Description. This is the data used in the experiment of the following conference paper:

N. Arınık, R. Figueiredo, and V. Labatut, “Signed Graph Analysis for the Interpretation of Voting Behavior,” in International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities, Graz, AT, 2017, vol. 2025. ⟨hal-01583133⟩

Source code. The code source is accessible on GitHub: https://github.com/CompNet/NetVotes

Citation. If you use the data or source code, please cite the above paper.

@InProceedings{Arinik2017, author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent}, title = {Signed Graph Analysis for the Interpretation of Voting Behavior}, booktitle = {International Conference on Knowledge Technologies and Data-driven Business - International Workshop on Social Network Analysis and Digital Humanities}, year = {2017}, volume = {2025}, series = {CEUR Workshop Proceedings}, address = {Graz, AT}, url = {http://ceur-ws.org/Vol-2025/paper_rssna_1.pdf},}

Details.

RAW INPUT FILESThe 'itsyourparliament' folder contains all raw input files for further data processing (such as network extraction).The folder structure is as follows:* itsyourparliament/** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.** meps: There are 870 Member of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs# NETWORKS AND CORRESPONDING PARTITIONSThis work studies the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) and "Economic and Monetary Affairs" (ECON) for each separate year of the 7th EP term (2009-10, 2010-11, 2011-12, 2012-13, 2013-14). Note that the interpretation part (section 4) of the published paper is limited to only a few of these instances (2009-10 in ECON and 2012-13 in AGRI).The extracted networks are located in the "networks" folder and the corresponding partitions are in the "partitions" folder. Both folders have the same structure, which is as follows:COUNTRY-NAME|_DOMAIN-NAME|_2009-10|_2010-11|_2011-12|_2012-13|_2013-14## NETWORKSThe networks in this folder are used in the article. All those networks are the ones obtained after the filtering step (as explained in the article). The networks are in 'Graphml' format. These networks are enriched with some MEPs' properties (such as name, political party, etc.) associated with each node.## ALL NETWORKSFor those who are interested in other countries or domains, we make available all possible networks that we can extract from raw data with vs. without filtering step.COUNTRY-NAME|_m3|_negtr=NA_postr=NA: This folder contains all filtered networks. Note that the filtering step is explained in Section 2.1.2 of the article.|_bygroup|_bycountry|_negtr=0_postr=0: This folder contains all original networks (i.e. no filtering step).|_bygroup|_bycountry## PARTITIONSThe partitions are obtained in this way: First, the Ex-CC (exact) method is run and we denote 'k' for the the number of detected cluster in output. This 'k' value is the reference point in order to run the ILS-RCC (heuristic) method by specifying the number of desired cluster in output. Then, ILS-RCC is run with various values ('k', 'k+1', 'k+2'). All those results are integrated into the initial network graphml files and then converted into gephi format so that this will help dive in the results in interactive way.Note that we need to handle the absent MEPs in clustering results. Because, those MEPs correspond to isolated nodes in networks. Each isolated node is considered a single cluster node in Ex-CC results. We simply omit those nodes in order to find the 'k' (number of detected cluster) value before running ILS-RCC. Not also that ILS-RCC does not process isolated nodes such that an isolated node can be part of a cluster.

----------------------# COMPARISON RESULTSThe 'material-stats' folder contains all the comparison results obtained for Ex-CC and ILS-CC. The csv files associated with plots are also provided.The folder structure is as follows:* material-stats/** execTimePerf: The plot shows the execution time of Ex-CC and ILS-CC based on randomly generated complete networks of different size.** graphStructureAnalysis: The plots show the weights and links statistics for all instances.** ILS-CC-vs-Ex-CC: The folder contains 4 different comparisons between Ex-CC and ILS-CC: Imbalance difference, number of detected clusters, difference of the number of detected clusters, NMI (Normalized Mutual Information)

----------------------Funding: Agorantic FR 3621, FMJH Program Gaspard Monge in optimization and operation research (Project 2015-2842H)

The goal of this activity is to familiarize students with reading graphs, to think about how cells are different from one another and how parasites can take advantage of cell surface markers to gain access to a cell and escape immune detection. Then, learners view and reflect on an interview with scientist Dr. Angel Kongsomboonvech, who was involved in the research project.

Although methodologists have provided us ample notice of both the problem of nonproportional hazards (NPHs) and the means of correcting for them, less attention has been paid to the postestimation interpretation. The suggested inclusion of time interactions in our models is more than a statistical fix: These corrections alter the substantive meaning and interpretation of results. Framing the issue as a specific case of multiplicative-interaction modeling, I provide detailed discussion of the problem of NPHs and present several appropriate means of interpreting both the substantive impact and the significance of variables whose effects may change over time.

Freebase is amongst the largest public cross-domain knowledge graphs. It possesses three main data modeling idiosyncrasies. It has a strong type system; its properties are purposefully represented in reverse pairs; and it uses mediator objects to represent multiary relationships. These design choices are important in modeling the real-world. But they also pose nontrivial challenges in research of embedding models for knowledge graph completion, especially when models are developed and evaluated agnostically of these idiosyncrasies. We make available several variants of the Freebase dataset by inclusion and exclusion of these data modeling idiosyncrasies. This is the first-ever publicly available full-scale Freebase dataset that has gone through proper preparation.

Dataset Details The dataset consists of the four variants of Freebase dataset as well as related mapping/support files. For each variant, we made three kinds of files available:

Subject matter triples file

fb+/-CVT+/-REV One folder for each variant. In each folder there are 5 files: train.txt, valid.txt, test.txt, entity2id.txt, relation2id.txt Subject matter triples are the triples belong to subject matters domains—domains describing real-world facts.

Example of a row in train.txt, valid.txt, and test.txt:

2, 192, 0 Example of a row in entity2id.txt:

/g/112yfy2xr, 2 Example of a row in relation2id.txt:

/music/album/release_type, 192 Explaination

"/g/112yfy2xr" and "/m/02lx2r" are the MID of the subject entity and object entity, respectively. "/music/album/release_type" is the realtionship between the two entities. 2, 192, and 0 are the IDs assigned by the authors to the objects. Type system file

freebase_endtypes: Each row maps an edge type to its required subject type and object type.

Example

92, 47178872, 90 Explanation

"92" and "90" are the type id of the subject and object which has the relationship id "47178872". Metadata files

object_types: Each row maps the MID of a Freebase object to a type it belongs to.

Example

/g/11b41c22g, /type/object/type, /people/person Explanation

The entity with MID "/g/11b41c22g" has a type "/people/person" object_names: Each row maps the MID of a Freebase object to its textual label.

Example

/g/11b78qtr5m, /type/object/name, "Viroliano Tries Jazz"@en Explanation

The entity with MID "/g/11b78qtr5m" has name "Viroliano Tries Jazz" in English. object_ids: Each row maps the MID of a Freebase object to its user-friendly identifier.

Example

/m/05v3y9r, /type/object/id, "/music/live_album/concert" Explanation

The entity with MID "/m/05v3y9r" can be interpreted by human as a music concert live album. domains_id_label: Each row maps the MID of a Freebase domain to its label.

Example

/m/05v4pmy, geology, 77 Explanation

The object with MID "/m/05v4pmy" in Freebase is the domain "geology", and has id "77" in our dataset. types_id_label: Each row maps the MID of a Freebase type to its label.

Example

/m/01xljxh, /government/political_party, 147 Explanation

The object with MID "/m/01xljxh" in Freebase is the type "/government/political_party", and has id "147" in our dataset. entities_id_label: Each row maps the MID of a Freebase entity to its label.

Example

/g/11b78qtr5m, Viroliano Tries Jazz, 2234 Explanation

The entity with MID "/g/11b78qtr5m" in Freebase is "Viroliano Tries Jazz", and has id "2234" in our dataset. properties_id_label: Each row maps the MID of a Freebase property to its label.

Example

/m/010h8tp2, /comedy/comedy_group/members, 47178867 Explanation

The object with MID "/m/010h8tp2" in Freebase is a property(relation/edge), it has label "/comedy/comedy_group/members" and has id "47178867" in our dataset. uri_original2simplified and uri_simplified2original: The mapping between original URI and simplified URI and the mapping between simplified URI and original URI repectively.

Example

uri_original2simplified

"http://rdf.freebase.com/ns/type.property.unique": "/type/property/unique" uri_simplified2original

"/type/property/unique": "http://rdf.freebase.com/ns/type.property.unique" Explanation

The URI "http://rdf.freebase.com/ns/type.property.unique" in the original Freebase RDF dataset is simplified into "/type/property/unique" in our dataset. The identifier "/type/property/unique" in our dataset has URI http://rdf.freebase.com/ns/type.property.unique in the original Freebase RDF dataset.

This data collection contains test Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the WUGsite.

The data is provided for testing purposes and thus contains specific data cases, which are sometimes artificially created, sometimes picked from existing data sets. The data contains the following cases:

• afternoon_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 427 judgments. Has clear cluster structure with only one cluster, no graded change, no binary change, and medium agreement of 0.62 Krippendorff's alpha.
• arm: standard textbook example for semantic proximity (see reference below). Fully connected graph with six words uses, annotated by author.
• plane_nn: sampled from DWUG EN 2.0.1. 200 uses partly annotated by multiple annotators with 1152 judgments. Has clear cluster structure, high graded change, binary change, and high agreement of 0.82 Krippendorff's alpha.
• target: similar to arm, but with only two repeated sentences. Fully connected graph with six words uses, annotated by author. Same sentence (exactly same string) is annotated with 4, different string is annotated with 1.

Please find more information in the paper referenced below.

Version: 1.0.0, 05.05.2023.

Reference

Dominik Schlechtweg. 2023. Human and Computational Measurement of Lexical Semantic Change. PhD thesis. University of Stuttgart.

This resource contains the Knowledge Graph supported by the Python library grainsack (https://github.com/rbarile17/grainsack).

This proposed vocabulary allows edges in Property Graphs (e.g Neo4j, RDF*) to be augmented with edge properties that specify ontological semantics, including (but not limited) to OWL-DL interpretations. [from GitHub]

The semantic knowledge graph market is experiencing robust growth, driven by the increasing need for organizations to derive actionable insights from complex, unstructured data. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of big data necessitates efficient data management and knowledge extraction tools; semantic knowledge graphs excel in this arena by organizing information into easily understandable and interlinked structures. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the capabilities of semantic knowledge graphs, improving their ability to process and analyze ever-increasing volumes of data. Thirdly, the growing adoption of cloud-based solutions is simplifying deployment and accessibility, further driving market growth. Key players like Microsoft, Google, and Yandex are heavily investing in this technology, creating a competitive yet innovative landscape. However, challenges remain, including the complexity of implementing these systems, high initial investment costs, and the need for skilled professionals to manage and interpret the resulting knowledge graphs. Despite these restraints, the long-term prospects for the semantic knowledge graph market are incredibly positive. The increasing demand for improved data governance, enhanced business intelligence, and personalized customer experiences will continue to fuel adoption across various sectors, including finance, healthcare, and manufacturing. The market segmentation is expected to evolve, with increasing specialization in specific industry verticals and the development of more sophisticated analytics tools built on top of semantic knowledge graph technologies. The focus will likely shift towards the integration of semantic knowledge graphs with other emerging technologies such as blockchain and the Internet of Things (IoT) to unlock even greater value from data. This convergence will lead to the emergence of smarter and more autonomous systems capable of decision-making based on comprehensive, contextualized knowledge. Regions like North America and Europe are anticipated to maintain significant market shares, though Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and technological advancements.

Network of 44 papers and 59 citation links related to "Interpretation of correlations in clinical research".

Network of 44 papers and 82 citation links related to "Interpretation and inference with maximal referential terms".

This data collection contains diachronic Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the WUGsite.

See previous versions for additional testsets.

Please find more information on the provided data in the paper referenced below.

Version: 2.0.0, 15.12.2021. Important: extends previous versions with one more annotation round and new clusterings.

Reference

Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages.

HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems built based on Wikipedia.

To enhance the understanding of the graph patterns extracted by the interpretable model, a visual analysis system is designed to better understand the graph patterns and the model interpretation process. The visual system is explored from three levels: single user, user group and multiple user groups, which facilitates users to explore the recommended pattern of graph neural networks, thereby verifying the reliability of the explanation.

United States - Total Revenue for Translation and Interpretation Services, All Establishments, Employer Firms was 6560.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Total Revenue for Translation and Interpretation Services, All Establishments, Employer Firms reached a record high of 6560.00000 in January of 2022 and a record low of 696.00000 in January of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Revenue for Translation and Interpretation Services, All Establishments, Employer Firms - last updated from the United States Federal Reserve on August of 2025.

Network of 46 papers and 72 citation links related to "The Interpretation of Gastric Motility".

Vertex-transitive Graphs On Fewer Than 48 Vertices

This dataset contains all the vertex-transitive graphs on 10-47 vertices.

It consists of a collection of tar files, with names like

alltrans26.tar

meaning that this tar file contains all the vertex-transitive graphs on 26 vertices.

Once the tar file is unpacked (using "tar xf alltrans26.tar") this will create a number of smaller gzipped files with names such as

alltrans26_k=03.gz

meaning that this file contains all the transitive graphs on 26 vertices with degree (valency) 3.

Once the gzip file is unpacked using "gunzip alltrans26_k=03.gz" the resulting file contains all the graphs, one per line, in graph6 format (this format was invented by Brendan McKay and is recognised by SageMath).

The first five lines of the file alltrans26_k=03 are as follows:

Ys???C????_CA?@?`?_GO?c?@_?Q??K??O@CG?aA?GAG@?OCCG?GGC??
Ys??WO@?O??O?J?E?A_H??A?C??O?????_?DC?AQ?AAA??oG?C_O?I??
Ys??WWG@?@?A?W?c??g?S?@??G???G??O??S??I?_?Ac??SS??OW??_?
Ys?GGSG@?@?A?W?c??g?S?@??G???G??O??S??I?_?Ac??SS??OW??_?
Ys?GOO?????c?Q?c?B?@_?I?A??G??A?@?CCC?OP?CCC?C_O?AOC?C_?

These can be directly used as input to SageMath with commands such as

g = Graph("Ys???C????_CA?@?`?_GO?c?@_?Q??K??O@CG?aA?GAG@?OCCG?GGC??")

No attempt has been made to reduce data storage by removing redundancy. So the tar file for vertex-transitive graphs on n vertices contains files for each feasible valency from 0 to n-1, despite the redundancy inherent in storing both a graph and its complement, and in storing both disconnected and connected graphs.

Knowledge Graphs As A Service Market Outlook

The global market size for Knowledge Graphs As A Service (KGaaS) was estimated at USD 1.2 billion in 2023 and is projected to reach approximately USD 5.8 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 19.1% during the forecast period. This rapid growth can be attributed to the increasing need for advanced data management solutions and the adoption of artificial intelligence (AI) and machine learning (ML) technologies across various industries. Businesses are recognizing the value of knowledge graphs in transforming raw data into meaningful insights, which is driving market expansion.

One of the major growth factors fueling the KGaaS market is the exponential increase in data generation across industries. Organizations are inundated with vast amounts of structured and unstructured data, which necessitates sophisticated data management and analysis tools. Knowledge graphs offer a way to interconnect data points, making it easier to derive insights, identify trends, and make data-driven decisions. This capability is particularly beneficial in sectors like healthcare, finance, and e-commerce, where timely and accurate data analysis is crucial.

Another significant factor contributing to market growth is the rising adoption of AI and ML technologies. Knowledge graphs enhance these technologies by providing a structured framework to organize and interpret data. For example, in natural language processing (NLP) applications, knowledge graphs can improve the accuracy of language models by offering context and relationships between words. This is driving demand across various use cases, from chatbots and virtual assistants to complex predictive analytics and recommendation systems.

The integration of knowledge graphs into business processes is also being driven by the need for enhanced customer experience. Knowledge graphs enable companies to create a unified view of customer data, which can be used to personalize interactions and improve customer service. For instance, in the retail and e-commerce sector, knowledge graphs help in understanding purchase history, preferences, and behavior, allowing businesses to tailor their offerings and marketing strategies accordingly. This focus on customer-centricity is a key driver of the KGaaS market.

From a regional perspective, North America is expected to dominate the KGaaS market due to the early adoption of advanced technologies and the presence of major market players. However, significant growth is also anticipated in the Asia Pacific region, driven by increasing digital transformation initiatives and the growing importance of data analytics in emerging economies. Europe is also expected to see considerable growth, supported by stringent data governance regulations and robust technological infrastructure.

Component Analysis

In the KGaaS market, the component segmentation includes software and services. The software segment encompasses various tools and platforms that enable the creation, management, and utilization of knowledge graphs. These software solutions are essential for building the underlying structure of knowledge graphs, integrating data sources, and providing analytical capabilities. The increasing complexity of data and the need for real-time analytics are driving the demand for advanced software solutions in this space.

Within the software segment, there are specialized tools for different applications, such as data integration, data visualization, and semantic search. These tools help organizations in effectively managing their data and extracting valuable insights. The growing adoption of cloud-based solutions is also contributing to the demand for software, as it offers scalability, flexibility, and cost-efficiency. Companies are increasingly opting for cloud-based knowledge graph solutions to leverage these benefits and support their digital transformation journeys.

On the other hand, the services segment includes consulting, implementation, training, and support services. These services are crucial for organizations to successfully deploy and maintain their knowledge graph solutions. Consulting services help businesses understand the potential of knowledge graphs and develop strategies for their implementation. Implementation services ensure the seamless integration of knowledge graph solutions with existing systems and processes. Training services are essential for building the necessary skills within the organization, while support services provide ongoing assistance to address any technical issues or

Additional file 3. Raw results of Cα location comparison. Description: This table contains the raw results of the experiment comparing the locations of the Cα atoms with the nodes of the density graphs created from our dataset of experimental cryo-EM maps. Included in the results are the comparable metrics from the DeepTracer predictions.

A good statistical graph for a randomized experiment simultaneously conveys the study's design, analysis, and results. It reveals the experimental design by mapping design elements to aesthetic parameters. It illuminates the analysis by plotting the statistical model in data-space.'' When the design and analysis of an experiment are encoded in a plot, the interpretation of the experimental results is clarified.Analyze as you randomize'' is a dictum attributed to Fisher that guides interpretations of experimental data. This chapter extends that principle to visualizations of randomized experiments. While not every experiment requires a visualization, those that do should be visualized in ways that communicate the design and results together.

United States - Total Revenue for Translation and Interpretation Services, Establishments Subject to Federal Income Tax, Employer Firms was 6560.00000 Mil. of $ in January of 2022, according to the United States Federal Reserve. Historically, United States - Total Revenue for Translation and Interpretation Services, Establishments Subject to Federal Income Tax, Employer Firms reached a record high of 6560.00000 in January of 2022 and a record low of 696.00000 in January of 1998. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Total Revenue for Translation and Interpretation Services, Establishments Subject to Federal Income Tax, Employer Firms - last updated from the United States Federal Reserve on August of 2025.