Emerging Europe and Central Asia University Rankings

Here is the new QS World University Rankings (thanks for Gyuri Bazsa for writing me).

if you click to Methodology, you see again the magic numbers  and categories.

Academic reputation (30%)

Employer reputation (20%)

Faculty/student ratio (15%)

Papers per faculty (10%)

Web impact (10%)

Staff with a PhD (5%)

Citations per paper (5%)

International faculty (2.5%) and international students (2.5%).

Lomonosov is still the first. Written on ~ November 7th, 2017.

A new model for efficient ranking in networks

Caterina De BaccoDaniel B. LarremoreCristopher Moore  published a new algorithm with the title A physical model for efficient ranking in networks.  The model is based on binary interactions among the entities. As often in  physical models, interactions via edges are considered as mechanical springs, and the optimal rankings of the nodes are minimizes the total energy (or “energy”) of the system. They show some examples for identifying  prestige, dominance, and social hierarchies in human and animal communities.

Further studies will tell how efficient is the new algorithm.

 

 

Rank reversal

Algorithms are maybe objective. A famous example now known how PageRank gives different results by changing the numerical value of what is cold the ”damping factor”. PageRank is based on an assumption how a web-surfer behaves. For a while the surfer will click to links she is seeing in a certain page, but get bored with the actual page she visits, and then jump to another page randomly (as with directly typing in a new URL rather than following a link on the current page). The original algorithm assumed that the probability of being bored is 0.15, so the numerical value of the damping factor was set as 1-0.15=0.85. So, setting the damping factor for other numbers we may get different ranking. The phenomenon is called rank reversal. Rank reversal is a change in the rank ordering depending on some not important, or many times irrelevant factors. While I find the paper of Seung-Woo Son, Claire Christensen, Peter Grassberger, Maya Paczuski PageRank and rank-reversal dependence on the damping factor  excellent, my opinion does not count much, during almost six years its citation number is just six.

On a somewhat different note it is reasonable to expect that the ranking of any two candidates, A and B, should be preserved even if one more candidate C enters the race. In the theory of election systems it is called the ”rank reversal rule”. This rule was infamously violated in the US election in 2000, when Ralph Nader captured a few per cent of the vote in Florida, giving the election to George W. Bush (over Al Gore). As all we know Gore would have won if Nader was not in the race.

A plan to rate and rank citizens and legal persons

(Thanks to János Tóth).

Big data meets Big Brother as China moves to rate its citizens

http://www.wired.co.uk/article/chinese-government-social-credit-score-privacy-invasion

,,,Imagine a world where many of your daily activities were constantly monitored and evaluated: what you buy at the shops and online; where you are at any given time; who your friends are and how you interact with them; how many hours you spend watching content or playing video games; and what bills and taxes you pay (or not). It’s not hard to picture, because most of that already happens, thanks to all those data-collecting behemoths like Google, Facebook and Instagram or health-tracking apps such as Fitbit. But now imagine a system where all these behaviours are rated as either positive or negative and distilled into a single number, according to rules set by the government. That would create your Citizen Score and it would tell everyone whether or not you were trustworthy. Plus, your rating would be publicly ranked against that of the entire population and used to determine your eligibility for a mortgage or a job, where your children can go to school – or even just your chances of getting a date….:

Read the whole article! Comments are welcome!

Struggle for reputation

We cannot have thousand friends. Not even thousand closer acquaintances. The British anthropologist Robin Dunbar estimated the number with whom we can form stable social relationship. It is 150, which more precisely means that between hundred and two hundred. When I opened my this website and run along my INBOX to decide whom I ask easily ”to follow” me … I was shocked.. their number was 149 (well, fifty of them kindly pushed the button. They are the people who know some of my characteristic features and my actions, so my reputation is based on their perception of my activities. But in a broader sense my reputation is the collective opinion of everybody else, except myself (well, too bad :-)). As it is known, to build reputation needs time. We all know that a single moment is sufficient to destroy a good reputation. Unfortunately, even malignant gossips are sufficient to smash this reputation. Well, but having a good reputation among friends might help, as they may defend you even without your knowing.

In the internet age we have digital reputation. Some of the reputation is expressed by numbers, and the whole book is about discussing the reality, illusion of of objectivity. One of my peer ((one of the 149) has more than forty thousand citation. He does not need any manipulation, he has non-digital and digital reputation. When I asked him to follow my website he wrote back: ”Your new project sounds very interesting. I don’t blog, twitter, facebook, etc., but if you want to send along something am happy to comment.”

Well, this peer is in my age, but how about the millennials? As I learned from an article in Chronicle of Higher Education published several years ago, Eszter Hargittai (a sociologist than at Northwestern University, now in Zurich; well it happened than I met her in her parents’s house in Buda, when she was about five years old, but I have not met her later, maybe once) studied the on-line skills of millennials. Her results confirmed what many of us sees in the classrooms, there is an obvious inhomogeneity among the students. It seems to be a correlation between the socioeconomic status of the students and their skill in building their own digital reputation, and there are many students, whose only skill is being able to post on Facebook without thinking how any post form their image. While it ‘s important to tell students that digital reputation is important, and it is possible to teach how to build either personal or business reputation. I hope that it is true that honesty is an essential part of building your online reputation (as I read in Susan Gunelius’ Forbes article from with the title ”10 Ways To Successfully Build Your Online Reputation”), still in 2015 Amazon sued 1,114 people who were paid to publish fake five star reviews for products.

 

The dark side of a success story: the search engine manipulation effect and its possible impact

Actually a big industry emerged to make websites more visible, and there are SEO (search engine optimization) companies who do the job. Even Reputation Management Companies are ranked. In October 2017. As in the Western movies there are characters with white and black hats (white generally worn by heroes and black hats by villains) there are SEOs who make manipulation with ”white hat” on their heads, they are called ethical hackers, and and there are manipulators with ”black hat”. As always, in democratic societies, first there are rules accepted by the community. Second, some people (organizations etc.) have black hats, and try to evade these rules. Third, we cannot do else just help identify and neutralize the effects of these troublemakers. Here is a warning you may find useful: Black Hat SEO can take you to the top of website ranking in a very short time. But strictly speaking, it is totally illegal. If you don’t want to get penalized and crash your Google ranking forever, it is strongly recommended to avoid black hat SEO.

You can choose something (somebody) if you know it (her/him): recommendation systems

Algorithms: friends or foes?

You have not made recently any decision without seeing the opinion of the web. As I open Amazon, I see a holiday toy list, with Star Wars Droid Inventor Kit on the top. I consulted Tripadvisor when I returned to Liverpool after decades to find a small hotel near Liverpool John Moores University where I actually talked with the same title that the present book has. I don’t really use Yelp, it might be might my fault. I have my favorite restaurants in Budapest, from Spinoza to Pozsonyi Kisvendéglő. In Manhattan, do you need recommendation? Actually I learned just this summer that some – maybe mostly Italian – restaurants don’t take cash only to save credit card transactions. Match.com leads the dating websites, and Jdates is fifteenths now. (You will not believe it, but it is true. Just I wrote one more paragraph, and had to return to here since a this moment TripAdvisor sent an email with the Subject: The United States’  #1 restaurant announced! I don’t tell you who is #1, but you saw a picture at the top of this post. Fine-dining lover New Yorkers might identify the restaurant.

I am teaching a class this winter about the Complexity or Ranking, and plan to discuss with my students their experience with Netflix, so I suppose to collect nice stories, but I don’t know now the details. What we know already now of course that ”Netflix developed and maintains an extensive personalized video-recommendation system based on ratings and reviews by its customers”.

Recommendation systems use algorithms, so we do what the algorithms dictate us. The modern recommendation systems combine several strategies, by answering such kinds of questions:

  • Show me stuffs what my friends like (collaborative filtering)
  • Show me stuffs what I liked in the past (content-based filtering)
  •  Show me stuffs what fits to   my needs: (knowledge-based recommendation).

A little data science will be explained, but will not be painful. A lot of data is being collected, not only, but preferentially via social media, about consumption habits, in case of Netflix specifically about movies and TV shows, first to movies are characterized by some important features. How ”similar” are the two movies can be answered by analyzing the similarities between features. As Xavier Amarian, who served as Research Director for Netflix writes:
”We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. All that data is fed into several algorithms, each optimized for a different purpose. In a broad sense, most of our algorithms are based on the assumption that similar viewing patterns represent similar user tastes. We can use the behavior of similar users to infer your preferences.” If you know the distances i.e. the dissimilarity between any two items, you can make an ordered list.

The other side of the story: Netflix addiction
I have to admit that I am not a Netflix subscriber, so I have second-hand information only. “Binge watching” is an action to watch multiple episodes of a television series in rapid succession. While it shows some correlation to depression and loneliness, more or less we understand how our brain forces us to be addict. Episodes of series end with an exciting scene, trigger is pulled, but we don’t know the implications. Such kinds of clickhangers activate stress by increasing a stress-related hormone… so you push the button, look the next episode and so on… after several hours binge watching you may have a feeling, oh it was an achievement, so your brain releases more dopamine, a substance related to award, and there is a reinforcement signal creating a self-amplifying loop. So you might spend the whole weekend by looking Netflix.