This document discusses how online communities can strengthen and grow by increasing connections between members. Strong communities are characterized by regular interactions beyond the core purpose, a positive flow of ideas, and connections that transition from weak to strong ties. One way to facilitate this is through a dynamic profile matching system that identifies common interests between members based on their online behaviors, simply displays potential matches, and encourages quick interactions to form new connections. This strengthens the social network and helps the community grow.

1. attraction
stronger online communities grow faster

2. Thesis
communities strengthen and grow
with the number and quality of connections
between their members
inter-connected communities create
stronger social networks

3. Characteristics of
strong communities
• regulars (daily)
• new visitors become new regulars
• interactions between members
beyond core community purpose
• positive flow of opinions, ideas

4. weak and strong ties
* TIES are bidirectional, mutual and
reciprocating
weak = first connection, loose interactions
strong = family, friends, commitments
*lifted from something, citation needed

5. change ties from weak to strong
• match members
based on common interests
as implicitly expressed
by their web behaviors
• identify matches simply, visibly
• encourage and enable quick interactions
creating new connections

6. dynamic profile match
every cell phone is a web server, with the ability to run
web apps anywhere regardless of connectivity.
albert wenger
I ported Node.js to iOS, so now every phone IS a web
server. Imagine--near-field apps, or just publishing your
accelerometer stats
too tall nate

7. profile on hover
@tootallnate
you share interests!
javascript
indy music
july aug sep oct
invite tootallnate to turntable.fm
I ported Node.js to iOS, so now every phone IS a web
server. Imagine--near-field apps, or just publishing your
accelerometer stats

8. dynamic profile match
albert wenger
too tall nate
albert’s web behavior time spent interest
browing, search, sharing, buying
http://www.tulek.org/2010/11/28/node-js-mongodb-mongoose/
http://howtonode.org/websockets-socketio
20% javascript
http://net.tutsplus.com/tutorials/javascript-ajax/10-super-helpful-
traversing-functions-in-jquery/
http://dev.opera.com/articles/view/javascript-best-practices/ 10% organic wines
http://stackoverflow.com/questions/6735250/what-are-jquery-hooks-
and-callbacks elementary
http://stackoverflow.com/questions/3586600/how-to-execute-jquery-
10%
callback-before-data
http://stackoverflow.com/questions/8070894/when-would-i-use-
jquery-callbacks
http://stackoverflow.com/questions/182630/jquery-tips-and-tricks
education
http://stackoverflow.com/questions/178325/how-do-you-test-if-
http://coding.smashingmagazine.com/2011/09/16/useful-node-js-
tools-tutorials-and-resources/
http://www.twilio.com/gallery/languages/javascript
8% indy music
http://jashkenas.github.com/coffee-script/
7% regular tennis
2% caribbean travel

9. customers & method
• developers (API)
• blogs (widget)
• media sites (widget)
• communities (widget)
• companies (widgets)

10. interest metrics
• interest proximity
• distance btw people based on interests
• interest depth
• quality, depth of skill, seriousness, commitment
IP*ID=interest compatibility

11. models?