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Source Paper: https://arxiv.org/abs/1802.06916
Usage
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="email-Eu", split="train")
Citation
@article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi =… See the full description on the dataset page: https://huggingface.co/datasets/SauravMaheshkar/email-Eu.
EmailEU is a directed temporal network constructed from email exchanges in a large European research institution for a 803-day period. It contains 986 email addresses as nodes and 332,334 emails as edges with timestamps. There are 42 ground truth departments in the dataset.
During a 2023 survey carried out among email marketers from the United States, the United Kingdom, and other European countries, it was found that automatic content and image generation was the most interesting application of artificial intelligence (AI) in email marketing, named by approximately ** percent of respondents. It was closely followed by personalization of content and newsletters, mentioned by ** percent of the interviewed.
This statistic shows the percentage of individuals in selected European countries who had used the internet for e-mail communication from 2017 to 2020. The share of individuals who send or received e-mails was highest in Denmark, with 96 percent of individuals using the internet in this way.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
European Union - Individuals using the internet for sending/receiving e-mails was 80.42% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for European Union - Individuals using the internet for sending/receiving e-mails - last updated from the EUROSTAT on June of 2025. Historically, European Union - Individuals using the internet for sending/receiving e-mails reached a record high of 80.42% in December of 2024 and a record low of 55.00% in December of 2009.
This dataset includes graph theory indicators (centrality and clustering coefficients) for the Stanford Network Analysis Project (SNAP) "email-Eu-core-temporal" network, a well-known reference dataset for Social Network Analysis (SNA) of e-mail traffic.
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Access Europe Email Marketing Software Industry Overview which includes Europe country analysis of (United Kingdom, France, Germany, Italy, Russia, Spain, Sweden, Denmark, Switzerland, Luxembourg, Rest of Europe), market split by Industry, Application, Channel, Deployment, Enterprise
We use the Enron email dataset to build a network of email addresses. It contains 614586 emails sent over the period from 6 January 1998 until 4 February 2004. During the pre-processing, we remove the periods of low activity and keep the emails from 1 January 1999 until 31 July 2002 which is 1448 days of email records in total. Also, we remove email addresses that sent less than three emails over that period. In total, the Enron email network contains 6 600 nodes and 50 897 edges. To build a graph G = (V, E), we use email addresses as nodes V. Every node vi has an attribute which is a time-varying signal that corresponds to the number of emails sent from this address during a day. We draw an edge eij between two nodes i and j if there is at least one email exchange between the corresponding addresses. Column 'Count' in 'edges.csv' file is the number of 'From'->'To' email exchanges between the two addresses. This column can be used as an edge weight. The file 'nodes.csv' contains a dictionary that is a compressed representation of time-series. The format of the dictionary is Day->The Number Of Emails Sent By the Address During That Day. The total number of days is 1448. 'id-email.csv' is a file containing the actual email addresses.
This statistic displays the findings of a survey on the frequency of experiences of social media or email accounts being hacked in the European Union (EU) as of 2019. During the survey period, it was found that ** percent of respondents reported that they experienced such situations at least once.
Access Email Address Data for IT businesses across Europe with Success.ai. Includes verified work emails, firmographic data, and employee counts. Continuously updated datasets. Best price guaranteed.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The network was generated using email data from a large European research institution. For a period from October 2003 to May 2005 (18 months) we have anonymized information about all incoming and outgoing email of the research institution. For each sent or received email message we know the time, the sender and the recipient of the email. Overall we have 3,038,531 emails between 287,755 different email addresses. Note that we have a complete email graph for only 1,258 email addresses that come from the research institution. Furthermore, there are 34,203 email addresses that both sent and received email within the span of our dataset. All other email addresses are either non-existing, mistyped or spam.
Given a set of email messages, each node corresponds to an email address. We create a directed edge between nodes i and j, if i sent at least one message to j.
Enron email communication network covers all the email communication within a dataset of around half million emails. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. Nodes of the network are email addresses and if an address i sent at least one email to address j, the graph contains an undirected edge from i to j. Note that non-Enron email addresses act as sinks and sources in the network as we only observe their communication with the Enron email addresses.
The Enron email data was originally released by William Cohen at CMU.
Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. Each registered user has a talk page, that she and other users can edit in order to communicate and discuss updates to various articles on Wikipedia. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all user talk page changes and created a network.
The network contains all the users and discussion from the inception of Wikipedia till January 2008. Nodes in the network represent Wikipedia users and a directed edge from node i to node j represents that user i at least once edited a talk page of user j.
The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.
The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).
Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.
The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.
SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.
During a 2023 survey carried out among email marketers from the United States, the United Kingdom, and other European countries, it was found that approximately ** percent of respondents believed that AI-supported email marketing was more effective than traditional email marketing approaches. Roughly ***percent noticed no significant difference or said they believed it was as effective as traditional approaches.
Success.ai's B2B Email Data for European Professionals offers unprecedented access to a vast dataset of over 700 million verified profiles, meticulously curated to empower your marketing and sales strategies across Europe. This comprehensive database includes work emails, phone numbers, and extensive professional histories, providing the key details you need to connect with decision-makers and influencers in various industries.
Why Choose Success.ai’s B2B Email Data?
Extensive European Coverage: Our dataset spans across the entire European continent, including both EU and non-EU countries, ensuring you can reach professionals in key markets. Verified Contact Details: Each profile is thoroughly verified for accuracy, ensuring you have the most reliable emails and contact numbers at your fingertips. In-depth Professional Histories: Gain insights into the careers of potential leads, including their past roles, industries of expertise, and professional achievements. Data Features:
Work Emails and Phone Numbers: Direct communication channels to engage with prospects effectively. Professional Backgrounds: Detailed histories to help you tailor your outreach and personalize communication. Industry and Role Segmentation: Data segmented by industry and job role to refine your targeting and increase conversion rates. Flexible Delivery and Integration: Our data can be delivered in various formats such as CSV, Excel, or through an API, allowing for easy integration into your existing CRM systems or marketing platforms. This flexibility ensures that you can start leveraging the data quickly, with minimal setup time required.
Competitive Pricing with Best Price Guarantee: We are committed to providing you the best value for your investment. Our Best Price Guarantee ensures you receive the highest quality data at the most competitive rates in the market.
Targeted Applications for B2B Email Data:
Lead Generation: Identify and connect with potential clients by utilizing accurate contact data to support cold emailing and telemarketing efforts. Account-Based Marketing (ABM): Enhance your ABM campaigns by reaching the key stakeholders in target companies directly. Market Research: Use detailed professional backgrounds to analyze market trends and understand the competitive landscape. Event Promotion: Drive attendance to webinars, conferences, and trade shows by reaching out to relevant professionals. Quality Assurance and Compliance:
Data Accuracy: Our stringent verification processes ensure a high level of accuracy, with regular updates to keep the data fresh. Compliance with Data Protection Laws: All data is collected and processed in compliance with GDPR and other relevant legislation, ensuring lawful and ethical use. Support and Consultation:
Customer Support: Our dedicated support team is available to assist with any queries or issues you may encounter. Consultation Services: Benefit from our expertise in data-driven marketing and sales strategies through personalized consultation sessions. Get Started with Success.ai Today: Empower your business with Success.ai’s B2B Email Data for European Professionals and start building meaningful connections that drive growth. Contact us to explore our data solutions and discover how we can help you achieve your business objectives.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
LiveJournal is a free on-line blogging community where users declare friendship each other. LiveJournal 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 LiveJournal friendship social network and ground-truth communities.
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.
Friendster is an on-line gaming network. Before re-launching as a game website, Friendster was a social networking site where users can form friendship edge each other. Friendster social network also allows users form a group which other members can then join. We consider such user-defined groups as ground-truth communities. For the social network, we take the induced subgraph of the nodes that either belong to at least one community or are connected to other nodes that belong to at least one community. This data is provided by The Web Archive Project, where the full graph is available.
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.
Orkut 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.
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.
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.
The DBLP computer science bibliography provides a comprehensive list of research papers in computer science. We construct a co-authorship network where two authors are connected if they publish at least one paper together. Publication venue, e.g, journal or conference, defines an individual ground-truth community; authors who published to a certain journal or conference form a community.
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.
Network was collected by crawling Amazon website. It is based on Customers Who Bought This Item Also Bought feature of the Amazon website. If a product i is frequently co-purchased with product j, the graph contains an undirected edge from i to j. Each product category provided by Amazon defines each ground-truth community.
We regard each connected component in a product category 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.
The network was generated using email data from a large European research institution. We have anonymized information about all incoming and outgoing email between members of the research institution. Th...
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Euro Area - Individuals using the internet for sending/receiving e-mails was 79.60% in December of 2022, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Euro Area - Individuals using the internet for sending/receiving e-mails - last updated from the EUROSTAT on June of 2025. Historically, Euro Area - Individuals using the internet for sending/receiving e-mails reached a record high of 79.60% in December of 2022 and a record low of 58.00% in December of 2009.
This statistic illustrates the results of a survey on checking work messages and emails while on holiday, in selected European countries in 2018. According to the study published by Ipsos, ** percent of Germans agreed that they never check for messages/emails back at their work when they go on vacation. Those in Russia and Serbia were more likely to check their messages, with only ** percent and ** percent of respondents saying they don't, respectively.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
France - Individuals using the internet for sending/receiving e-mails was 86.83% in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Individuals using the internet for sending/receiving e-mails - last updated from the EUROSTAT on July of 2025. Historically, France - Individuals using the internet for sending/receiving e-mails reached a record high of 86.83% in December of 2024 and a record low of 62.00% in December of 2009.
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Source Paper: https://arxiv.org/abs/1802.06916
Usage
from torch_geometric.datasets.cornell import CornellTemporalHyperGraphDataset
dataset = CornellTemporalHyperGraphDataset(root = "./", name="email-Eu", split="train")
Citation
@article{Benson-2018-simplicial, author = {Benson, Austin R. and Abebe, Rediet and Schaub, Michael T. and Jadbabaie, Ali and Kleinberg, Jon}, title = {Simplicial closure and higher-order link prediction}, year = {2018}, doi =… See the full description on the dataset page: https://huggingface.co/datasets/SauravMaheshkar/email-Eu.