Connectedness
Helping businesses link to results.
Updated: 2 hours 50 min ago
Geographic networks
Geography (or spatial arrangement) of nodes is often an important factor in network dynamics. Though it is straightforward to map geographical information by itself, mapping that information simultaneously with network data is quite a challenge.
In collaboration with Holly Massett and her team at the National Cancer Institute, I have been tackling the geographic + network mapping problem head on. Holly and I recently presented some of our results, and she graciously gave me permission to share them.
What happens when we draw a network map with geographically located nodes? We get a map with lines on it:
The geography is plainly apparent, but the network structure is all but invisible. That's a shame, because the network structure hidden above is actually quite striking when you redraw the above network using traditional network layout techniques:
Now we can clearly see that there is one node that bridges between two distinct clusters.
As a simple first step toward integrating these two important views of the above collaboration network, I created this slide show, which morphs back and forth between pure geography and pure network information, showing the interaction of the two along the way (RSS readers must view my actual blog to see this):
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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In collaboration with Holly Massett and her team at the National Cancer Institute, I have been tackling the geographic + network mapping problem head on. Holly and I recently presented some of our results, and she graciously gave me permission to share them.
What happens when we draw a network map with geographically located nodes? We get a map with lines on it:
The geography is plainly apparent, but the network structure is all but invisible. That's a shame, because the network structure hidden above is actually quite striking when you redraw the above network using traditional network layout techniques:
Now we can clearly see that there is one node that bridges between two distinct clusters.
As a simple first step toward integrating these two important views of the above collaboration network, I created this slide show, which morphs back and forth between pure geography and pure network information, showing the interaction of the two along the way (RSS readers must view my actual blog to see this):
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Working the math in your favor
Last week I was part of a panel discussion about technology and business. Forty or fifty accomplished businesswomen attended--I was the only man in the room. In hindsight, this was a perfect opportunity for me to focus on Bion's three pillars of group behavior (or at least the first of those three pillars, having the mating partner ratio so heavily in my favor); however, I confess I occasionally let my thoughts drift from that #1 priority and instead contemplated the diverse perspectives on technology represented in in the room.
The audience was predominantly business-savvy and ranged from the tech-curious to the tech-confused. It was not the most receptive setting for preaching a Taoist bliss of ignorance, but that's what I pitched, with lines like "the best technology is whatever you're using now"; "reading email when you receive it lowers your IQ more than chronic pot-smoking"; and "technology is implemented to benefit its creators, not its users, so look for technology where the users and the creators are the same."
The room was filled with questions about LinkedIn and Twitter. I realized that LinkedIn has taken hold of a much wider business audience than it had when I last disparaged it on these pages 2-3 years ago. Sensible successful business people speak with complete earnestness about the 500,000 people in their LinkedIn network, and I am speechless.
I have some hope. My LinkedIn network has 2,850,200 people, including 16,927 new connections in just the last 4 days. Before I leverage all of them, however, I sense that LinkedIn is giving me an opportunity to update this old joke:
A museum guide leads a group of tourists through a dinosaur exhibit. Stopping at an impressively scary skeleton baring its fossilized teeth, he says, "This T-Rex is 70 million and 3 years old." One of the tourists responds, "Wow! How do they figure that out so precisely?" The guide responds, "Well, when I started working here, this skeleton was 70 million years old, and that was 3 years ago."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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The audience was predominantly business-savvy and ranged from the tech-curious to the tech-confused. It was not the most receptive setting for preaching a Taoist bliss of ignorance, but that's what I pitched, with lines like "the best technology is whatever you're using now"; "reading email when you receive it lowers your IQ more than chronic pot-smoking"; and "technology is implemented to benefit its creators, not its users, so look for technology where the users and the creators are the same."
The room was filled with questions about LinkedIn and Twitter. I realized that LinkedIn has taken hold of a much wider business audience than it had when I last disparaged it on these pages 2-3 years ago. Sensible successful business people speak with complete earnestness about the 500,000 people in their LinkedIn network, and I am speechless.
I have some hope. My LinkedIn network has 2,850,200 people, including 16,927 new connections in just the last 4 days. Before I leverage all of them, however, I sense that LinkedIn is giving me an opportunity to update this old joke:
A museum guide leads a group of tourists through a dinosaur exhibit. Stopping at an impressively scary skeleton baring its fossilized teeth, he says, "This T-Rex is 70 million and 3 years old." One of the tourists responds, "Wow! How do they figure that out so precisely?" The guide responds, "Well, when I started working here, this skeleton was 70 million years old, and that was 3 years ago."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Viewing network data in Excel... with banana
Today I received an invitation from Harvard's Program on Networked Governance to watch Marc Smith demonstrate the powers of .NetMap --- a network visualization tool that runs inside Excel 2007. Maybe I will upgrade my MS Office and check it out; the screen shots look good.
On a more personal note, my BU faculty site is up. The site demonstrates what any monkey can do after enough time hanging with my students.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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On a more personal note, my BU faculty site is up. The site demonstrates what any monkey can do after enough time hanging with my students.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Structural equivalence: related tags in social bookmarking
In my "Holy Trinity of Network Power," structural equivalence is conceptually the most obscure. But practically speaking, it is easy to use. For example, searching for "sna" with the social bookmarking engine delicious provides the following:
I have enlarged and highlighted the "Related Tags" provided by delicious. This sort of information helps people find and learn from others with shared interests, using structural equivalence, regardless of how many degrees of separation they have on Facebook or LinkedIn. I'll expand more on this idea soon.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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I have enlarged and highlighted the "Related Tags" provided by delicious. This sort of information helps people find and learn from others with shared interests, using structural equivalence, regardless of how many degrees of separation they have on Facebook or LinkedIn. I'll expand more on this idea soon.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Structural equivalence: social bookmarking on a corporate intranet
Last week Laurie Damianos of MITRE presented to the Boston KM Forum, sharing her experience implementing a social bookmarking system within the enterprise.
For newbies, I often describe social bookmarking as similar to Amazon.com in its ability to track both people who read the same "books" and "books" that share common audiences--whether those "books" are literal or metaphorical. For the mathematically curious, structural equivalence is the underlying principle. Also, here's an introduction to social bookmarking I wrote a while back. Bill Ives has written a few times about applying social bookmarking within the enterprise, including specific references to MITRE's and IBM's experiences.
Laurie's presentation was great and left me feeling more excited than ever about business applications of social bookmarking. But I also left feeling puzzled by the response I got to one of my (many) questions. One way MITRE manages its in-house social bookmark system is by deleting bookmarks created by people who have since left the company. When I asked if there had been any debate within MITRE about deleting this information, I got two responses from the group: (1) Bookmarks are deleted, but the content (referenced by the former bookmarks) remains; and (2) Without the context of an owner, what good is a bookmark?
These two assertions strike me as odd, especially coming from a group that aims to solve the "lost knowledge" problem (e.g., Dave DeLong).
Deleting bookmarks of ex-employees seems to me on a par with burning bibliographies of articles whose authors are dead. After all, the artices and their references still exist. Furthermore, the authors are no longer around to provide context to their bibliographies. So why don't we save library shelf space and rip out all those bibliographies? Anyone who has ever done research can answer that question.
If bibliography-burning seems extreme, here's a milder example much closer to the MITRE reality: Amazon.com could save tons of disk space if it deleted the purchase records of people who haven't bought anything for the past year (i.e., those who have "left Amazon"). I wonder what the managers of Amazon would say to someone who suggested this strategy and argued that (1) the products purchased are still listed, and (2) the purchasers have left, so why bother to keep those records?
As pioneers of collaborative filering, managers at Amazon would probably recognize purchase records of the departed as a valuable resource. Acquiring those records in the first place is one of the biggest competitive advantages a service like Amazon can achieve--commonly known as surmounting the "cold start problem."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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For newbies, I often describe social bookmarking as similar to Amazon.com in its ability to track both people who read the same "books" and "books" that share common audiences--whether those "books" are literal or metaphorical. For the mathematically curious, structural equivalence is the underlying principle. Also, here's an introduction to social bookmarking I wrote a while back. Bill Ives has written a few times about applying social bookmarking within the enterprise, including specific references to MITRE's and IBM's experiences.
Laurie's presentation was great and left me feeling more excited than ever about business applications of social bookmarking. But I also left feeling puzzled by the response I got to one of my (many) questions. One way MITRE manages its in-house social bookmark system is by deleting bookmarks created by people who have since left the company. When I asked if there had been any debate within MITRE about deleting this information, I got two responses from the group: (1) Bookmarks are deleted, but the content (referenced by the former bookmarks) remains; and (2) Without the context of an owner, what good is a bookmark?
These two assertions strike me as odd, especially coming from a group that aims to solve the "lost knowledge" problem (e.g., Dave DeLong).
Deleting bookmarks of ex-employees seems to me on a par with burning bibliographies of articles whose authors are dead. After all, the artices and their references still exist. Furthermore, the authors are no longer around to provide context to their bibliographies. So why don't we save library shelf space and rip out all those bibliographies? Anyone who has ever done research can answer that question.
If bibliography-burning seems extreme, here's a milder example much closer to the MITRE reality: Amazon.com could save tons of disk space if it deleted the purchase records of people who haven't bought anything for the past year (i.e., those who have "left Amazon"). I wonder what the managers of Amazon would say to someone who suggested this strategy and argued that (1) the products purchased are still listed, and (2) the purchasers have left, so why bother to keep those records?
As pioneers of collaborative filering, managers at Amazon would probably recognize purchase records of the departed as a valuable resource. Acquiring those records in the first place is one of the biggest competitive advantages a service like Amazon can achieve--commonly known as surmounting the "cold start problem."
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Network Centrality: Rob Cross Braintrust Keynote and Density
As an example of network-cluster-driven-behavior, last time I suggested a simple way to stereotype the work of Rob Cross. The first row of the table below, from his "Braintrust Keynote" presentation, was my Exhibit A:
The other rows of the above table deserve comment as well. Let's focus today on the third row, Centrality, with apologies to those who thought that my recent series on network centrality was finished.
In all my posts on centrality, I never actually described a mathematical formula for calculating it. There are quite a few reasonable ways to define centrality. See this post for links to a few of them. We see above that Cross's Braintrust Keynote describes centrality as the "average # of relationships per person." Unfortunately, this notion of centrality has nothing at all to do with what other people mean when they say "centrality."
First, a preliminary clarification: "Centrality" is most commonly used to describe a single node in a network, but it is also used to describe a global property of an entire network (much like "centralization" in the bottom row of the Braintrust Keynote table above). So we should be clear that "average # of relationships per person" is a global property of an entire network.
With that in mind, observe the following two networks that have exactly the same number of nodes, exactly the same number of edges, and hence exactly the same value of "centrality" or "average # of relationships per person":
I don't think too many people would describe the above two networks as having equal centrality, despite the Braintrust Keynote assertion.
It's a shame to equate "centrality" and "average # of relationships per person." They are two of my most favorite network metrics. I have devoted enough recent bandwidth to centrality to make clear my affinity for that metric. Soon, I will explain why I like "average # of relationships per person" as an alternative to density (top row of the Braintrust Keynote table) that is much less susceptible to the network size bias noted by Kathleen Carley.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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The other rows of the above table deserve comment as well. Let's focus today on the third row, Centrality, with apologies to those who thought that my recent series on network centrality was finished.
In all my posts on centrality, I never actually described a mathematical formula for calculating it. There are quite a few reasonable ways to define centrality. See this post for links to a few of them. We see above that Cross's Braintrust Keynote describes centrality as the "average # of relationships per person." Unfortunately, this notion of centrality has nothing at all to do with what other people mean when they say "centrality."
First, a preliminary clarification: "Centrality" is most commonly used to describe a single node in a network, but it is also used to describe a global property of an entire network (much like "centralization" in the bottom row of the Braintrust Keynote table above). So we should be clear that "average # of relationships per person" is a global property of an entire network.
With that in mind, observe the following two networks that have exactly the same number of nodes, exactly the same number of edges, and hence exactly the same value of "centrality" or "average # of relationships per person":
I don't think too many people would describe the above two networks as having equal centrality, despite the Braintrust Keynote assertion.
It's a shame to equate "centrality" and "average # of relationships per person." They are two of my most favorite network metrics. I have devoted enough recent bandwidth to centrality to make clear my affinity for that metric. Soon, I will explain why I like "average # of relationships per person" as an alternative to density (top row of the Braintrust Keynote table) that is much less susceptible to the network size bias noted by Kathleen Carley.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Network Clustering: Guide to Stereotyping Rob Cross and Kathleen Carley
Recently I mentioned how network clustering on the WWW indicates that Rob Cross and Kathleen Carley each have their own close-knit camps that co-dominate the world of "organizational network analysis." Before that, I shared Ron Burt's point that such close-knit camps are known not only for amazing productivity but also for stereotyping outsiders.
I am outside both the Cross and Carley camps, but I enjoy stereotyping as much as anyone, so today I provide convenient superficial labels with which my readers can simplify the contributions of these two notable network leaders.
Guide to stereotyping Rob Cross and Kathleen Carley:
(1) The recent research of the Network Roundtable features Cross's "Braintrust Keynote Presentation." Here is his third slide:
Note the simple and compelling story in the top row of the table: Network density within and across departments of less than 20% indicates little collaboration. If you read the actual presentation, you'll see that the "target density" is only 9.4% because the current density is less than half that, so the target is a healthy step up towards 20%. I will skip the other rows of the table for now.
(2) Kathleen Carley's camp responds to the above story with the following article:
As far as stories go, this article sucks. But look, it is classified under "statistical simulation," because the researchers use computer programs not only to analyze networks, but also to create the very networks that they study (no pesky data collection necessary).
For those whose eyes are glazing over, let me summarize the computer model punchline with a picture. The following three networks all have exactly the same density, 20%; and so according to Cross each of the three networks below has exactly the minimum recommended allowance of connectivity to indicate collaboration:
As you can see, density of 20% means different things depending on how many nodes are in the network.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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I am outside both the Cross and Carley camps, but I enjoy stereotyping as much as anyone, so today I provide convenient superficial labels with which my readers can simplify the contributions of these two notable network leaders.
Guide to stereotyping Rob Cross and Kathleen Carley:
- Rob Cross provides stories for business
- Kathleen Carley provides computer models for the military
(1) The recent research of the Network Roundtable features Cross's "Braintrust Keynote Presentation." Here is his third slide:
Note the simple and compelling story in the top row of the table: Network density within and across departments of less than 20% indicates little collaboration. If you read the actual presentation, you'll see that the "target density" is only 9.4% because the current density is less than half that, so the target is a healthy step up towards 20%. I will skip the other rows of the table for now.
(2) Kathleen Carley's camp responds to the above story with the following article:
As far as stories go, this article sucks. But look, it is classified under "statistical simulation," because the researchers use computer programs not only to analyze networks, but also to create the very networks that they study (no pesky data collection necessary).
For those whose eyes are glazing over, let me summarize the computer model punchline with a picture. The following three networks all have exactly the same density, 20%; and so according to Cross each of the three networks below has exactly the minimum recommended allowance of connectivity to indicate collaboration:
As you can see, density of 20% means different things depending on how many nodes are in the network.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Read email or smoke pot---The choice is yours
While playing hooky from Rob Cross's school of networks, I am free to indulge in all kinds of reckless neuron-destroying behavior. One option is attending to email, which is even better than pot-smoking at reducing IQ.
Chances are you know someone with an email problem. Give them the gift of 5 additional IQ points by inviting them to take this survey, created by Peggy Kuo at the University of New South Wales, Australia:
Email Addiction in the Workplace.
Link to survey: http://www.questionpro.com/akira/TakeSurvey?id=1023102
The aim of this study is to determine if Email Addiction exists in the workplace; if so what factors contribute to it and how can it be measured or determined. In addition we also aim to determine the impacts it has on productivity in the workplace.
If you decide to participate, you will be asked to complete an online survey. It is envisaged that the survey will take between 5-10 minutes to complete. There are no known or foreseeable risks associated with the survey.This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Chances are you know someone with an email problem. Give them the gift of 5 additional IQ points by inviting them to take this survey, created by Peggy Kuo at the University of New South Wales, Australia:
Email Addiction in the Workplace.
Link to survey: http://www.questionpro.com/akira/TakeSurvey?id=1023102
The aim of this study is to determine if Email Addiction exists in the workplace; if so what factors contribute to it and how can it be measured or determined. In addition we also aim to determine the impacts it has on productivity in the workplace.
If you decide to participate, you will be asked to complete an online survey. It is envisaged that the survey will take between 5-10 minutes to complete. There are no known or foreseeable risks associated with the survey.This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Network Clustering: Rob Cross and Kathleen Carley
Next Monday, Sept 8, begins the 2-day Network Roundtable Fall Conference. Rob Cross at UVA has led the Network Roundtable from its inception. He and his colleagues have quite an agenda planned for their time in DC.
My regular readers with sharp eyes may have noticed Rob Cross in a recent post of mine. That post introduced network clustering with an example --- a WWW clustering analysis of "organizational network analysis" computed by Grokker:
One of my favorite metaphors for clustering analysis is the table of contents. It is useful for seeing the big picture, all-inclusively, broken down into sub-categories. In an organizational network setting, a natural application would be identifying communities of practice (including those that don't yet recognize themselves as such).
Continuing with the book metaphor, we can see that the WWW authors of organizational network analysis have devoted "chapters" to these topics:
Another way to view these "book chapters" is as "closed networks" (relatively speaking), as I described in my last post. I refer my readers again to that post, this time keeping Rob Cross and Kathleen Carley in mind. It's fun to speculate how the Cross and Carley camps employ stereotypes to describe their counterparts.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.
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My regular readers with sharp eyes may have noticed Rob Cross in a recent post of mine. That post introduced network clustering with an example --- a WWW clustering analysis of "organizational network analysis" computed by Grokker:
One of my favorite metaphors for clustering analysis is the table of contents. It is useful for seeing the big picture, all-inclusively, broken down into sub-categories. In an organizational network setting, a natural application would be identifying communities of practice (including those that don't yet recognize themselves as such).
Continuing with the book metaphor, we can see that the WWW authors of organizational network analysis have devoted "chapters" to these topics:
- Social networks
- Organizational systems
- Public health
- Information management
- Knowledge
- Tools
- Rob Cross
- Kathleen M Carley
- Other
Another way to view these "book chapters" is as "closed networks" (relatively speaking), as I described in my last post. I refer my readers again to that post, this time keeping Rob Cross and Kathleen Carley in mind. It's fun to speculate how the Cross and Carley camps employ stereotypes to describe their counterparts.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.
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Network Clustering: The Power of Reputation
As we leave our series on network centrality and begin an exploration of network clustering, who better to help us bridge the gap than Ron Burt. Burt is perhaps best known for his amazing network-based research on innovation and the source of good ideas, which brought "structural holes" to the world's attention. In Brokerage & Closure he expands these ideas into book form and brings additional attention to "closure," a key trait related to network clustering.
Very briefly, closure refers to the interconnectedness of one's contacts: When my contacts don't know each other, my network is "open," and when they do know each other, my network is "closed." Assuming that I am #1 (naturally), two extremes of open (left) and closed (right) are pictured below:"Open" and "closed" are pretty much the same as bridging and bonding, as I have discussed before:
For more discussion of network closure, I recommend Burt's online notes for his executive MBA course, "Strategic Leadership," specifically the chapter on Closure, which I would sum up with these two points:
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.
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Very briefly, closure refers to the interconnectedness of one's contacts: When my contacts don't know each other, my network is "open," and when they do know each other, my network is "closed." Assuming that I am #1 (naturally), two extremes of open (left) and closed (right) are pictured below:"Open" and "closed" are pretty much the same as bridging and bonding, as I have discussed before:
For more discussion of network closure, I recommend Burt's online notes for his executive MBA course, "Strategic Leadership," specifically the chapter on Closure, which I would sum up with these two points:
- The peer pressure created by closed networks builds commitment and productivity
- The peer pressure created by closed networks reinforces groupthink and promotes mindless stereotypes
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2007 by Connective Associates LLC except where otherwise noted.
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NSF and Google-induced stupidity
The NSF has just published Fostering Learning in the Networked World: The Cyberlearning Opportunity and Challenge. Reading it reminds me of why I bailed out of academia. The introduction starts: "To address the global problems of war and peace, economics, poverty, health, and the environment, we need a world citizenry with ready access to knowledge about science, technology, engineering, and mathematics."
Wow. Another thing the world citizenry needs is a ban on vapid topic sentences whose only purpose is to inflate the perceived importance of the author's pet project.
In the NSF-funded land of cyberlearning, there is a five-tiered hierarchy of human interaction, represented by the cool picture below: The report explains the picture thus: "[The figure above] depicts historical advances in the communication and information resources available for human interaction. Basic face-to-face interaction at the bottom level requires no resources to mediate communication. The second wave of resources offered symbol systems such as written language, graphics, and mathematics but introduced a mediating layer between people. The communication revolution of radio, telephony, television, and satellites was the third wave. The outcomes of the fourth wave—networked personal computers, web publishing, and global search—set the stage for the fifth wave of cyberinfrastructure and participatory technologies that are reviewed in our report."
So, we are going to solve the "global problems of war and peace" with a framework that explicitly omits mediation from the realm of face-to-face communication. I wonder how much cyberinfrastructure South Ossetia would need to put this framework to use.
Next time I will get back on my network clustering thread again...
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Wow. Another thing the world citizenry needs is a ban on vapid topic sentences whose only purpose is to inflate the perceived importance of the author's pet project.
In the NSF-funded land of cyberlearning, there is a five-tiered hierarchy of human interaction, represented by the cool picture below: The report explains the picture thus: "[The figure above] depicts historical advances in the communication and information resources available for human interaction. Basic face-to-face interaction at the bottom level requires no resources to mediate communication. The second wave of resources offered symbol systems such as written language, graphics, and mathematics but introduced a mediating layer between people. The communication revolution of radio, telephony, television, and satellites was the third wave. The outcomes of the fourth wave—networked personal computers, web publishing, and global search—set the stage for the fifth wave of cyberinfrastructure and participatory technologies that are reviewed in our report."
So, we are going to solve the "global problems of war and peace" with a framework that explicitly omits mediation from the realm of face-to-face communication. I wonder how much cyberinfrastructure South Ossetia would need to put this framework to use.
Next time I will get back on my network clustering thread again...
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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Network Clustering: The Un-Google
Having finished our series on network centrality, we now approach its most natural complement: network clustering.
An easy way to appreciate the usefulness of network clustering is to try search engines that (unlike Google) are not centrality-driven. There are quite a few such search engines out there. They are great at providing a sense of direction within a previously unknown field --- when you're not yet sure exactly what question you're asking. In contrast, Google is better when your query is more specific, or when you just don't care about the rest of the forest, dammit, and want to find the biggest most popular tree ASAP.
Below are two examples of how non-centrality-based search engines display the WWW of "organizational network analysis". Click on either image to go to the search engine pictured.
There are dozens more search engines listed here by search engine junkie Bill Sebald.
I hope you enjoy the Un-Google world. Soon I'll say more about understanding this world with the help of network clustering.
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This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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An easy way to appreciate the usefulness of network clustering is to try search engines that (unlike Google) are not centrality-driven. There are quite a few such search engines out there. They are great at providing a sense of direction within a previously unknown field --- when you're not yet sure exactly what question you're asking. In contrast, Google is better when your query is more specific, or when you just don't care about the rest of the forest, dammit, and want to find the biggest most popular tree ASAP.
Below are two examples of how non-centrality-based search engines display the WWW of "organizational network analysis". Click on either image to go to the search engine pictured.
There are dozens more search engines listed here by search engine junkie Bill Sebald.
I hope you enjoy the Un-Google world. Soon I'll say more about understanding this world with the help of network clustering.
-->
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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NetDraw / UCINET tutorial; networks = organizing
Link of the week: Network Mapping as a Diagnostic Tool, by Louise Clark. This is the best NetDraw user's guide I have seen. Thanks to Cai Kjaer at www.onasurveys.com (via his helpful wiki) for alerting me to this resource.
A few weeks ago I got an anonymous email with nothing but this quote:
"Science is organized knowledge. Wisdom is organized life."
--Immanuel KantThat is some interesting spam. It got me thinking: Is "organized" really the fundamental property of science and wisdom? No, I decided; it's just a word making a pithy quote. I then forgot the matter, only to remember it today, when I read "Using Emergence to Take Social Innovation to Scale," by Meg Wheatley and Debbie Frieze of The Berkana Institute. They say:
"Networks are the only form of organization on this planet used by living systems."True enough, but I claim their statement is too weak. I would rephrase it "Networks = Organization." If you disagree, please send me a counterexample in the form of an organizing principle that does not invoke things (i.e., nodes) and relationships (i.e., links). And feel free to consider other planets, non-living systems, dark matter, alternate universes, etc. You must also agree to let me use confusing mathematical machinery in order to refute your counterexample. The best "counterexamples" I have so far are organizing by space and time. For example, jellyfish organize by drifting near the surface of the ocean, and people organize by sleeping when it's dark.
Once you accept that Networks = Organization, Wheatley and Frieze's assertion becomes somewhat less interesting; however, it does (somehow) lead to the Berkana-esque question: Isn't it odd that the words "organization" and "organic" have the same root? Doubters like myself can verify right here the etymological network connecting "organization" with "organic." The root is the Greek organon, literally "that with which one works," and which since the 12th century has described not only tools but also musical instruments and body parts.
Putting all our quotes and equations together, we have:
"Science is knowing tools, musical instruments, and body parts. Wisdom is living tools, musical instruments, and body parts."And that, dear reader, is an org chart that really counts.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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A few weeks ago I got an anonymous email with nothing but this quote:
"Science is organized knowledge. Wisdom is organized life."
--Immanuel KantThat is some interesting spam. It got me thinking: Is "organized" really the fundamental property of science and wisdom? No, I decided; it's just a word making a pithy quote. I then forgot the matter, only to remember it today, when I read "Using Emergence to Take Social Innovation to Scale," by Meg Wheatley and Debbie Frieze of The Berkana Institute. They say:
"Networks are the only form of organization on this planet used by living systems."True enough, but I claim their statement is too weak. I would rephrase it "Networks = Organization." If you disagree, please send me a counterexample in the form of an organizing principle that does not invoke things (i.e., nodes) and relationships (i.e., links). And feel free to consider other planets, non-living systems, dark matter, alternate universes, etc. You must also agree to let me use confusing mathematical machinery in order to refute your counterexample. The best "counterexamples" I have so far are organizing by space and time. For example, jellyfish organize by drifting near the surface of the ocean, and people organize by sleeping when it's dark.
Once you accept that Networks = Organization, Wheatley and Frieze's assertion becomes somewhat less interesting; however, it does (somehow) lead to the Berkana-esque question: Isn't it odd that the words "organization" and "organic" have the same root? Doubters like myself can verify right here the etymological network connecting "organization" with "organic." The root is the Greek organon, literally "that with which one works," and which since the 12th century has described not only tools but also musical instruments and body parts.
Putting all our quotes and equations together, we have:
"Science is knowing tools, musical instruments, and body parts. Wisdom is living tools, musical instruments, and body parts."And that, dear reader, is an org chart that really counts.
This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License and is copyrighted (c) 2008 by Connective Associates LLC except where otherwise noted.
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