I want to get into playing with data like this Six Degrees of Wikipedia. I'll have to soon with the continuation from the Separating the wheat from the chaff: identifying key elements in the NLA .au domain harvest paper.

Sometimes Crowds Aren't That Wise" by Josh Catone. I downloaded the PDF file in question. No it didn't check the version of Photoshop I'm using. I don't use Photoshop. I use http://gimp.org.

Distributed Reputation Systems for Internet-based Peer-to-Peer Systems and Mobile Ad-Hoc Networks by Jochen Mundinger, Sonja Buchegger and Jean-Yves Le Boudec. Reputation systems are widely and successfully used in centralized scenarios. Will they work equally well, however, in decentralized scenarios such as Internet-based peer-to-peer systems and mobile ad hoc networks?
Jean-Yves Le Boudec Artificial Immune System For Collaborative Spam Filtering
Summary. Artificial immune systems (AIS) use the concepts and algorithms inspired by the
theory of how the human immune system works. This document presents the design and initial
evaluation of a new artificial immune system for collaborative spam filtering1 .
Collaborative spam filtering allows for the detection of not-previously-seen spam content,
by exploiting its bulkiness. Our system uses two novel and possibly advantageous techniques
for collaborative spam filtering. The first novelty is local processing of the signatures cre-
ated from the emails prior to deciding whether and which of the generated signatures will
be exchanged with other collaborating antispam systems. This processing exploits both the
email-content profiles of the users and implicit or explicit feedback from the users, and it uses
customized AIS algorithms. The idea is to enable only good quality and effective information
to be exchanged among collaborating antispam systems. The second novelty is the represen-
tation of the email content, based on a sampling of text strings of a predefined length and at
random positions within the emails, and a use of a custom similarity hashing of these strings.
Compared to the existing signature generation methods, the proposed sampling and hashing
are aimed at achieving a better resistance to spam obfuscation (especially text additions) -
which means better detection of spam, and a better precision in learning spam patterns and
distinguishing them well from normal text - which means lowering the false detection of good
emails.
Initial evaluation of the system shows that it achieves promising detection results under
modest collaboration, and that it is rather resistant under the tested obfuscation. In order to
confirm our understanding of why the system performed well under this initial evaluation,
an additional factorial analysis should be done. Also, evaluation under more sophisticated
spammer models is necessary for a more complete assessment of the system abilities.

In the Basement of the Ivory Tower struck a chord with me. Here's a summary:

No one has drawn up the flowchart and seen that, although more-widespread college admission is a bonanza for the colleges and nice for the students and makes the entire United States of America feel rather pleased with itself, there is one point of irreconcilable conflict in the system, and that is the moment when the adjunct instructor, who by the nature of his job teaches the worst students, must ink the F on that first writing assignment.

...For I, who teach these low-level, must-pass, no-multiple-choice-test classes, am the one who ultimately delivers the news to those unfit for college: that they lack the most-basic skills and have no sense of the volume of work required; that they are in some cases barely literate; that they are so bereft of schemata, so dispossessed of contexts in which to place newly acquired knowledge, that every bit of information simply raises more questions.

(created by SummaryService 1.2.1)

Just watched a Google video featuring Peter Norvig on large data and analysis. Some amazing results. This and lots of interesting IT posts at http://catonmat.net.

The Nature article by Aaron Clauset, Cristopher Moore & M. E. J. Newman (2008) Hierarchical structure and the prediction of missing links in networks suggests that many network structures including communities in social networks could be reorganised into hierarchical structures with missing links predictable in the network.