NARN Rail Nodes dataset is a database of North America's railway system at 1:24,000 or better within the United States. The data set covers all 50 States, the District of Columbia, Mexico, and Canada. The dataset holds topology of the network and provides geographic location information. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529070

Paper abstract: Permutation tests have a long history in testing hypotheses of independence between nodal attributes and network structure, though they are often thought less informative than parametric modeling techniques. In this paper, we show that when the nodal attribute is random assignment to a treatment condition, permutation tests provide a valid test of the causal effect of treatment. We discuss existing test statistics used in network permutation tests and propose several new statistics. In simulations we find that these statistics perform well compared to parametric tests and that specific statistics can be selected to provide power against common network models. We illustrate the methods with gene-wide association study performed on randomized study participants and an observational study of gender membership on Scandinavian corporate boards.

This replication archive contains all materials to recreate analysis, figures, and the paper itself. See README.txt for system requirements.

The share of internet users who visit social networks is expected to keep increasing in the upcoming years. As of 2019, 77.5 percent of internet users globally had visited a social network via any device at least once a month. The corresponding figure for 2024 is expected to reach 82.3 percent. Over 4.4 billion monthly active social media users are expected by 2025.

This dataset has been produced during a small sub-project of APIS (https://apis.acdh.oeaw.ac.at). It contains data from 151 annotated biographies of the Austrian Biographical Dictionary. These selection of biographical articles describe the life and career steps of historians, librarians, teachers etc. These particular texts have been manually annotated through the webapplication APIS. Through these annotations relations between different kinds of entities were established. The result are biographical data which can be used for network visualization or statistical queries.

Group descriptive statistics and network parameters.