Author Archives: Charles Nicholson

Probabilistic Prediction of Post-disaster Functionality

Probabilistic Prediction of Post-disaster Functionality Loss of Community Building Portfolios Considering Utility Disruptions

Journal of Structural Engineering

I am proud to announce that the latest collaborative work from the CORE lab has been accepted for publication in the ASCE’s Journal of Structural Engineering.  The new paper title is a mouthful, “Probabilistic Prediction of Post-disaster Functionality Loss of Community Building Portfolios Considering Utility Disruptions”, but the researchers (Weili Zhang, Peihui Lin, Naiyu Wang, Charles Nicholson, and Xianwu Xue) have been just calling the effort the “PPPD” project.

The study proposes a framework for the probabilistic prediction of building portfolio functionality loss in a community following an earthquake hazard. Building functionality is jointly affected by both the structural integrity of the building itself and the availability of critical utilities.

Post-disaster functionality loss relates to direct damage and critical utilities

To this end, the framework incorporates three analyses for a given earthquake scenario:

  1. evaluation of the spatial distribution of physical damages to both buildings and utility infrastructure
  2. computation of utility disruptions deriving from the cascading failures occurring in the interdependent utility networks; the cascading failures are simulated by use of new mixed-integer, multicommodity network flow optimization model
  3. by integrating (1) and (2), a probabilistic prediction of the post-event functionality loss of building portfolios at the community scale.
Framework for Post-disaster Functionality Loss Prediction

Overview of the PPPD Framework

The framework couples functionality analyses of physical systems of distinct topologies and hazard response characteristics in a consistent spatial scale, providing a rich array of information for community hazard mitigation and resilience planning.

Case Study

An implementation of the framework is illustrated using the residential building portfolio in Shelby County, TN, subjected to an earthquake hazard.  A single realization of an earthquake scenario in Shelby Country, TN is depicted below.

Single realization of post-disaster functionality

Single disruptive event simulation realization

Since the building damage, the flow model, the data collection/aggregation can all be complted efficiently, it is easy to extend the single simulation realization to many realizations.  This allows for a spatial probabilistic analysis of the vulnerabilities in the affected area. The figure below depicts the expected impact to the region based on 1,000 simulations of the scenario earthquake.

Multiple realizations of post-disaster functionality

Expected impact based on multiple earthquake simulation realizations

The intricacies that relate how the electric power network (EPN) support the potable water network (PWN), along with the particular individual component vulnerabilities of the EPN and PWN, produce probabilistic failure patterns in building functionality (see sub-figure d. above), that are not obvious!


Additionally, the framework allows us to compare a more traditional building portfolio analysis to with that of the practical implications of disruptive events.  That is, even if your place of employment is not damaged, if the building does not have power or water, then it will be closed for business anyway!

The green line in the figure to the right denotes the probability of exceedance for the ratio of buildings which cannot be occupied (RUO) due to physical damage.  The dotted line relates to the ratio of functional loss of buildings (RFL) which is due to any combination of direct damage and utility loss.   Clearly, the RUO is a conservative estimate compared to RFL.  For example, there is only a 40% chance that 40+% of the buildings will be directly damaged to the extent of restricted occupancy. However, that number jumps to 80% when the utilities are considered!


This work represents a wonderful collaborative effort within the CORE lab.  Weili Zhang developed the interdependency model and worked closely with Peihui Lin, who provided the building analyses.  And both worked closely with Xianwu Xue, the GIS expert.  And of course, I am always pleased to work with my colleague Naiyu Wang in Civil Engineering.   We have much, much collaborative work already in-progress and planned for the future!

Southwest Airlines Operations Tour

NOC at Southwest Airlines

Southwest Airlines Network Operations Control


Southwest Airlines Visit

(back row, left-to-right) Kyle Beatty, Warren Qualley, Kelvin Droegemeier, Hank Jenkins-Smith, Ed Cokely (front row, left-to-right) Carol Silva, Amy McGovern, Radhika Santhanam, Le Gruenwald, Sridhar Radhakrishnan, Charles Nicholson

I was happy to represent the Analytics Lab recently as a part of a larger team from OU who were invited down to Dallas, TX near Love Field to meet with Southwest Airlines (SWA) to learn more about the airline business and operations.   The attendees from OU included the Vice President of Research; directors from the School of Computer Science in the Gallogly College of Engineering and Management Information Systems in the Price College of Business; senior researchers and specialists from political science, psychology, computer science, and of course, data science.

We were privileged to take a tour of the famous Southwest Airlines Network Operations Control, a.k.a., the NOC.  This facility and the employees who work here are at the very core of the SWA network operations.   From dispatchers to air traffic control specialists to flight operations to maintenance to crew schedulers to weather analysts — this is where the major operational decisions are made.  

The unique look of the NOC, bathed in blue as it is, was designed scientifically to help with mood and to reduce eye strain.  And, well, it simply looks cool.

While we were at the NOC, it so happens that Southwest Airlines was actively engaged in planning for the expected impacts from the impending Hurricane Harvey.  Obviously, weather, and especially major weather events like hurricanes, play a huge role in flight delays and cancellations for all airlines. Such disruptive events can have impacts across across an entire transportation network. Analyzing and optimizing under this larger “system-wide” view is what ISE’s are famous for. These are hard problems, but they are worth solving!

Planning for Harvey at Southwest Airlines NOC

Southwest NOC in action planning for Hurricane Harvey


12th International Conference on Structural Safety & Reliability

Several members of the combined CORE lab at OU attended the 12th International Conference on Structural Safety & Reliability (ICOSSAR 2017) at the Technische Universität Wien in Vienna, Austria during the summer.

The CORE lab had a combined 5 presentations during the conference!


On Tuesday, August 8, Naiyu Wang and Xianwu Xue both gave presentations relating to resilience and climate change:

  • 2:30-2:50p Dresback, K., Xue X., Xu J., Wang, N., Kolar, R., Geoghehan K.  “STORM-CoRe: A coupled model system for hurricanes, storm surge and coastal flooding to support community resilience planning under climate change”
  • 5:20-5:40p Xue X., Wang, N., Ellingwood, B., Zhang, K.  “The impact of climate change on riverine flooding at the community scale”.

On Wednesday, August 9, Yingjun Wang and Charles Nicholson gave presentations in the general resilience section; and Peihui Lin gave a presentation in the section relating to urban resilience:

  • 10:50-11:10 Wang, Y., Wang, N. “Retrofitting building portfolios to achieve community resilience goals under tornado hazard”
  • 11:10 -11:30 Zhang, W., Wang, N., Nicholson, C., Hadikhan Tehrani, M. “Stage-wise resilience planning for transportation networks”
  • 4:40-5:00 Lin, P., Wang, N. “A simulation-based model for post-disaster functionality recovery f community building portfolios”

OU was well represented in Vienna: in addition to the five faculty/students from the CORE lab listed above who traveled to ICOSSAR, there were separate presentations from Kash Barker and Hiba Baroud (PhD from OU, now faculty at Vanderbilt).

Several colleagues from the NIST-funded Center of Excellence on Community Resilience also participated in the conference including Bruce Ellingwood (CSU), John van de Lindt (CSU), Paolo Gardoni (UIUC), and Jamie Padgett (Rice), among others.

For fun

Outside of the conference itself, Vienna was a beautiful and interesting place — museums, history, and incredible architecture.  I was very happy to enjoy the trip t Vienna with my father.  We enjoyed Stephansplatz, a main square at the center of Vienna, named after its Vienna’s amazing cathedral (two pictures below).   Also had a chance to visit the Schönbrunn Palace and gardens.  Finally, I also made a side-trip to work out with Cross Fit Vienna, “The Dungeon”!



Sr. Data Analyst Position – Open in Plano, TX

JCPenney is hiring Sr. Data Analyst

A friend of mine who works at the JC Penney HQ in Plano, TX just sent me a new job posting — she would love to hire an OU DSA student!  See below for job description and let me know if you are interested!   Please note that JCPenney is not doing Visa sponsoring for this position.

Job posting

JCPenney is one of the nations largest apparel and home furnishing retailers with more than 1,000 stores and We are a diverse community of people, all working together to bring sensational style, sensible prices and the best service possible to our customers. Were looking for talented individuals who want to work in an energetic, respectful, collaborative environment. With a wide array of jobs, internships, training and more, there are countless opportunities for you to grow your career with us.

JCPenney is looking for an experienced data analyst who is eager to learn, to add value, and to do interesting work as a valued member of the Customer Strategy team. This position is data-intensive and will involve use of SQL and SAS software tools to pull data for analysis and reporting purposes. Insights produced by this team inform business decisions in Marketing and beyond, including those by senior executive leaders.

Primary Responsibilities:

  • Facilitate the definition of analysis needs and work product requirements of internal clients
  • Translate client needs and requirements into specific data, logic and reporting requirements and realistic work plans
  • Understand and have a working knowledge of customer/transactional level data
  • Strive to structure analysis to provide conclusive insights that directly align to decision-making
  • Prioritize and balance multiple activities in parallel and communicate status proactively to manage stakeholder expectations
  • Understand data sources to determine the correct source(s) and logic to ensure accurate, efficient and timely deliverables
  • Build, run and automate data queries, analysis and reports
  • Speak out when business strategies do not align with data insights and when insights suggest new marketing tactics
  • Identify and log data issues and work with department, IT and vendor teammates to understand and resolve them
  • Proactively seek help from and offer help to JCP teammates to accelerate skill development, business understanding and overall goal achievement
  • Anticipate future insight needs/opportunities and deliver self-initiated value to JCP

Core Competencies & Accomplishments:

  • College graduate with 3+ years of experience
  • At least 2 years experience using databases and SQL (structured query language) and SAS
  • Ability to combine, cleanse and harmonize data for descriptive and predictive analytics
  • Strong math, computer and problem-solving skills, including MS Excel
  • Structured thinker and high attention to detail
  • Strong teamwork, communication and interpersonal skills
  • Desire to consistently meet and exceed stakeholder expectations
  • Desire to acquire new technical skills (e.g., R, Hive, Tableau, Datameer) and business knowledge

Welcome to Fall 2017!

Farewell to Summer

I hope everyone had a great summer and are enjoying the beginning of the Fall 2017 classes begin anew.  I’ve been here most of the summer, and wow! it is great to have the students back — the peace and quiet are nice for a while, but the campus really comes alive in Fall.

My summer included a trip to Disney with the family, a solo climb of two 14,000+ foot mountains in Colorado (Blanca Peak and Ellingwood Point), and trip to Austria for the ICOSSAR 2017 conference.

Welcome to Fall 2017 courses

My classes begin on Tuesday, 8/22 — both DSA/ISE 5103 and ISE 4113.

The DSA/ISE 5103 Intelligent Data Analytics graduate course is one which I think is core to data science.  In it we will  study and practice how to deal with real-world data intensive problems.   The topics include lots of data work and some great modeling techniques/applications such as dimension reduction, facial recognition, linear and logistic regression, LASSO, elasticnet, support vector machines, MARS, decision trees, random forests, boosted trees, neural networks, and clustering.  You will use powerful open source statistical programming language (R) and work on hands-on, applied data analysis projects.  No previous R experienced is required.  That said, I will expect you to work hard to learn the tool!  This course is being offered both online and on-campus.

In the ISE 4113 undergraduate course, we will be delving into the nitty-gritty of MS Excel to build spreadsheet-based decision support systems.  Excel is essentially ubiquitous in industry and mastery of it is critical!  We will go way beyond simple formulas and the basic usage of the tool and delve into optimization modeling, simulation, and even Visual Basic for Applications (VBA) programming.  This class is a big class, but fortunately, we have an excellent TA supporting the class.

DSA club?

One piece of great news is that it looks like there is some interest in starting a Data Science and Analytics club.  I will have more news about this later this semester, but if you are interested in joining such a club, please feel free to email me!  More info to follow!

I  look forward to meeting and getting to know you all this semester!

Charles Nicholson


Congratulations to three new Masters!

Congratulations Alexandra, Emily, and Megan — new Masters of Science!

Profile Picture
Snelling Megan

Alexandra Amidon (left), Emily Grimes (center), and Megan Snelling (right) have all successfully defended their Master’s theses this Spring 2017 are the three newest Masters from the Analytics Lab @ OU.

Alexandra and Emily completed their Masters of Science in Data Science and Analytics from the Gallogly College of Engineering.  The DSA program is a joint effort between the School of Industrial & Systems Engineering and the School of Computer Science. Megan completed her Masters of Science from the School of Industrial & Systems Engineering.

I’ll start with Megan since she is the lone ISE in this group of three.

Megan’s work is entitled “MODEL FOR MITIGATING ECONOMIC AND SOCIAL DISASTER DAMAGE THROUGH STRUCTURAL REINFORCEMENT” and is a continuation of previous work completed as a part of the NIST-funded Center of Excellence on Risk-Based Community Resilience Planning  and CORE Lab @ OU.

Abstract: Natural disasters have both severe negative short-term consequences on community structures, inhabitants, and long-term impacts on economic growth. In response to the rising costs and magnitude of such disasters to communities, a characteristic of modern community development is the aspiration towards resilience. An effective and well-studied mitigation measure, structural interventions reduce the value lost in buildings in earthquake scenarios. Both structural loss and socioeconomic characteristics are indicators for whether a household will dislocate from their residence. Therefore, this social vulnerability can be mitigated by structural interventions and should be minimized as it is also indicator of indirect economic loss. This research presents a model for mitigating direct economic loss and population dislocation through decisions regarding the selection of community structures to retrofit to higher code levels. In particular, the model allows for detailed analysis of the tradeoffs between budget, direct economic loss, population dislocation, and the disparity of dislocation across socioeconomic classes given a heterogeneous residential and commercial structure set. The mathematical model is informed by extensive earthquake simulation and as well as recent dislocation modeling from the field of social science. The non-dominated sorting genetic algorithm II (NSGA-II) is adapted to solve to model, as the dislocation model component is non-linear. Use of the mitigation model is demonstrated through a case study using Centerville, a test bed community designed by a multidisciplinary team of experts.  Details of the retrofit strategies are interpreted from the estimated Pareto front.

We should also offer congratulations to Megan on another account she is getting married soon and plans to spend her Summer hiking through Europe!

Alexandra and Emily both worked on project related to T.U.G. (The Untitled Game) which was partially funded by Nerd Kingdom.Nerd Kingdom


Abstract: Predictive algorithms applied to streaming data sources are often trained sequentially by updating the model weights after each new data point arrives. When disruptions or changes in the data generating process occur (“concept drifts”), the online learning process allows the algorithm to slowly learn the changes; however, there may be a period of time after concept drift during which the predictive algorithm underperforms. This thesis introduces a method that makes online neural network classifiers more resilient to these concept drifts by utilizing data about concept drift to update neural network parameters.

Alexandra has accepted a position with MSCI, a leading provider of investment decision support tools worldwide, as a Reference Data Production Analyst.  She will be using her skills in machine learning to continue developing new tools for anomaly detection.


Abstract: Player engagement is a concept that is both vital to the online gaming industry and difficult to define. Typically, engagement is defined using social science methodologies such as observing, surveying, and interviewing players. With the vast amount of data being collected from video games as well as user bases increasing in size, it is worthwhile to investigate whether or not user engagement can be defined and interpolated from data alone. This study develops a methodology for defining engagement using analytic methods in order to approach the question of whether gathering (as a proxy for social interaction) in sandbox games has an effect on player engagement.

Emily is following up on leads for a full-time position now, but in the meantime she has a road trip planned to the Grand Canyon, Sequoia National Park, and the Big Sur in California.  She is also in discussions with KGOU and NPR about starting a new radio program!

Congratulations to all three excellent students!  We wish you great success!

Emily Grimes, MS DSA, May 2017

Megan Snelling, MS ISE, May 2017

OU Industrial & Systems Engineering and Data Science & Analytics

Public Webinar Announcement: Center for Risk-Based Community Resilience Planning

Public Webinar Announcement — Community Resilience: Modeling, Field Studies and Implementation

Learn more about NIST-funded Center for Risk-Based Community Resilience Planning and how the Center is developing a computational environment to help define the attributes that make communities resilient.

WEBINAR: Thursday, April 27, 10:00 a.m. – 12:00 p.m. (CDT)  

The webinar is open to anyone immediately followed by a Q&A “chat” period.

A Resilient Community is one that is prepared for and can adapt to changing conditions and can withstand and recover rapidly from disruptions to its physical and social infrastructure.  Modeling community resilience comprehensively requires a concerted effort by experts in engineering social sciences and information sciences to explain how physical, economic and social infrastructure systems within a real community interact and affect recover efforts.

Join this information WEBINAR to learn more about the Center’s recent activities.

A Center overview will be followed by a session on the Center’s recent Special Issue of Resilient and Sustainable Infrastructure, which features six papers on the virtual community Centerville.  The modeling and analysis theory behind each paper will be explained followed by a demonstration of IN-CORE, the Interdependent Connected Modeling Environment for Community Resilience.  Presentations on the first validation study, the Joplin Hindcast, and the Center’s First Field Study, the 2016 Lumberton floods in NC will also be a highlight of the Webinar.

No registration is required this time, just click, watch, and chat.

Both Dr. Nicholson and Dr. Wang will be giving presentations during the webinar.

Flier for distribution: Webinar Flier 27-April-2017

Postdoctoral Research Fellow Position in Community Resilience

Prof. Charles Nicholson is currently accepting applications for a postdoctoral research fellow position in Community Resilience within the School of Industrial and Systems Engineering at the University of Oklahoma.

The primary area of research is with respect to the following broad objective:

Enhance community resilience to natural and man-made disasters through modeling, optimization, and risk-informed decision making with respect to vital, large-scale, interdependent civil infrastructure and socio-economic systems.

Researchers with backgrounds and interests in one or more the following areas are encouraged to apply:

  • Optimization: network flow optimization, multi-objective optimization, stochastic optimization; stochastic programming
  • Data science and analytics: including machine learning for predictive and classification modeling as well as unsupervised and semi-supervised learning
  • Decision modeling for community and regional resilience planning

The postdoctoral research fellow will embark on an exciting and innovative research program within a well-established and active multidisciplinary research group with collaboration opportunities across the United States.  In this role, you will also supervise one or more PhD students.  Experience with tools such as Python or R is highly preferred.  Familiarity with Civil Infrastructure systems and/or economic modeling is a plus.  The position will be supported by funded research projects with multi-year durations.

Interested applicants please send a one-page statement of research interests and CV to cnicholson @ OU (dot) edu.

Total Chaos: Soccer, ISE, and Old People


Total Chaos

In Fall 2017  I decided that it was time to start working on my bucket-list, item #117: play an actual game of soccer.   There are other items on my bucket-list too, but I figured I better try this one soon since I am not getting any younger.  This is the impetus for my new team: Total Chaos.

I’ve coached soccer for 3 years (my daughter’s team) for Norman Youth Soccer Association (NYSA) . When I started then, I had very little understanding of the game.  I knew that most of the players were not supposed to use their hands, but any rules other than that were  a bit vague…

Anyway, I’ve wanted to play soccer for years, but starting out as a complete newbie with such a demanding and skilled sport like futbol over the age 40, well, it was somewhat of daunting thing to do.  The options were: (1)  try to join an existing team and then ultimately disappoint all of the other players with my complete lack of skill or… (2) start my own team from scratch with the understanding that (a) everyone is welcome — even newbies and old people — and  (b) we will not likely win.  That is, set expectations low: so low in fact that no one has a right to be disappointed with any outcome!   I opted for the latter.  NYSA has an adult league, and thus I started recruiting for my new team…

To make a long story short, the response to my invitation “do you want to play soccer in a league even if we have no chance of winning any games?” — was a resounding yes.  Soccer mom’s and dad’s, friends, OU faculty, and both grad and undergrad students in ISE for some reason found the idea appealing.  My wife, who like me, has never played the sport in her life even joined up.  Thankfully, not everyone that answered the call was a complete newbie, because several of us needed teachers!

The student becomes the teacher…

In this case, literally “the students become the teachers” — Jack, Austin, Leslie, and Brad are all undergrads who took my ISE 4113 course in Fall 2017 and now they had their work cut out for them trying to teach me what to do on the field. Joining them we also have Darin, Nicole, Andrew, and Yasser — all PhD or MS students in either ISE or DSA.

Now, while our defense is not this bad:

without Jack Appleyard leading the defense, it could be much worse (I’m on defense you see — which does not give Jack much to work with!) so he is almost a one man team in the backfield — saving our collective butts more than once keeping it from being the total chaos it would’ve been otherwise!

Brad “the slide tackle ninja” Osborn, Austin Shaw, “the king of awesome”, and Leslie “the beast” Barnes head-up the midfield and offense and simply rock the pitch…

Darin Chambers — who happens to also be a political candidate running for State Representative District 46 — is a fellow soccer dad and great teammate and leader.  Yasser, a PhD student in ISE has both published research with me and taught me how to defend and pass.  Nicole and Andrew, both new to the game, are simply fearless.  Pravin, who is going up for tenure at OU the same time as me, has stepped up to help play keeper after our first keeper was injured.  Finally, Everton, Omar, Nery, Marco, Justin, Alicia, and Greg — are all new friends.

In summary — we have a great team: a great mix of ages, genders, languages, skills, and backgrounds.  Thanks for helping me mark off an item on my bucket-list I’ve dreamed to do for years.    The team pic below is missing a few players, so I’ll update it later, but here we are: Total Chaos.

Finally, despite my hand-balls and/or fouls in the box and/or missed passes and/or bad throw-ins (sorry about all that…) — so far we’ve played two games and won both.

Total Chaos team picture

Left to Right — Back: Everton, Omar, Marco, Justin, Jack, Brad, Austin, Pravin, Greg, Charles, Andrew, Nicole; Front: Yasser, Alicia, Zorelly


Two new resilience publications 2017!

Two new resilience publications!

Well, here at the Analytics Lab @ OU  2017 started off nicely with two new articles published in the area of community resilience. We are also very excited about finally being able to share the virtual community we created named “Centerville” as a part of the Center for Risk-Based Community Resilience Planning — the special issue on Centerville is finally published in Sustainable and Resilient Infrastructure.  Please check out the post on Centerville!

The first of these resilience publications is entitled Resilience-based post-disaster recovery strategies for road-bridge networks which appears in Structure and Infrastructure Structure and Infrastructure EngineeringEngineering, an international journal which aims to present research and developments on the most advanced technologies for analyzing, predicting and optimizing infrastructure performance.

This paper by Weili Zhang, Naiyu Wang, and myself presents a novel resilience-based framework to optimise the scheduling of the post-disaster recovery actions for road-bridge transportation networks.  This work was supported, in part, by the Center for Risk-Based Community Resilience Planning, National Institute of Standards and Technology (NIST) [Federal Award No. 70NANB15H044].

The methodology systematically incorporates network topology, redundancy, traffic flow, damage level and available resources into the stochastic processes of network post-hazard recovery strategy optimisation. Two metrics are proposed for measuring rapidity and efficiency of the network recovery: total recovery time (TRT) and the skew of the recovery trajectory (SRT).  The SRT is a novel metric designed to capture the characteristics of the recovery trajectory which relate to the efficiency of the restoration strategies.  This is depicted in the figure below.

resilience publication

Depiction of new skew metric for network recovery

Based on this two-dimensional metric, a restoration scheduling method is proposed for optimal post-disaster recovery planning for bridge-road transportation networks. To illustrate the proposed methodology, a genetic algorithm is used to solve the restoration schedule optimisation problem for a hypothetical bridge network with 30 nodes and 37 bridges subjected to a scenario seismic event. A sensitivity study using this network illustrates the impact of the resourcefulness of a community and its time-dependent commitment of resources on the network recovery time and trajectory.

  • Zhang, W., N. Wang, C. Nicholson. 2017. Resilience-based post-disaster recovery strategies for road-bridge networks.  Structure and Infrastructure Engineering, Accepted.  LINK

The next of the resilience publications, is a paper appearing in Reliability Engineering & System Safety entitled A multi-criteria decision analysis approach for importance ranking of network components.  This a joint effort between Yasser Almoghathawi, Kash Barker, Claudio Rocco.Reliability Engineering and System Safety

Reliability Engineering and System Safety is an international journal devoted to the development and application of methods for the enhancement of the safety and reliability of complex technological systems. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to achieve a balance between academic material and practical applications.

In the study, we propose a new approach to identify the most important network components based on multiple importance measures using a multi criteria decision making method, namely the technique for order performance by similarity to ideal solution (TOPSIS), able to take into account the preferences of decision-makers. We consider multiple edge-specific flow-based importance measures provided as the multiple criteria of a network where the alternatives are the edges.

resilience publication in RESS

Component Importance Measures may rank elements within a newtwork differently. TOPSIS provides one approach to considered such cases.

Accordingly, TOPSIS is used to rank the edges of the network based on their importance considering multiple different importance measures. The proposed approach is illustrated through different networks with different densities along with the effects of weights.

  • Almoghathawi, Y., K. Barker, C.M. Rocco, and C. Nicholson. 2017. A multi-criteria decision analysis approach for importance ranking of network components. Reliability Engineering and System Safety, 158: 142-151 LINK [bibTex]