Hey, What’s the Big Idea: Modeling the the Networks of Emerging Innovations

“Connex Labyrinth”, Image by fdecomite.

When you want to invite someone new into your LinkedIn network, the social business platform provides users with a simple formatted email making it easier to do this. It reads in part “I’d like to add you to my network”. Recently, a research team published the results of their work proving that a similar network effect 1 occurs not only among people, but also in a comparable manner among new ideas. As a result of such inventive schmoozing, ideas plus other new ideas can now be seen as yielding all kinds of unexpected links to innovation 2.

This “mathematical model for the emergence of innovations” mapping out all these processes was published by the School of Mathematical Sciences at Queen Mary University of London, as reported in a fascinating article about an exciting advance on Phys.Org in a story entitled Mathematicians Develop Model for How New Ideas Emerge, posted January 24, 2018. (No writer is credited.)  This article is a summary of the full paper by the research team responsible entitled Network Dynamics of Innovation Processes that appeared in the Physical Review Letters (subscription required), published on January 26, 2018.

I highly recommend a click-through and full read of the Phys.org article, including its two accompanying visualizations. I will recap and annotate some of the key points in this piece and then pose some of own questions about how and why ideas may either swipe left or swipe right when trying to meet up with each other.

New Modeling Combining Two Established Approaches

The research team was led by Professor Vito Latora, who was also the lead author of the paper. They found that by “studying creative processes and understanding how innovations arise” and, furthermore, how [mathematical] “novelties” can lead to additional discoveries, the results of these interactions can lead to “effective interventions” that could support the “success and sustainable growth” in our society. Similar patterns have been seen in scientific and artistic fields 3.

This was accomplished by first transposing the theory of the ‘adjacent possible’ from its original field of biological systems into, second, the “language of complex networks“. The former describes the “set of all novel opportunities” that arise whenever a new discovery appears. The latter has become a reliable means to study actual systems in the real world by examining the key relationships “between the components” and, in turn, modeling the “hidden structure behind many complex social phenomena”.

In effect, network modeling has been applied here to construct the “underlying space of relations among concepts”. Indeed, some very cool and productive connections are occurring within such idea-fueled networks.

Professor Latora further believes that understanding the key elements of a successful idea are critical to later making decisions, forming strategies and supporting successes.  He thinks that such results can be part of “sustainable growth in our society”.

Potential Benefits for Multiple Fields

Another new methodology was derived during the course of his team’s research: The concept of “reinforced walks” (as a subset of “random walks“, a form of mathematical object 4), was used as the basis to model the interaction among concepts and ideas. During all of this activity “innovation corresponds to the first visit of a site on the network”, as well as every time such a “walker”, as termed in the article, transits from one concept to another concept. The more this type of path is traveled, the more it becomes reinforced and thus productive. This network dynamic, named by the team as the “edge-reinforced random walk“, is clearly diagrammed and further described in the Phys.Org article’s first graphic.

In an actual case study applying this methodology, the research team built a database of 20 years’ worth of publications from a diversity of fields including “astronomy, ecology, economics and mathematics”. This was done to examine the emergence of new concepts. Their results of applying their edge-reinforced random walk model to this compilation was that they succeeded in reproducing evidence of the growth of knowledge in contemporary science.

Professor Latora and his team are currently working to extend their model by studying network spaces where several of these “walkers” are operating simultaneously 5.

My Questions

  • Can edge-reinforced random walks be adapted and applied to additional non-scientific and non-artistic domains such as history, politics and culture? That is, can it yield meaningful results and insights in sectors of society that are less data-derived?
  • How is this methodology distinguishable from various branches of artificial intelligence where vast stores of data are used to “train” the capabilities of these systems?
  • Can edge-reinforced random walks also be deployed as a form of predictive device? For example, what if a great deal more data from a larger diversity of fields was similarly compiled and tested, would this provide a partial preview into the future in science, technology and biology?
  • Taking this a step further, could edge-reinforced random walks be enhanced to include the capacity to predict or at least sense the possibilities and/or probabilities of entirely unpredictable major events such as market crashes? 6


For a comparative perspective, albeit a dated one published 18 years ago, on how human social systems and their interactions will still be needed in the all-encompassing “Information Age”, I suggest a book that is still considered a significant achievement for its time entitled The Social Life of Information, by John Seely Brown and David Duguid (Harvard Business Review, 2000).

1.  Network effects among people, populations and technologies have also been explored in these 10 Subway Fold posts.

2.  See also this April 28, 2016 Subway Fold post entitled Book Review of “Inventology: How We Dream Up Things That Change the World”

3.  Another example is the study of laws and legal precedents  in this manner as described in this May 15, 2015 Subway Fold post entitled Recent Visualization Projects Involving US Law and The Supreme Court.

4.  This concept as applied in securities trading is the subject of a classic text on this subject entitled A Random Walk Down Wall Street, by Burton Malkiel (W. W. Norton & Company, Eleventh Edition, 2016)

5.  In an entirely different context, fans of The Walking Dead will also appreciate how large groups of walkers manifest their own distinctly emergent behaviors.

6.  The leading text on this subject is The Black Swan: Second Edition: The Impact of the Highly Improbable, by Nassim Nicholas Taleb (Random House, 2010).

LinkNYC Rollout Brings Speedy Free WiFi and New Opportunities for Marketers to New York

Link.NYC WiFi Kiosk 5, Image by Alan Rothman

Link.NYC WiFi Kiosk 5, Image by Alan Rothman

Back in the halcyon days of yore before the advent of smartphones and WiFi, there were payphones and phone booths all over of the streets in New York. Most have disappeared, but a few scattered survivors have still managed to hang on. An article entitled And Then There Were Four: Phone Booths Saved on Upper West Side Sidewalks, by Corey Kilgannon, posted on NYTimes.com on February 10, 2016, recounts the stories of some of the last lonely public phones.

Taking their place comes a highly innovative new program called LinkNYC (also @LinkNYC and #LinkNYC). This initiative has just begun to roll out across all five boroughs with a network of what will become thousands of WiFi kiosks providing free and way fast free web access and phone calling, plus a host of other online NYC support services. The kiosks occupy the same physical spaces as the previous payphones.

The first batch of them has started to appear along Third Avenue in Manhattan. I took the photos accompanying this post of one kiosk at the corner of 14th Street and Third Avenue. While standing there, I was able to connect to the web on my phone and try out some of the LinkNYC functions. My reaction: This is very cool beans!

LinkNYC also presents some potentially great new opportunities for marketers. The launch of the program and the companies getting into it on the ground floor were covered in a terrific new article on AdWeek.com on February 15, 2015 entitled What It Means for Consumers and Brands That New York Is Becoming a ‘Smart City’, by Janet Stilson. I recommend reading it in its entirety. I will summarize and annotate it to add some additional context, and pose some of my own ad-free questions.

LinkNYC Set to Proliferate Across NYC

Link.NYC WiFi Kiosk 2, Image by Alan Rothman

Link.NYC WiFi Kiosk 2, Image by Alan Rothman

When completed, LinkNYC will give New York a highly advanced mobile network spanning the entire city. Moreover, it will help to transform it into a very well-wired “smart city“.¹ That is, an urban area comprehensively collecting, analyzing and optimizing vast quantities of data generated by a wide array of sensors and other technologies. It is a network and a host of network effects where a city learns about itself and leverages this knowledge for multiple benefits for it citizenry.²

Beyond mobile devices and advertising, smart cities can potentially facilitate many other services. The consulting firm Frost & Sullivan predicts that there will be 26 smart cities across the globe during by 2025. Currently, everyone is looking to NYC to see how the implementation of LinkNYC works out.

According to Mike Gamaroff, the head of innovation in the New York office of Kinetic Active a global media and marketing firm, LinkNYC is primarily a “utility” for New Yorkers as well as “an advertising network”. Its throughput rates are at gigabit speeds thereby making it the fastest web access available when compared to large commercial ISP’s average rates of merely 20 to 30 megabits.

Nick Cardillicchio, a strategic account manager at Civiq Smartscapes, the designer and manufacturer of the LinkNYC kiosks, said that LinkNYC is the only place where consumers can access the Net at such speeds. For the AdWeek.com article, he took the writer, Janet Stilson, on a tour of the kiosks include the one at Third Avenue and 14th Street, where one of the first ones is in place. (Coincidentally, this is the same kiosk I photographed for this post.)

There are a total of 16 currently operational for the initial testing. The WiFi web access is accessible with 150 feet of the kiosk and can range up to 400 feet. Perhaps those New Yorkers actually living within this range will soon no longer need their commercial ISPs.

Link.NYC WiFi Kiosk 4, Image by Alan Rothman

Link.NYC WiFi Kiosk 4, Image by Alan Rothman

The initial advertisers appearing in rotation on the large digital screen include Poland Spring (see the photo at the right), MillerCoors, Pager and Citibank. Eventually “smaller tablet screens” will be added to enable users to make free domestic voice or video calls. As well, they will present maps, local activities and emergency information in and about NYC. Users will also be able to charge up their mobile devices.

However, it is still too soon to assess and quantify the actual impact on such providers. According to David Krupp, CEO, North America, for Kinetic, neither Poland Spring nor MillerCoors has produced an adequate amount of data to yet analyze their respective LinkNYC ad campaigns. (Kinetic is involved in supporting marketing activities.)

Commercializing the Kiosks

The organization managing LinkNYC, the CityBridge consortium (consisting of Qualcomm, Intersection, and Civiq Smartscapes) , is not yet indicating when the new network will progress into a more “commercial stage”. However, once the network is fully implemented with the next few years, the number of kiosks might end up being somewhere between 75,000 and 10,000. That would make it the largest such network in the world.

CityBridge is also in charge of all the network’s advertising sales. These revenues will be split with the city. Under the 12-year contract now in place, this arrangement is predicted to produce $500M for NYC, with positive cash flow anticipated within 5 years. Brad Gleeson, the chief commercial officer at Civiq, said this project depends upon the degree to which LinkNYC is “embraced by Madison Avenue” and the time need for the network to reach “critical mass”.

Because of the breadth and complexity of this project, achieving this inflection point will be quite challenging according to David Etherington, the chief strategy officer at Intersection. He expressed his firm’s “dreams and aspirations” for LinkNYC, including providing advertisers with “greater strategic and creative flexibility”, offering such capabilities as:

  • Dayparting  – dividing a day’s advertising into several segments dependent on a range of factors about the intended audience, and
  • Hypertargeting – delivering advertising to very highly defined segments of an audience

Barry Frey, the president and CEO of the Digital Place-based Advertising Association, was also along for the tour of the new kiosks on Third Avenue. He was “impressed” by the capability it will offer advertisers to “co-locate their signs and fund services to the public” for such services as free WiFi and long-distance calling.

As to the brand marketers:

  • MillerCoors is using information at each kiosk location from Shazam, for the company’s “Sounds of the Street” ad campaign which presents “lists of the most-Shazammed tunes in the area”. (For more about Shazam, see the December 10, 2014 Subway Fold post entitled Is Big Data Calling and Calculating the Tune in Today’s Global Music Market?)
  • Poland Spring is now running a 5-week campaign featuring a digital ad (as seen in the third photo above). It relies upon “the brand’s popularity in New York”.

Capturing and Interpreting the Network’s Data

Link.NYC WiFi Kiosk 1, Image by Alan Rothman

Link.NYC WiFi Kiosk 1, Image by Alan Rothman

Thus far, LinkNYC has been “a little vague” about its methods for capturing the network’s data, but has said that it will maintain the privacy of all consumers’ information. One source has indicated that LinkNYC will collect, among other points “age, gender and behavioral data”. As well, the kiosks can track mobile devices within its variably 150 to 400 WiFi foot radius to ascertain the length of time a user stops by.  Third-party data is also being added to “round out the information”.³

Some industry experts’ expectations of the value and applications of this data include:

  • Helma Larkin, the CEO of Posterscope, a New York based firm specializing in “out-of- home communications (OOH)“, believes that LinkNYC is an entirely “new out-of-home medium”. This is because the data it will generate “will enhance the media itself”. The LinkNYC initiative presents an opportunity to build this network “from the ground up”. It will also create an opportunity to develop data about its own audience.
  • David Krupp of Kinetic thinks that data that will be generated will be quite meaningful insofar as producing a “more hypertargeted connection to consumers”.

Other US and International Smart City Initiatives

Currently in the US, there is nothing else yet approaching the scale of LinkNYC. Nonetheless, Kansas City is now developing a “smaller advertiser-supported  network of kiosks” with wireless support from Sprint. Other cities are also working on smart city projects. Civiq is now in discussions with about 20 of them.

Internationally, Rio de Janeiro is working on a smart city program in conjunction with the 2016 Olympics. This project is being supported by Renato Lucio de Castro, a consultant on smart city projects. (Here is a brief video of him describing this undertaking.)

A key challenge facing all smart city projects is finding officials in local governments who likewise have the enthusiasm for efforts like LinkNYC. Michael Lake, the CEO of Leading Cities, a firm that help cities with smart city projects, believes that programs such as LinkNYC will “continue to catch on” because of the additional security benefits they provide and the revenues they can generate.

My Questions

  • Should domestic and international smart cities to cooperate to share their resources, know-how and experience for each other’s mutual benefit? Might this in some small way help to promote urban growth and development on a more cooperative global scale?
  • Should LinkNYC also consider offering civic support services such as voter registration or transportation scheduling apps as well as charitable functions where pedestrians can donate to local causes?
  • Should LinkNYC add some augmented reality capabilities to enhance the data capabilities and displays of the kiosks? (See these 10 Subway Fold posts covering a range of news and trends on this technology.)

February 19, 2017 Update:  For the latest status report on LinkNYC nearly a year after this post was first uploaded, please see After Controversy, LinkNYC Finds Its Niche, by Gerald Schifman, on CrainsNewYork.com, dated February 15, 2017.

1.   While Googling “smart cities” might nearly cause the Earth to shift off its axis with its resulting 70 million hits, I suggest reading a very informative and timely feature from the December 11, 2015 edition of The Wall Street Journal entitled As World Crowds In, Cities Become Digital Laboratories, by Robert Lee Hotz.

2.   Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia (W. W. Norton & Company, 2013), by Anthony M. Townsend, is a deep and wide book-length exploration of how big data and analytics are being deployed in large urban areas by local governments and independent citizens. I very highly recommend reading this fascinating exploration of the nearly limitless possibilities for smart cities.

3.   See, for example, How Publishers Utilize Big Data for Audience Segmentation, by Arvid Tchivzhel, posted on Datasciencecentral.com on November 17, 2015

These items just in from the Pop Culture Department: It would seem nearly impossible to film an entire movie thriller about a series of events centered around a public phone, but a movie called – – not so surprisingly – – Phone Booth managed to do this quite effectively in 2002. It stared Colin Farrell, Kiefer Sutherland and Forest Whitaker. Imho, it is still worth seeing.

Furthermore, speaking of Kiefer Sutherland, Fox announced on January 15, 2016 that it will be making 24: Legacy, a complete reboot of the 24 franchise, this time without him playing Jack Bauer. Rather, they have cast Corey Hawkins in the lead role. Hawkins can now be seen doing an excellent job playing Heath on season 6 of The Walking Dead. Watch out Grimes Gang, here comes Negan!!

Studies Link Social Media Data with Personality and Health Indicators

twitter-292994_1280[This post was originally uploaded on January 27, 2015. It has been updated below with new information on March 20, 2015 and February 26, 2018.]

Reports of two new studies were issued recently describing meaningful connections between the predictive value of Facebook Likes and personality types, and next the parsing of language in Tweets to forecast the likelihood of heart disease. This presents us with an opportunity to examine two highly similar human health indicators that were identified by sophisticated analytics applied to massive troves of data generated by two of the world’s leading social media platforms. Where is all of this leading and what issues arise as a result? I will first summarize some parts of these two reports, add some links and annotations, and then pose some questions. I also highly recommend clicking through for a full read of both of pieces.

The first report was posted on NewScientist.com on January 12, 2015 with the concise title of What You ‘Like’ on Facebook Gives Away Your Personality by Hal Hodson. According to this article, researchers working at Stanford University and Cambridge University have developed an algorithm that, based completely upon what people “Like” on Facebook, can be determinative of a user’s personality. The data for this was gathered in a survey of 86,000 people who filled out personality questionnaires that were then matched against their activity on Facebook. Indeed, the results showed that this new method was more accurate than the determinations of the test subjects’ family and friends.

These characteristics are called the Big Five personality traits and include (as explored in detail in the preceding Wikipedia link):

  • Openness to experience
  • Conscientiousness
  • Extraversion
  • Agreeableness
  • Neuroticism

The article includes comments from David Funder of the University of California, Riverside, who is a researcher on personality, that while this study is “impressive”, it still does not provide a truly deep understanding of an individual’s personality. Funder’s work looks at 100 dimensions, a far larger number than the researchers in the Facebook study who focused upon the Big Five.

Nonetheless, two of these researchers on this new study, Youyou Wu  of Cambridge and Michael Kosinski of Stanford, believe their work is applicable on a global scale and applied in several areas. For instance,  they foresee their new Like algorithm could be used to in hiring operations to search large data files of candidates and identify those who might be most suitable for a particular job. Other possibilities include health and education. Kosinski also acknowledges that this approach would further require appropriate policy and technology considerations in order to address issues such its potential invasiveness.

(In a similar application Facebook Likes and other data from social media sites, universities in the US are now using such information and analytics to locate and pitch to alumni as potential donors as reported in a most interesting article in the January 25, 2015 edition of The New York Times entitled Your College May Be Banking on Your Facebook Likes, by Natasha Singer. Among other things, this story reports on the work and methods of two startups in this area called EverTrue and Graduway.)

The second report linking social media data to a health indicator was Scientists Say Tweets Predict Heart Disease and Community Health by Derrick Harris posted on Gigaom.com on January 22, 2015. In a study authored by researchers at the University of Pennsylvania, as part of their Well-Being Project, entitled Psychological Language on Twitter Predicts County-Level Heart Disease Mortality, they concluded that the vocabulary use by individuals in their Tweets can  predict “the rate of heart disease deaths in the counties where they live”. This phenomenon manifests itself by showing that Tweets concerning more upbeat topics and expressed in more positive terms correlated with lower mortality rates when compared to rates reported by the Center for Disease Control (CDC). Conversely, mortality rates were higher in areas “with angry language about negative topics”.

The accompanying side-by-said graphics of the Twitter data and the CDC data covering the upper right quarter of the US states and their constituent 1,300 counties, dramatically illustrates these findings. The pool of data was drawn from 148 million Tweets with geotags.

These results also provide further support for the accuracy and predictive validity of data from Twitter, notwithstanding any “inherent geographical biases”, and exceeding that of more “traditional polls or surveys”. Indeed, language in Tweets turns out to have a comparatively higher predictive value than other economic or health-related data. The researchers further believe that their findings might be more helpful when applied to “community-scale policies or interventions” rather than to assisting specific people.

My follow-up questions include:

  • Would mapping a statistically significant number of Twitter networks in counties with higher and/or lower mortality rates, a process described in the February 5, 2015 Subway Fold post entitled Visualization, Interpretation and Inspiration from Mapping Twitter Networks, provide additional insights that would be helpful to medical professionals and local policy planners? For example, are many of the negative Twitter posters in each other’s networks such that they become self-reinforcing? Are there recognizable network effects occurring that can somehow be corrected with regards to the degree of negativity and, in turn, public health? Would this pose any legal, policy or privacy issues?
  • For both of these articles, do these types of findings require more rigorous and wider-scale mathematical and scientific analysis before applying them to such critically important mental and physical health matters? If so, should such testing be done by public or private institutions, universities and/or the government agencies?
  • As first expressed in this November 22, 2014 Subway Fold post entitled Minting New Big Data Types and Analytics for Investors, how are the differences in correlation and causation being factored into these studies? Given the skepticism expressed above about Facebook Likes being so indicative about personality, are there other effects and influences that need to be identified and filtered out of these types of conclusions?
  • If the usage and analysis of social media data continues to grow in areas, well, like employment, education and health, what protections, if any, should people be given, by law and/or the social media companies, to protect themselves or opt out in advance of any potentially negative consequences?

March 20, 2015 Update:

Providing some very worthwhile additional insight and analysis of the University of Pennsylvania study covered in the initial post above, Maria Konnikova has written a very engaging article entitled What Your Tweets Say About You that was posted on The New Yorker website on March 17, 2015. I highly recommend clicking through and reading the entire text. I will sum up just some of the key points, add some links and pose several  additional questions.

The research study (linked to above), was conducted by a team led by psychologist and Professor Johannes Eichstaedt. Their main conclusion was that the collection and subsequent linguistic analysis of tweets proved to be validly predictive of locations with higher concentrations of fatalities from cardiovascular disease. The inverse was also true that geographic clusters of tweets with more positive content had lower death rates from the same cause. It was not that the population tweeting had heart disease, but rather, there is a discernible correlation between angrier content and a higher incidence of the heart disease within an area.

This “correlation is especially strange” due to the fact that Twitter users are generally younger that individuals who perish from heart ailments. Citing a January 9, 2015 study from the Pew Research Center entitled Demographics of Key Social Networking Platforms (also, imho, well worth a click-through and full reading), which, among other things tabulates the ages of the users of all of the leading social media platforms. Just 22% of US Twitter users are more than 50 years old. However, the relative risk of heart disease does not begin to rise until decades later.

How, then, to analytically connect younger people in a particular area who are posting negative tweets with their older neighbors who face higher chances of developing heart disease? The researchers theorize that the tweets “may be a window into the aggregated and powerful effects if the community context”. The overall health of people living in a particular area that is “poorer, more fragmented” and not as healthy as those residing in “richer, integrated ones”. As a result, the angrier tweets of someone in their twenties are likely reflective of an area with higher life stressors that, in turn, later result in more heart-related deaths.

Nonetheless, another renowned expert in this field of linguistic analysis of text, James Pennebaker, recommended caution in drawing any connection based upon this data. He urges further study of the data and posing additional questions about causation. Currently, in his own work, he is examining Twitter data to see how family and religious factors evolve.

There is also value in studying social media content of individuals. For example, Microsoft has previously studied 70,000 tweets of people with depression and then used this data to construct a “predictive index” to identify “other users who were likely depressed based on their social-media posts”.

Eisenstaedt’s team is continuing their work by looking at Twitter data for individuals and communities over time periods, rather than a “snapshot” data set. They are also adding Facebook profiles to their work.

Finally, Pennebaker believes that social media may also generate positive effects on mental health based on his previous studies on the benefits of keeping a personal journal. This may be so despite the private nature of a journal and the very public access of social media and its interactivity.

My additional questions are as follows:

  • Will additional discreet language patterns be discovered and validated that will indicate concentrations of other medical conditions within communities? Are we only at the beginning of using textual analysis of tweets as a metric of the states of local health?
  • Given that there is a lag time of years between negative tweets and the appearance of heart disease, should interventions be undertaken within a community at higher risk and, if so, by whom and at what cost?
  • Are other negative online behaviors such as cyberbullying indicative of some form of identifiable illness that can be treated on a community-wide basis or must this be dealt with on an individual in a case-by-case manner?

February 26, 2018 Update: Using social media activity data to diagnose and treat possible health conditions has advanced in a number of new systems and studies as reported in today’s New York Times in an article entitled How Companies Scour Our Digital Lives for Clues to Our Health, by Natasha Singer, dated February 26, 2018. 

2014 LinkedIn Usage Trends and Additional Data Questions

A very enlightening report was recently published on Forbes.com in May entitled New Research: 2014 LinkedIn User Trends (And 10 Top Surprises) that I highly recommend for its insights, detail and thoroughness. This professional social and networking sites continues to grow its global use base, services, influence and reputation on a daily basis. The particulars provided within this report about the size ranges of users’ personal networks, the percentages of free versus paid account, the average time users spend on the site, awareness of available feature sets, the utility of special interest groups, and other data points illuminate a truly thriving and extensible services.

What is particularly impressive about format and content of this report is the infographic it contains entitled Portrait of a LinkedIn User 2013 Edition. This expertly limns the who’s who and what’s what of the user base. Moreover, it sets into perspective just how unique LinkedIn is among the other leading social networks such as Facebook, Twitter, Google+ and others.

Having viewed many other infographics online, imho, this one receives an A+ from me for its simple and engaging design which belie such a wealth of useful information. I believe that you will learn much about LinkedIn here while simultaneously viewing a clinic on how to create a highly effective visual representation of data.

The data and graphics presenting a distribution of percentages of the relative sizes of users’ networks immediately grabbed my attention. The largest percentage, 25.2% of the entire user base, of first degree user connections falls between 500 to 999. Okay, I’m in there with my personal network, too. What I would further be interested in knowing is whether dividing the total number of second degree connections by the number of first degree connections, would produce an accurate average number of second degree connections among all of a user’s first degree connections. For example, if I have 100 first degree connections and, in turn, from them 2,000 second degree connections, does that mean that the average number of second degree connections among the first degree connections in my network is equal to 20? If this is a valid number, how can a user apply possibly this figure to advancing the size of his or her network, increase their presence and influence within their network, and assist in business and/or career development?

Another data point I am curious about is whether the degree to which one’s personal network on LinkedIn grows buy itself each day worth examining? That is, if you stop issuing or accepting new invitations to join other personal networks for a period of time, how much does a personal network continue to grow by virtue only of your own first degree connections continuing to send and accept invitations? If, for example, a network will increase its daily second degree connections in this manner by 0.005%, how can a user employ this factor in the same set of questions in the previous paragraph?

Finally, I would like to see some more granular data about networks involving particular professions and job titles. For instance, do attorneys as opposed to web designers have, on average, larger or smaller personal networks? As well, would this evidence direct causation or perhaps just correlation in the data?

Another POV on the Power of the Network Effect

The propulsive power, structural operations and connective benefits of the Network Effect have been identified and studied in many fields of science, technology, the Web, telecom, transportation, biology, neuroscience and many others. Indeed, the Network Effect is writ large across nearly every aspect of the Web’s endless reach.

In an insightful post on Socialmediatoday.com on July 16, 2014, entitled Connection Brings Opportunity at Exponential Scale the author, Brian Vellmure, found his inspiration for this piece during a recent trip where he suddenly lost all connectivity. He then uses this to write about his perceptions of the multiplier effect the web has on creativity, science and industry by virtue of its potential for boundless numbers of connections to others out there in c-space. In effect, more connections equate to more value added with each additional user and node. He ends by posing the questions of how readers can use this to “evolve your organization to thrive in this new environment?”. I recommend a click-through and read of this worthwhile new take on this ubiquitous phenomenon.