The XML Business Reporting Language (XBRL) is a standard for business and financial information reporting. It is based on XML so instance documents based on XBRL, e.g. a quarterly report, are highly constrained by the XML document-oriented nature. This makes more difficult to perform queries that mix information from filings from different dates, companies, or accounting principles than with a formalism based on a graph model instead of a tree model. Semantic Web technologies provide a graph model that facilitates mashing-up different XBRL sources. We have put into practice this approach mapping the XBRL filings available from the SEC’s EDGAR program to Resource Description Framework (RDF) and the XML Schema taxonomies these filings are based on to Web Ontology Language (OWL). The resulting semantic metadata, though highly tied to the XML structure it is mapped from, benefits from Semantic Web technologies and tools in order to facilitate integration and cross-querying, even together with other parts of the Web of Linked Data.
Towards a Linked Open Data Cloud of Language Resources in the Legal DomainLynx Project
Towards a Linked Open Data Cloud of Language Resources in the Legal Domain - Law Via Internet
Patricia Martín-Chozas, Elena Montiel-Ponsoda,
Víctor Rodríguez-Doncel
10th October 2018
The XML Business Reporting Language (XBRL) is a standard for business and financial information reporting. It is based on XML so instance documents based on XBRL, e.g. a quarterly report, are highly constrained by the XML document-oriented nature. This makes more difficult to perform queries that mix information from filings from different dates, companies, or accounting principles than with a formalism based on a graph model instead of a tree model. Semantic Web technologies provide a graph model that facilitates mashing-up different XBRL sources. We have put into practice this approach mapping the XBRL filings available from the SEC’s EDGAR program to Resource Description Framework (RDF) and the XML Schema taxonomies these filings are based on to Web Ontology Language (OWL). The resulting semantic metadata, though highly tied to the XML structure it is mapped from, benefits from Semantic Web technologies and tools in order to facilitate integration and cross-querying, even together with other parts of the Web of Linked Data.
Towards a Linked Open Data Cloud of Language Resources in the Legal DomainLynx Project
Towards a Linked Open Data Cloud of Language Resources in the Legal Domain - Law Via Internet
Patricia Martín-Chozas, Elena Montiel-Ponsoda,
Víctor Rodríguez-Doncel
10th October 2018
Developing A Big Data Analytics Framework for Industry IntelligenceGene Moo Lee
Researchers often model industry as a network where each node corresponds to an organization and an edge represents an inter-organizational relationship (e.g., competition, acquisition, alliance). Structural holes are an important construct in identifying network opportunity structures. While there have been significant theoretical and empirical works around this concept, there has been limited fine-grained empirical research on the operationalization of the structural hole concept based on organizational self-identified strategic posturing. In this project, we propose an innovative method to quantify self-identified strategic posturing structural holes using a machine learning approach called doc2vec, which transforms textual documents into numeric vector representations. Specifically, we apply the doc2vec model to the collection of 10-K annual reports from U.S. public firms in the 1995-2016 period. To show the effectiveness of our measure, we conducted empirical analyses on firm birth (i.e., IPO) and firm mortality (i.e., delisting) using Compustat data. First, our firm birth analysis, using the generalized linear model, shows that new organizations have an increasing birth rate in structural holes between a pair of existing firms. Second, using the Cox proportional hazard model, we show that organizations entering into a structural hole have a significant decrease in mortality rates. This is the first large-scale empirical study to use self-identified strategic posturing structural holes in the analysis of industry dynamics, and as such provides an advance to both the industry dynamics and network literature.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?
This document brings together a set
of latest data points and publicly
available information relevant for
Agile & AI Operations Industry. We
are very excited to share this content
and believe that readers will benefit
from this periodic publication
immensely.
Wait, What? It’s Already Done? The State of Colorado’s Effective Migration to...Amazon Web Services
Join us as we discuss how the State of Colorado successfully migrated their Integrated Eligibility System (Colorado Benefits Management System (CBMS)) to AWS. Using Deloitte’s Cloud Migration Factory approach, enabled by ATADATATM, over 300 workloads were effectively migrated to AWS. Understand how we developed a FedRAMP compliant design, deployed with AWS CloudFormation, and successfully helped the State of Colorado migrate off Oracle Exadata and set a foundation for moving other business-critical applications to the cloud. We'll share insights and lessons learned on a number of topics including – why AWS, readiness assessment, migration workflow, migration of Oracle Exadata, licensing in cloud, software EULAs, security and compliance, and billing. Sponsored by Deloitte.
Open government international garry lloydGarry Lloyd
“Our vision is for an open government. For the government and community to be able to leverage a government platform with social media tools, developing a community instinct. This would then enable both government and community to have an inherent inclination toward the same behaviour / goal.”
Cloud computing, edge computing, is a kind of conflict, a convergence of views. Take a look at the technology trends of edge computing, also Let's take a look at what it's like to look at cloud computing
Data Analytics is ubiquitous. Some organisations like Netflix and Amazon are proficient in extracting significant Competitive Advantage from their while other like HP and IBM have extended this model to derive Corporate Advantage by aggregating the data layer across business units and portfolio companied. What if organisations across the sector combined their data to the elusive Sector Advantage?
How to Utilize Analytics to Better Understand Your Donors.pdfTechSoup
In this webinar, Amazon Web Services (AWS) environment experts shared how your organization can use analytics to better understand your donors using business intelligence tools such as Amazon Quicksight and how you can connect your donor data from your existing CRM into your Amazon Web Services (AWS) environment.
Welcome & Update on our Hong Kong Business
Abstract:
- Introduction to AWS Hong Kong
- Organizational Tenets / Market Differentiation
- Working Model / Considerations
- Forward Looking View / Opportunities
Speaker: Zane Moi, Head of Business Development, Hong Kong & Taiwan, AWS
Developing A Big Data Analytics Framework for Industry IntelligenceGene Moo Lee
Researchers often model industry as a network where each node corresponds to an organization and an edge represents an inter-organizational relationship (e.g., competition, acquisition, alliance). Structural holes are an important construct in identifying network opportunity structures. While there have been significant theoretical and empirical works around this concept, there has been limited fine-grained empirical research on the operationalization of the structural hole concept based on organizational self-identified strategic posturing. In this project, we propose an innovative method to quantify self-identified strategic posturing structural holes using a machine learning approach called doc2vec, which transforms textual documents into numeric vector representations. Specifically, we apply the doc2vec model to the collection of 10-K annual reports from U.S. public firms in the 1995-2016 period. To show the effectiveness of our measure, we conducted empirical analyses on firm birth (i.e., IPO) and firm mortality (i.e., delisting) using Compustat data. First, our firm birth analysis, using the generalized linear model, shows that new organizations have an increasing birth rate in structural holes between a pair of existing firms. Second, using the Cox proportional hazard model, we show that organizations entering into a structural hole have a significant decrease in mortality rates. This is the first large-scale empirical study to use self-identified strategic posturing structural holes in the analysis of industry dynamics, and as such provides an advance to both the industry dynamics and network literature.
How Graphs Continue to Revolutionize The Prevention of Financial Crime & Frau...Connected Data World
Financial crime prevention is something that affects everyone in one way or another. From the Deutsche Banks of the world to small and medium online merchants, regulations for anti-money laundering, know your customer, and customer due diligence apply.
Failing to comply with such regulations can bring on substantial fines. Even more importantly, it can hurt the bottom line and reputation of businesses, having far-reaching side effects. Complying with such regulations, and actively cracking down on financial crime, however, is not easy.
Cross-referencing interconnected data across various datasets, and trying to apply detection rules and to discover patterns in the data is complicated. It takes expertise, effort, and the right technology to be able to do this efficiently.
A natural and efficient way of looking for patterns and applying rules in troves of interconnected data is to model and view that data as a graph. By modeling data as a graph, and applying graph-based algorithms such as PageRank or Centrality, traversing paths, discovering connections and getting insights becomes possible.
Graphs and graph databases are the fastest-growing area of data management technology for a number of reasons. One of the reasons is because they are a perfect match for use cases involving interconnected data.
Queries that would be very complicated to express and very slow to execute using relational databases or other NoSQL database technology, are feasible using graph databases. With the rise in complexity of modern financial markets, financial crimes require going 4 to 11 levels deep into the account – payment graph: this requires a different solution than either relational or NoSQL databases.
How are organizations such as Alibaba, OpenCorporates, and Visa using graph database technology to not just stay on top of regulation, but be one step ahead in the race against financial crime?
Is it possible to do this in real time?
What do graph query languages have to do with this?
This document brings together a set
of latest data points and publicly
available information relevant for
Agile & AI Operations Industry. We
are very excited to share this content
and believe that readers will benefit
from this periodic publication
immensely.
Wait, What? It’s Already Done? The State of Colorado’s Effective Migration to...Amazon Web Services
Join us as we discuss how the State of Colorado successfully migrated their Integrated Eligibility System (Colorado Benefits Management System (CBMS)) to AWS. Using Deloitte’s Cloud Migration Factory approach, enabled by ATADATATM, over 300 workloads were effectively migrated to AWS. Understand how we developed a FedRAMP compliant design, deployed with AWS CloudFormation, and successfully helped the State of Colorado migrate off Oracle Exadata and set a foundation for moving other business-critical applications to the cloud. We'll share insights and lessons learned on a number of topics including – why AWS, readiness assessment, migration workflow, migration of Oracle Exadata, licensing in cloud, software EULAs, security and compliance, and billing. Sponsored by Deloitte.
Open government international garry lloydGarry Lloyd
“Our vision is for an open government. For the government and community to be able to leverage a government platform with social media tools, developing a community instinct. This would then enable both government and community to have an inherent inclination toward the same behaviour / goal.”
Cloud computing, edge computing, is a kind of conflict, a convergence of views. Take a look at the technology trends of edge computing, also Let's take a look at what it's like to look at cloud computing
Data Analytics is ubiquitous. Some organisations like Netflix and Amazon are proficient in extracting significant Competitive Advantage from their while other like HP and IBM have extended this model to derive Corporate Advantage by aggregating the data layer across business units and portfolio companied. What if organisations across the sector combined their data to the elusive Sector Advantage?
How to Utilize Analytics to Better Understand Your Donors.pdfTechSoup
In this webinar, Amazon Web Services (AWS) environment experts shared how your organization can use analytics to better understand your donors using business intelligence tools such as Amazon Quicksight and how you can connect your donor data from your existing CRM into your Amazon Web Services (AWS) environment.
Welcome & Update on our Hong Kong Business
Abstract:
- Introduction to AWS Hong Kong
- Organizational Tenets / Market Differentiation
- Working Model / Considerations
- Forward Looking View / Opportunities
Speaker: Zane Moi, Head of Business Development, Hong Kong & Taiwan, AWS
Slides (currently unannotated) to support the "Preparing for the Future: Technological Challenges and Beyond" workshop presented with Brian Kelly - http://ukwebfocus.com/events/ili-2015-preparing-for-the-future/
Note - slideshare seems to have messed up the conversion - some slides are (unintentionally) blank....
Implicitly or explicitly all competing businesses employ a strategy to select a mix
of marketing resources. Formulating such competitive strategies fundamentally
involves recognizing relationships between elements of the marketing mix (e.g.,
price and product quality), as well as assessing competitive and market conditions
(i.e., industry structure in the language of economics).
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
𝐓𝐉 𝐂𝐨𝐦𝐬 provides unlimited package services including such as Event organizing, Event planning, Event production, Manpower, PR marketing, Design 2D/3D, VIP protocols, Interpreter agency, etc.
Sports events - Golf competitions/billiards competitions/company sports events: dynamic and challenging
⭐ 𝐅𝐞𝐚𝐭𝐮𝐫𝐞𝐝 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬:
➢ 2024 BAEKHYUN [Lonsdaleite] IN HO CHI MINH
➢ SUPER JUNIOR-L.S.S. THE SHOW : Th3ee Guys in HO CHI MINH
➢FreenBecky 1st Fan Meeting in Vietnam
➢CHILDREN ART EXHIBITION 2024: BEYOND BARRIERS
➢ WOW K-Music Festival 2023
➢ Winner [CROSS] Tour in HCM
➢ Super Show 9 in HCM with Super Junior
➢ HCMC - Gyeongsangbuk-do Culture and Tourism Festival
➢ Korean Vietnam Partnership - Fair with LG
➢ Korean President visits Samsung Electronics R&D Center
➢ Vietnam Food Expo with Lotte Wellfood
"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
Enterprise Excellence is Inclusive Excellence.pdfKaiNexus
Enterprise excellence and inclusive excellence are closely linked, and real-world challenges have shown that both are essential to the success of any organization. To achieve enterprise excellence, organizations must focus on improving their operations and processes while creating an inclusive environment that engages everyone. In this interactive session, the facilitator will highlight commonly established business practices and how they limit our ability to engage everyone every day. More importantly, though, participants will likely gain increased awareness of what we can do differently to maximize enterprise excellence through deliberate inclusion.
What is Enterprise Excellence?
Enterprise Excellence is a holistic approach that's aimed at achieving world-class performance across all aspects of the organization.
What might I learn?
A way to engage all in creating Inclusive Excellence. Lessons from the US military and their parallels to the story of Harry Potter. How belt systems and CI teams can destroy inclusive practices. How leadership language invites people to the party. There are three things leaders can do to engage everyone every day: maximizing psychological safety to create environments where folks learn, contribute, and challenge the status quo.
Who might benefit? Anyone and everyone leading folks from the shop floor to top floor.
Dr. William Harvey is a seasoned Operations Leader with extensive experience in chemical processing, manufacturing, and operations management. At Michelman, he currently oversees multiple sites, leading teams in strategic planning and coaching/practicing continuous improvement. William is set to start his eighth year of teaching at the University of Cincinnati where he teaches marketing, finance, and management. William holds various certifications in change management, quality, leadership, operational excellence, team building, and DiSC, among others.
Memorandum Of Association Constitution of Company.pptseri bangash
www.seribangash.com
A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
https://seribangash.com/article-of-association-is-legal-doc-of-company/
Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
www.seribangash.com
Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
https://seribangash.com/promotors-is-person-conceived-formation-company/
Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
Personal Brand Statement:
As an Army veteran dedicated to lifelong learning, I bring a disciplined, strategic mindset to my pursuits. I am constantly expanding my knowledge to innovate and lead effectively. My journey is driven by a commitment to excellence, and to make a meaningful impact in the world.
The world of search engine optimization (SEO) is buzzing with discussions after Google confirmed that around 2,500 leaked internal documents related to its Search feature are indeed authentic. The revelation has sparked significant concerns within the SEO community. The leaked documents were initially reported by SEO experts Rand Fishkin and Mike King, igniting widespread analysis and discourse. For More Info:- https://news.arihantwebtech.com/search-disrupted-googles-leaked-documents-rock-the-seo-world/
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
Website – www.pmday.org
Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
27. Go back to the original, complete network
in workspace 1 and duplicate it again
This time find the projects that are
connected by common companies
28. So what have you learned?
- How to export selected columns from OpenRefine
- How to import CSV data into Gephi
- How to visualise a simple network in Gephi
- How to map a bipartitie network to show relations
between entities connected by a common element
-…
This tutorial describes how to use network analysis tools to visually explore the links between companies working on the same contract.
The example dataset we will use comes from the World Bank.Each row represents a contract. Inspecting the column names tells us what data we have available about each contract.Looking at the data, we can see how we could order the companies based on the value of the total contract amount; or we might order the contracts by time; or we might look to see which contracts were awarded in a particular project, or to a particular company in the event of the same company being awarded more than one contract.
We might also wish to look for patterns in the data that show us how the things described in one row might connect to things described in other rows.For example, can we organise the data somehow to see which companies are associated with which projects? Could a network style visualisation help us do this?
But if we were to draw a network, what sort of thing should we connect to what? And how would would know what to connect to each other?One way is to look at the data… at which point we might notice that some of entries within a column take on the same value. This means that we can “connect” the data that appears in different rows using these common elements…
So what columns have usefully repeating elements? The projects column certainly has repeating elements, so if we should be able to draw diagrams that show all the companies that connect to each project. And if a company is associated with more than one project, it should in a certain sense be seen to join those projects together…
A few of the contract numbers repeat, so it might be interesting to explore the extent to which companies connect to contracts. If two different companies are associated with the same contracts, that might be interesting.
Let’s get some data so we can start to explore the network…
We just need to do a little bit of tidying of the data before we make use of it.The major problem is that the Total Contract Amount column does not contain numbers, as such… In particular, we need to get rid of the dollar sign. Let’s create a new column into which we can put the cleaned values.
This little bit of code says: take the value of each cell in the original column and replace the $ symbol with nothing (that is, an empty string). In other words, delete the dollar sign… Put this value in the corresponding cell of the new column, and make the cell a number type.
Now we can export the data using the Custom Tabular Exporter, which allows us to select just those columns we want to export. (This can be very handy when a table has a large number of columns that we are not interested in!)I have rearranged the cells in the Custom Tabular Exporter simply by clicking on them and dragging them around. We just want three columns for now: Project ID, Supplier, and our new Amount column.Now that you know how to export the data just a few columns at a time, once you are comfortable with the process of visualising the data, you should be able to take other slices through the data (such as companies related to contracts) and visualise them yourself.You might also like to try using a similar method on a data set of your own…
There’s a final bit of tidying to do before we can use this data in Gephi, the application we’ll be using to visualise the network.In particular, Gephi expects the data to be presented to it with particular column names.Open the exported CSV data in a text editor and rename the columns: Source,Target,Weight (no spaces?)Note – you could have also renamed the columns in OpenRefine before exporting them…
We might also wish to look for patterns in the data that show us how the things described in one row might connect to things described in other rows.For example, can we organise the data somehow to see which companies are associated with which projects? Could a network style visualisation help us do this?
Network diagrams allow us to show relationships between different things. Networks are referred to in mathematical terms as graph structures, or graphs. You may be more familiar with thinking of things like line charts and bar charts as graphs, but when it comes to network, we use the term graph to describe the mathematical structure that defines the network.The circles – or nodes – represent “things” in the network, in this case, particular companies or projects.The lines – or edges – represent relationships between the things in the network. In this example, the edges represent contracts that associate a particular company with one or more projects, (or conversely, associate a project with one or more companies).Where nodes are placed in the diagram can be used to convey information about the structure of the network. Many different algorithms exist to lay out (that is, place, or position) the nodes at specific points in the diagram. Typically, we try to place nodes that are heavily interconnected by edges close to each other. Nodes that are grouped closely together on the page might then be assumed to be associated in some way because of the increasing number of links that connect them to each other.Note that we may use colour to represent that a node is a member of a particular group. In this case, we use colour to depict whether or not a node represents a company or a project.
Launch Gephi and from the File menu select New Project. Click on the Data Laboratory tab, and then Import Spreadsheet.Load in the file (with amended column names) as an Edges Table. The default settings should be fine…
Click on the Overview tab – you should see the network that connects Companies to Project IDs displayed there…But what does it mean? And can we tidy it up a little?!
I used the Yifan Hu layout to generate this view over the network.Yifan Hu is a good all round layout engine that works particularly well when the data is hierarchically structured.Another good general purpose layout algorithm is ForeceAtlas2.
Whilst we might get a feeling for the structure and shape of the dataset as a whole from the overall visualisation, we often want to inspect one or more of the nodes in detail.The quickest way of doing this is to look at the labels…You may also have noticed that the edge thickness is thicker for some lines than others. In this case, the line thicknesses are proportional to the contract value, which we set in the weight column. If a company is associated with more than a single contract on a particular project, the edge weight well be proportional to the overall (total) sum of values of all the contracts relating that company to that project.
As well as using space (or position) and colour to represent structural elements of the network, we can also use edge weight (that is the thickness, or width) of the lines connecting nodes to each other to represent some feature of the network.In this case, we might use edge weight to represent the value of contract that connects a company with a project, or the number of contracts that a company has on a particular project.When placing nodes, we might also use edge weight to contribute to the determination of how closely two connected nodes should be placed to each other. If you think of the edge thickness in terms of the size, thickness or strength of a mechanical spring, you might perhaps start to imagine how nodes connected by thick springs will be pulled closer to each other than nodes connected by much weaker springs.
As well as edge thickness, we might also make use of node size to highlight some feature of the network.In this example, we use node size to represent the degree of each node, that is, the number of edges connected to it. Sometimes, we might want to highlight nodes that have small numbers of connections, for example to identify projects with very few companies contracted to them. In this case, we might make nodes with only a single incoming edge very large, and nodes with large number of edges much smaller.The node size thus represents how well connected a node is. In this case, the size of the project nodes indicates how many companies are associated with it, and the size of the company nodes depicts how many project contracts the company is engaged with.Note that we can combine edge weight and node size, for example, by setting node size proportional to the summed weights of edges that are connected to the node.Hopefully, you are already starting to see how a network diagram can provide a range of powerful visual representations for helping us explore the structure of network and identify key elements of it.
We can size the nodes according to statistical values calculated over the network.In this case, we might want to highlight nodes according to the total value of contracts flowing into them (for companies) or out of them (for projects). The weighted average statistic calculates the corresponding value for each node in the network.The spline operator in the Ranking tab – where we set the node size – allows us to tweak the relationship between the value used to size the node and the node size. The default is a simple linear proportional map. However, we may find that the range of values we want to map are “clumped” together (for example, one very large value and a range of smaller values clumped together at the other end of the overall range). In such a case, we might want to tweak the mapping to provide a little more salience when it comes to distinguishing between the values that are otherwise clumped together.As well as making node size proportional to some quantity, we can also set the label size to be proportional to the node size.
There are several other tools available to us that allow us to explore other properties of the network. For example, there is a wide selection of filters that allow us to select particular filtered views of the network.In this case, we use the degree range filter to show only nodes that have degree of two or more. This filters out nodes that have degree 1 – for example, companies that are only associated with a single project. The result is a view over the network that shows which companies are associated with two or more projects, and which projects they are. The node sizes are indicative of the total overall vale of contracts associated with each particular node.So for example, we see that Siemens AG is associated with contracts from projects P072018 and P090104. The large node size suggests that the sum total of contracts Siemens AG has received via this projects is quite significant. In addition, the line from P072018 to Siemens AG suggests that the total value of contracts (or maybe just a single contract) Siemens AG has received from that project is quite large.
So far, out network diagram has shown us how companies relate to projects, and conversely, how projects relate to companies.But sometimes we may want to know rather more directly the extent to which two things are connected by virtue of having a common partner – for example, which companies worked on the same projects together, or which projects are linked by virtue of having used the same companies.When the data is represented as a graph, we can manipulate the graph in order to generate derived graphs that can capture these sorts of relationship directly.
When we have a dataset represented in the form of a network, we can start to analyse it by looking at additionalnetwork properties.For example, for the projects and companies graph, we might process the graph so as to remove project nodes and replace the edges with edges that connect companies that were on one or more project with each other. We might even use edge weight to depict how many projects there were in common between two companies.
From the workspace menu, duplicate the original network (remember to turn off all the filters! We want the whole network.)You will automatically be moved to a new workspace containing a copy of the original network. (Navigate between workspaces from the workspace selector at the bottom right hand corner of the whole application window.)In the Multimode Networks Projection panel, click on Graph Coloring to try to split the network into complementary types of node (companies and projects). Hopefully, the tool will return with the report that Bipartitie:true. That is, two complementary sets of nodes have been found (nodes in the first group are only ever connected to nodes in the second group.)Click on Load attributes and select the Node Color Multimode option.
To check what the multimode tool has called nodes of each type, click on the edit button in the palette toolbar, and click on a project node. An edit panel will appear – make a note of what colour the project type node has been labeled.We can now use the multimode network projection tool to process the network by joining together company nodes that are connected by a common project, and deleting the project nodes.That is, we want to connect blue company nodes to blue company nodes if they are connected by edges that pass through a common red project node. One we have made the mapping, we can delete the inner red project nodes.Running the projection results in several distinct clusters of companies that are connected to each other by virtue of being associated with the same project, as well as some companies that bridge different clusters by virtueof being associated with companies from different projects.
Conversely, we might remove the company nodes, and identify a new set of edges that connect projects that shared one or more common contracted companies. Again, edge thickness might be use to show how tightly connected two projects were by virtue of increasing numbers of common contracted companies.
By projecting the original network onto the network that shows links between projects that arise from common companies, we get a much clearer picture about how many projects there are, as well as possible linkages between them.
Here are some of the things you have hopefully learned…feel free to add anything else you might have learned to the list…
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