The document presents a study that aims to 1) analyze relationships between companies from news articles, 2) transform these relationships into intercompany networks to reveal metrics about companies, and 3) generate models integrating network features and financial metrics to predict company value over time. The study utilizes network analysis techniques, machine learning algorithms like regression, and statistical methods. It mines news articles and financial data to construct longitudinal social networks of companies and predict metrics like profit based on network features and a company's financial history. The models were shown to outperform alternatives that only use network data or only financial data.
Network relationship is positively correlated with enterprise development and performance, which is an important
basis for studying enterprise development and judging enterprise development situation. In this paper, complex
network theory and methods are used to study enterprise network relationships. Based on graph theory, a graph
model of enterprise network relationships is constructed, and the Laplacian matrix of the graph model is used to
analyze important indicators of network relationships, relationship strength, stability, reciprocity, centrality and other
indicators of numerical analysis methods.
Crowdsourcing the Policy Cycle - Collective Intelligence 2014 Araz Taeihagh ...Araz Taeihagh
Crowdsourcing is beginning to be used for policymaking. The “wisdom of crowds” [Surowiecki 2005], and crowdsourcing [Brabham 2008], are seen as new avenues that can shape all kinds of policy, from transportation policy [Nash 2009] to urban planning [Seltzer and Mahmoudi 2013], to climate policy. In general, many have high expectations for positive outcomes with crowdsourcing, and based on both anecdotal and empirical evidence, some of these expectations seem justified [Majchrzak and Malhotra 2013]. Yet, to our knowledge, research has yet to emerge that unpacks the different forms of crowdsourcing in light of each stage of the well-established policy cycle. This work addresses this research gap, and in doing so brings increased nuance to the application of crowdsourcing techniques for policymaking.
Network relationship is positively correlated with enterprise development and performance, which is an important
basis for studying enterprise development and judging enterprise development situation. In this paper, complex
network theory and methods are used to study enterprise network relationships. Based on graph theory, a graph
model of enterprise network relationships is constructed, and the Laplacian matrix of the graph model is used to
analyze important indicators of network relationships, relationship strength, stability, reciprocity, centrality and other
indicators of numerical analysis methods.
Crowdsourcing the Policy Cycle - Collective Intelligence 2014 Araz Taeihagh ...Araz Taeihagh
Crowdsourcing is beginning to be used for policymaking. The “wisdom of crowds” [Surowiecki 2005], and crowdsourcing [Brabham 2008], are seen as new avenues that can shape all kinds of policy, from transportation policy [Nash 2009] to urban planning [Seltzer and Mahmoudi 2013], to climate policy. In general, many have high expectations for positive outcomes with crowdsourcing, and based on both anecdotal and empirical evidence, some of these expectations seem justified [Majchrzak and Malhotra 2013]. Yet, to our knowledge, research has yet to emerge that unpacks the different forms of crowdsourcing in light of each stage of the well-established policy cycle. This work addresses this research gap, and in doing so brings increased nuance to the application of crowdsourcing techniques for policymaking.
Brief Presentation on paper: "Mining Dynamic Social Networks From Public News Articles For Company Value Prediction" (Jin et al, 2012) and others. (Presentation by M Paiva and M Courtney)
For more course tutorials visit
www.tutorialrank.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more classes visit
www.snaptutorial.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
For more classes visit
www.snaptutorial.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
Mark Zangari, CEO, Quantellia at MLconf SEA - 5/01/15MLconf
Agency Theory: The existence of massive data sets in many arenas is creating new challenges. Most people know about the issue of spurious correlations that do not represent true cause-and-effect. However a second challenge is more insidious and costly: this is the expense – which can run into the billions of dollars – of managing data that does not lead to actionable and valuable outcomes for an organization. For this reason, organizations that can identify the 20% of data that represents 80% of value realize a substantial advantage.
In this talk, I introduce Agency Theory, which is a mathematical framework for analyzing decision models to solve this problem. Agency theory borrows key ideas from machine learning, to solve a different purpose: rather than finding a set of parameters that best fits a data set, the objective is to find a set of decisions that leads to the most favorable set of outcomes, along with the data that is most valuable in supporting those decisions. Just as many foundational aspects of machine learning can be understood using information theory, I’ll describe how entropy and related concepts underlie Agency, and how to use this approach to prioritize data management and improve decision making.
INF 220 Effective Communication - tutorialrank.comBartholomew44
For more course tutorials visit
www.tutorialrank.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
Toyota focuses on quality as its competitive advantage.
INF 220 Possible Is Everything/newtonhelp.comlechenau71
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
Paper Explained: Deep learning framework for measuring the digital strategy o...Devansh16
Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It's a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
OL 325 Final Project Guidelines and Rubric Overvie.docxcherishwinsland
OL 325: Final Project Guidelines and Rubric
Overview
Acting as a recently hired compensation consultant, you will assist the burgeoning online music firm e-sonic to develop an internally consistent and market-
competitive compensation system that recognizes the achievements of individual contributors. The major portion of the project is divided into three milestones,
which will build upon the previous milestone. The milestones are submitted in Modules Three, Five, and Six. The final version of the entire project will be due at
the end of Module Seven.
Sample report outlines are included in the project text found in MyManagementLab. Each of the sections for this assignment will be submitted via Blackboard.
Outcomes
The project helps students to meet the following course outcomes:
• Students will gain an understanding of the evolution and administration of compensation and benefit programs for organizations
• Students will explore wage theory, principles and practices, unemployment security, worker income security, group insurance, disability insurance,
and pension plans and how these compensation and benefit items are balanced to provide incentive and recruitment of a high-performance
workforce
• The connection between the organization’s mission, objectives, policies, and the implementation and revision of their respective compensation and
benefit systems will be analyzed to gain a deeper understanding of the importance of such systems to the organization’s overall human resource
management
• At the conclusion of this course, students will be able to demonstrate the acquisition and application of theories and concepts that support the
enhancement and proficiency in 7 primary competencies: strategic approach, research, teamwork, communication, analytical skills, problem solving,
and legal and ethical practices
Preparation
1. Read the Building Strategic Compensation Project narrative linked in the course menu of the MyManagementLab home page. Note: Section 3:
Recognition of Individual Achievements WILL NOT be included in the course project.
2. Download the Comp Analysis Software Microsoft Excel file. Directions on accessing this file are located in the Module Resources section of Module One.
To run on a PC, the file requires Microsoft Excel 2007 or later. To run on a Mac, the version requires Excel 2011 or later.
o NOTE: Users of the CompAnalysis software must set the macros to a low level in order for the software to work. If the macros are set on too high of a
security level, then the software will be disabled and will not work properly. Navigate to the Tools menu, click Macros, and then click Security. Lower
the security level, save the spreadsheet, close, and re-open.
o Click on the External Market Survey feature, which will be used in Section 2 of the project, titled Market Competitiveness. Make your decisions first
by following the directions in the Building Strategic.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Brief Presentation on paper: "Mining Dynamic Social Networks From Public News Articles For Company Value Prediction" (Jin et al, 2012) and others. (Presentation by M Paiva and M Courtney)
For more course tutorials visit
www.tutorialrank.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more classes visit
www.snaptutorial.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
For more classes visit
www.snaptutorial.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
Mark Zangari, CEO, Quantellia at MLconf SEA - 5/01/15MLconf
Agency Theory: The existence of massive data sets in many arenas is creating new challenges. Most people know about the issue of spurious correlations that do not represent true cause-and-effect. However a second challenge is more insidious and costly: this is the expense – which can run into the billions of dollars – of managing data that does not lead to actionable and valuable outcomes for an organization. For this reason, organizations that can identify the 20% of data that represents 80% of value realize a substantial advantage.
In this talk, I introduce Agency Theory, which is a mathematical framework for analyzing decision models to solve this problem. Agency theory borrows key ideas from machine learning, to solve a different purpose: rather than finding a set of parameters that best fits a data set, the objective is to find a set of decisions that leads to the most favorable set of outcomes, along with the data that is most valuable in supporting those decisions. Just as many foundational aspects of machine learning can be understood using information theory, I’ll describe how entropy and related concepts underlie Agency, and how to use this approach to prioritize data management and improve decision making.
INF 220 Effective Communication - tutorialrank.comBartholomew44
For more course tutorials visit
www.tutorialrank.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
Toyota focuses on quality as its competitive advantage.
INF 220 Possible Is Everything/newtonhelp.comlechenau71
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
The “Sources of Competitive Advantage” image above shows the focus of competitive advantage for seven companies:
For more course tutorials visit
www.newtonhelp.com
INF 220 Week 2 Assignment Source of Competitive Advantage
This team assignment is meant to provide experience with real-world applicability in working in teams.
Paper Explained: Deep learning framework for measuring the digital strategy o...Devansh16
Companies today are racing to leverage the latest digital technologies, such as artificial intelligence, blockchain, and cloud computing. However, many companies report that their strategies did not achieve the anticipated business results. This study is the first to apply state of the art NLP models on unstructured data to understand the different clusters of digital strategy patterns that companies are Adopting. We achieve this by analyzing earnings calls from Fortune Global 500 companies between 2015 and 2019. We use Transformer based architecture for text classification which show a better understanding of the conversation context. We then investigate digital strategy patterns by applying clustering analysis. Our findings suggest that Fortune 500 companies use four distinct strategies which are product led, customer experience led, service led, and efficiency led. This work provides an empirical baseline for companies and researchers to enhance our understanding of the field.
Check out my other articles on Medium. : https://rb.gy/zn1aiu
My YouTube. It's a work in progress haha: https://rb.gy/88iwdd
Reach out to me on LinkedIn. Let's connect: https://rb.gy/m5ok2y
My Twitter: https://twitter.com/Machine01776819
My Substack: https://devanshacc.substack.com/
If you would like to work with me email me: devanshverma425@gmail.com
Live conversations at twitch here: https://rb.gy/zlhk9y
To get updates on my content- Instagram: https://rb.gy/gmvuy9
Get a free stock on Robinhood: https://join.robinhood.com/fnud75
OL 325 Final Project Guidelines and Rubric Overvie.docxcherishwinsland
OL 325: Final Project Guidelines and Rubric
Overview
Acting as a recently hired compensation consultant, you will assist the burgeoning online music firm e-sonic to develop an internally consistent and market-
competitive compensation system that recognizes the achievements of individual contributors. The major portion of the project is divided into three milestones,
which will build upon the previous milestone. The milestones are submitted in Modules Three, Five, and Six. The final version of the entire project will be due at
the end of Module Seven.
Sample report outlines are included in the project text found in MyManagementLab. Each of the sections for this assignment will be submitted via Blackboard.
Outcomes
The project helps students to meet the following course outcomes:
• Students will gain an understanding of the evolution and administration of compensation and benefit programs for organizations
• Students will explore wage theory, principles and practices, unemployment security, worker income security, group insurance, disability insurance,
and pension plans and how these compensation and benefit items are balanced to provide incentive and recruitment of a high-performance
workforce
• The connection between the organization’s mission, objectives, policies, and the implementation and revision of their respective compensation and
benefit systems will be analyzed to gain a deeper understanding of the importance of such systems to the organization’s overall human resource
management
• At the conclusion of this course, students will be able to demonstrate the acquisition and application of theories and concepts that support the
enhancement and proficiency in 7 primary competencies: strategic approach, research, teamwork, communication, analytical skills, problem solving,
and legal and ethical practices
Preparation
1. Read the Building Strategic Compensation Project narrative linked in the course menu of the MyManagementLab home page. Note: Section 3:
Recognition of Individual Achievements WILL NOT be included in the course project.
2. Download the Comp Analysis Software Microsoft Excel file. Directions on accessing this file are located in the Module Resources section of Module One.
To run on a PC, the file requires Microsoft Excel 2007 or later. To run on a Mac, the version requires Excel 2011 or later.
o NOTE: Users of the CompAnalysis software must set the macros to a low level in order for the software to work. If the macros are set on too high of a
security level, then the software will be disabled and will not work properly. Navigate to the Tools menu, click Macros, and then click Security. Lower
the security level, save the spreadsheet, close, and re-open.
o Click on the External Market Survey feature, which will be used in Section 2 of the project, titled Market Competitiveness. Make your decisions first
by following the directions in the Building Strategic.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Mining dynamic social networks from public news articles for company value prediction.
1. Mining dynamic social networks from
public news articles for company value
prediction.
- PRATIK, MICHEL, KAI & MINGHAO
2. Objectives and Key notes
What we discovered!
1. Study, analyze and understand impactful relations that exist between companies.
2. Transform the discovered relations into intercompany networks, revealing features
and metrics about the company.
3. Generate models that integrate network-feature metrics as well as company
financial valuations in order to substantially project or predict a company’s future
value OR profit over time e.g.
Metrics like Number of company's’ a company relates with (Network feature metric),
Company’s profit (financial metric).
3. Concepts and Techniques utilized.
Network Analysis
Graph theory
Ranking
Machine learning Algorithms
Regression (𝑦 = 𝑎 + 𝑏𝑥)
Statistical Methods
Correlation. (𝑅2
)
Mean Squared Error.
Algebraic equations
e.g the one that they used for the relation score
4. Choice of research domain
Document-level and sentence-level co-occurrence
The more companies co-appear or are described together in important news articles
and/or sentences, the stronger their mutual relationship.
NB: The study doesn’t extract specific relations separately but rather generalizes all
co-occurrence’s as impact relations, i.e., how many impacts a company receives from
others, by considering consider positive/negative structural impacts from networks.
5. Research Coverage
For a Target company
Generation of inter-company networks entailing Local and global relations, historical
relations and the delta change in impact of relations over time.
Borrowing the Page ranking algorithm ideology used in Information retrieval systems.
Companies are ranked by each network feature and company valuations.(e.g. Profit)
Usage of machine learning algorithm such as linear regression and SVM regression to
combine the features of the longitudinal network with a company’s financial
information to predict the company value.
6. Extracting Data
New York Times
Social Network Data
From the large scalable Public data about companies available in the news and
electronically through the web. (News Articles mainly. ). Data dated from 1981 – 2009
(year by year).
e.g. IBM appeared in about 300 news articles in the New York Times in 2009 (277 articles
as IBM and 84 articles as International Business Machines).
Interviews, Questionnaires and Observations.
Financial Data.
Company valuations were also obtained from New York Times Fortune 500 List (1955 -
2009) .
7. Pre-processing the data
For a Target company
For target company x, let candidate company be y (one that is impacting x in a period of
time t. Sets of documents D and sentences S in which they’ve co-occurred during time t
are collected.
Generating Longitudinal directed/undirected and valued/unvalued Networks over a
period of years for a set of companies 𝑉.
𝐺 𝑡 = {𝐺 𝑡1, 𝐺 𝑡2, 𝐺 𝑡3 … … … . } Where 𝑡1 < 𝑡2 < 𝑡3
For eachcompany
𝑥 ∈ 𝑉
a structural feature vector F 𝑥
𝑇
is generated F 𝑥
𝑇
⊆ G 𝑇
where F 𝑥
𝑇
indicates network
effects for target company x.
9. Calculating Impact relation Strength
Algorithm
𝑆𝑐𝑜𝑟𝑒 𝑥(𝑦) = a* 𝑖∈𝐷 𝑥.𝑦
𝑡 𝑤 𝑑 𝑖 + b ∗ 𝑗𝜖𝐷 𝑥.𝑦
𝑡 𝑤𝑠 𝑗
𝑤 𝑑 𝑖 And 𝑤𝑠 𝑖 - Weights computed for the total number of documents and
sentences in which target company 𝑥 and candidate company 𝑌co-occur.
𝑤 𝑑(𝑖) = log(1 +
1
𝑌′ 𝑖
+
𝑡𝑓𝑥(𝑖)
𝑦∈{𝑥,𝑌} 𝑡𝑓𝑦(𝑖)
)
𝑤𝑠(𝑖) = log(1 +
1
𝑌′′ 𝑖
)
e.g. IBM in 2009. It is apparent that Microsoft had the greatest impact on IBM in 2009. They co-occurred in 55
articles and were described together in 264 sentences. From these sentences, we can infer that they are direct
competitors.
Sometimes impact isn’t obvious, SPSS and IBM are not competitors and co-occurred in only 1 article and in 3
sentences, but their relation is important because SPSS and IBM co- appeared in an article in a high-weight
document (which describes only SPSS and IBM’s acquisition relation in the entire article).
10. Mining Longitudinal Network
Network effects
Six types of network effects are considered.
1. The number of connections that target company has.
2. Distance between x and its related nodes.
3. The number of connections that the companies relating with target company have.
4. Number of connections among x’s related nodes.
5. Distance between target company’s related nodes.
6. Number of node pairs having x on the shortest path.
11. Mining Longitudinal Network
1. Network effects generation
A set of nodes that directly or indirectly impact focal company x is generated - 𝑁𝑥
3 different types of node pairs are defined,
𝑥, 𝑖 ∀ (𝑖 ∈ 𝑁𝑥) then
𝑖, 𝑗 ∀ (𝑖, 𝑗 ∈ 𝑁𝑥, 𝑖 ≠ 𝑗) and
𝑖 𝑖, 𝑘 ∀ (𝑖 ∈ 𝑁𝑥, 𝑘 ∈ 𝑉).
Measures of degree connectivity𝛽(𝑖, 𝑗), Eccentricity 𝜇(𝑖, 𝑗), betweeness 𝜁 𝑥(𝑖, 𝑗), are
computed and then standardized to the network size 𝑉 .
12. Further analysis on the Networks
Traversing the valued directed network for more patterns revealing possible impact
relations.
1. Two new sub-networks are incorporated.
Neighboring node sets 𝐿 𝑥 which are considered to exert an impact on to x through their
direct connection to 𝑁𝑥.
NB: 𝐿 𝑥 ∶ 𝑁𝑥 - shows degree to which companies are directly related to x rather than
indirectly.
2. Retaining only arcs (directed edges) to reveal who is impacting who
3. Step 1(Network effects generation – (prev page)) is repeated to obtain historical
network effects.
13. Network Feature Selection
Filtering out companies with maximum Impact
Individual feature selection.
Companies are ranked by network features 𝑓𝑖 and by their valuations (profit).
𝑋𝑖 – Rank vector of companies ranked by network feature
Y – Companies ranked by their valuations like profit.
Spearman’s rank correlation is calculated between 𝑋𝑖 and Y.
The salient implication is that if there is an increase in the ratio of the number of
connections that a company has with the numbers of connections that its neighbors
have, then the value of its profits will increase.
14. Prediction Model
Network effects + Company valuations
Longitudinal network effects as well as valuations of each target company x are integrated into
Linear regression model (LRM) – Predicting a company’s current or future financial value.
Support vector regression model (SVR) – To learn Parameters.
Experimental results.
20 Fortune companies’ are selected as a sample. Their valuation records i.e. profits are captured and
networks are generated.
First, they calculate the mean profit value of the companies, then after train their model on the records for
records that span each five years networks, then after test it to predict the next five years profits then
they’re compared.
This is repeated for just a company.
15. Performance Evaluation
Prediction of the mean profits of 20 companies
Discovered
Network features do not seem to contribute
to revenue prediction but rather contribute
to predicting companies’ profit.
Company profit prediction by joint network
and financial analysis outperforms network-
only by 150% and financial-only by 34%.
17. Aspects of Network science in paper.
Graph-theory : such as degree of connectivity, diameter, shortest path used to calculate
network effects
Developing models to understand the network
Extracting data from NYT , Problem Statement part of Paper.
Building models to anticipate the evolution of the networks.
Network effects, company valuations
Constructing models to optimise the outcomes of networks
Experimental results and improvements.
18. What else can be done.
Improvements
1. A company's value (or performance) may encompass several factors depending on the
context in which it’s defined. Such as
Market performance, and Employee satisfaction and Responsibility. Analysis into these
aforementioned areas can potentially improve the model’s performance.
2. More social network data resources can be used. e.g.
social media especially Twitter. e.g. Twitter analysis or Facebook analysis to get the longitudinal
social network data.
3. Categorizing relations as negative or positive using sentiment analysis. Separately handling
networks i.e. positive impact relations networks handled on their own as well as negative
impact relations networks.
Editor's Notes
Precisely, The Paper aims to deal with the three bullets, in the order placed above.
Main point: Researchers aimed to develop a formula that would predict a company’s financial value over a period of time.
Techniques employed included: Network Analysis – (Graph theory), Statistical Methods – (Correlation), Machine learning Algorithms – (Regression), and Algebric equations – (e.g the one that they used for the relation score)
The concept initiated in this research was interesting, Not one that can easily be thought of.
It had the assumption that if companies co-appear in written records, then they’re most likely impacting each other in one way or another. Which definitely makes sense,
For instance, in football, Often you’ll get two giant clubs (that are rivals) mentioned alongside each other in documents, articles or anywhere. They impact each other by virtue of their rivalry, As one goes into the market to purchase top players, the other makes a similar move just so to stay on top.
However, Mutual relationship was something we didn’t agree with, because, the impact isn’t necessarily on a common understanding, but rather an automatic impact. So when the researcher claims mutual relationship
Extracting data about the relations on a local as well as global level and drawing back the years to capture historical relations between companies was smart and brilliant. Past and present statistics speak volumes about the future.
In order to filter out companies that made the most impact, an algorithm that ranks the relations between was useful.
Page-ranking – (Used to rank the importance of web pages by count of back links on the page).
Regression (Machine Learning algorithm) is a very reliable predictive analysis tool that was used o project outcomes or results after putting together all the necessary metrics as earlier talked about, Network feature metrics and financial metrics.
New York times was the source of the data used in the research.
Interviews, Questionnaires and Observations were a brilliant because researchers would then have more elaborated answers to their questions which would validate the data published by articles.
DISAGREE: A variety of data sets generated from different locations would be ideal, We didn’t agree to the fact that only one source was used.
Target company, was the focal company, So companies with whom it relates formed the network this company.
𝐺 𝑡 vector or set represents graphs for the specified times. 𝑥 𝑏𝑒𝑙𝑜𝑛𝑔𝑠 𝑉.
You can flip through slides and talk about the network effects to make this clear enough.
Relational score was an indicator of the strength of the relationship between companies. It can otherwise be understood as degree of connectivity – if implied through graph theory.
Candidate company: Company being investigated to discover how strong it’s relationship with the target company is.
RS was obtained by summing up the weights of the total number of documents and sentences in which the companies of interest co-appeared.
The weights were obtained using formula’s above. 𝑌 ′ (𝑖) and 𝑌 ′′ (𝑗) – counts of the company names that appear in document I and sentences j
𝑡 𝑓 𝑥 (𝑖) and 𝑡 𝑓 𝑦 (𝑖) frequency of company name y in the document I and sentence j.
A and b were constants that represented trade off’s between document weight and sentence weight.
i.e. The higher this metric, the more connected the involved companies were.
Degree of connectivity of target company.
Eccentricity of target company.
Degree of connectivity of candidate companies.
Vertex degree of the graph
Eccentricity of candidate companies (related nodes).
Betweeness centrality.
𝑥,𝑖 ∀ (𝑖∈ 𝑁 𝑥 ) – target company x and and a candidate company I for each company I In Node set 𝑁 𝑥
𝑖,𝑗 ∀ (𝑖,𝑗∈ 𝑁 𝑥 , 𝑖≠𝑗) candidate companies I and j both belong to Node set 𝑁 𝑥
and 𝑖 𝑖,𝑘 ∀ (𝑖∈ 𝑁 𝑥 , 𝑘∈𝑉).k belongs to the big Network of all nodes, so it can belong to any subgraph within the entire network
𝐿 𝑥 - set of nodes that are indirectly connected to x
𝐿 𝑥 - set of nodes that are directly connected to x
3. We agree and very usefull
Clearly there is an overlap in the research methodologies of these three areas:
They draw on data gathered from social networks, infrastructures, sen sors and the Internet of Things