ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY
PERSPECTIVE
Ying Zhou, Joseph Davis
School of Information Technologie, The University of Sydney, Australia
Keywords: Weblog, community, social network, social tie.
Abstract: In this paper, we report a two level study on weblog link structures. At the micro level, we carried out an in-
depth investigation of individual weblogs. Our goal was to obtain some preliminary understanding of the
different types of links that might indicate underlying communities of bloggers. Complete and detailed link
data was collected from eight weblogs followed by a variety of analyses. The result shows that both
incoming and outgoings follow Zipf like distribution in terms of the sources of those links. These suggest
clustering patterns (communities) within the whole blogspace. We also examine the temporal aspects of
weblogs. The average life span of a weblog entry is fairly long in most of our sample cases. In addition,
analysis on individual comment authors shows that in average, active comment authors maintain a rather
long relationship with a certain weblog. It provides evidence that historical data may be useful in
understanding weblog communities. On a larger scale, we developed a program to collect complete link
data from large number of interconnected weblogs and performed cluster analysis on it. Communities with
common topics are successfully extracted using those link data.
1 INTRODUCTION
Weblogs are web pages with several dated entries
usually arranged in reverse chronological order
(Kuma et al. 2004). This new form of online diary
has become an influential web application, with
thousands of blogs added on the web everyday. In
general, blog sites cover a wide range of topics. Sites
devoted to politics or technology-related topics
usually receive thousands of hits per day.
Blogs usually contain a large number of links to
other pages (Barabasi et al. 2001). This could be
links to regular webpages or links to other blog
entries. Each entry of a blog has its own “permalink”
(permanent link). It could be an individual webpage
or a section in a webpage. Blogs with similar topic
are usually interwoven into a network of
communities referred to as blogspace (Searls & Sifry
2003). Within a blogspace, bloggers list blogs they
read and other links on the sidebar, sometimes
referred to as blogrolls. They comment on each
other’s postings, generating periods of bursty
activities around interesting topics. Such bursty
activities are usually exemplified by heavy linkage
amongst the blogs involved with in a time interval
(Kumar et al. 2003). Blogs have evolved into both
link magnets and sources of links on Internet. The
result, as illustrated by Searls and Sifry (2003), is
both striking and to be expected, They state “name a
topic with a community of interest around it. Now
go to Google and look it up. There is a good chance
one or more of the top results will include
somebody’s weblog (aka blog)”. The example given
by them are: 802.11b, Segway and webblog. For
each search term, Google listed a weblog among the
top three results, which still holds at the time of
writing of this paper. There are also reports that
people prefer to have the latest news or development
trends through blogs rather than through traditional
media.
Blogspace represents a new form of online
community as well as a new form of online
knowledge repository connected by hyperlinks. Yet
it is not an easy task for a newcomer to discover the
virtual communities if they are interested in tracking
particular community discussions. Google recently
released a beta version of blog search services
(http://blogsearch.google.com/). It indexes a huge
collection of blog and news feeds. This search
service is based on the feed data. Most query results
point to individual entry of a weblog, with a few
highlighted matched entire weblogs appear on the
top. Beside the latest Google blog search, a few
specialized blog/news search engine has also
13
Zhou Y. and Davis J. (2006).
ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE.
In Proceedings of WEBIST 2006 - Second International Conference on Web Information Systems and Technologies - Society, e-Business and
e-Government / e-Learning, pages 13-20
DOI: 10.5220/0001248200130020
Copyright
c
SciTePress
emerged, including bloglines.com, daypop.com and
blogdex.com. Each has its own copy of a collection
of weblogs and can perform content related search
for weblogs and news. Most of them are focused on
page-level search rather than community oriented
search. Hence the results are usually a mixture of
regular news pages and some blog pages based on
conventional static page rank algorithm. Weblogs,
compared the regular webpages are more dynamic
and evolves very quickly. Besies, People typically
search search weblogs for a different purpose. Most
of the time, they are trying to find a weblog that they
can keep reading regularly. A desirable weblog
search and ranking service requires different
methods to model and organize the whole blog
space. Understanding of individual blogs as well as
the structure of linkage interaction among weblogs is
important in the design of a weblog search service.
We carried out a two level study on weblog
search. We first conduct a case study on a number of
weblogs and investigated the feature of link
component within each of them. This case study
tries addresses the following questions: what kind of
data should we collect to study the community of
weblogs? what is the average life span of a weblog
entry? what are the general interaction patterns of a
weblog with other weblogs? Based on the findings
of the case study we conducted a larger scale
experiment to extract weblog communities by
exploiting their link structure.
2 RELATED WORKS
At a macro scale there are Gruhl et al (2004)’s
information diffusion study and Kumar et. Al
(2003)’s bursty evolution study. Both studies draw
on a large collection of weblogs over time. Gruhl
et.al (2004) utilizes the epidemic model of disease-
propagation to investigate the dynamics of
information propagation through networks of
bloggers. Their work can help to identify the hottest
topics and predict the diffusion of certain piece of
information in the community of bloggers. Kumar
et. al adopted the notion of time graph and use that
as a basis to extract temporal communities and
studied bursty behavior. They argued that blog
communities have striking temporal characteristis; in
particular, communities only formed when an
interesting topic arose and it faded away after a
certain period.
On a micro scale, Judit Bar-llan (2004)
monitored 15 sample weblogs for 2 months and
generated statistics related to the blogs and posts
such as, average postings per day, number of posting
day in a period of time, average links per post and so
on. The majority of her sample weblogs are
technology or research oriented. Her results show
that the topics of most postings in the blogs were
typically closely related to the declared topics. Since
the data collected by Bar-llan were over a two-
month period, it cannot give a complete picture of
long running blogs and their evolution.
3 BLOG COMMUNITIES
We define a blog community as a network of
weblogs with similar topics connected through
hyperlinks. Within the community, bloggers may
read, write and comment on other bloggers’ entries.
These are reflected by the three types of links
indicating certain interactions between two weblogs.
They are candidate links we need to consider for
discovering and constructing the blogspace.
Many bloggers list a few other blogs they read
on the siderbar. The list is called a blogroll. The
blogroll links indicate a read relationship between
this blogger and others.
It is very common for a blogger to cite or
reference another blogger’s writing on his/her own
post. This indicates a response relationship between
the two bloggers.
Bloggers may respond to one another’s writing
through other channels. They may leave a comment
on the posting site. Sometimes, a commenter
identifies self by a link to his or her own weblog in
the comment body. This enables a crawler to build
the connection between the two weblogs. In
addition, some blog authoring tools provide
“trackback” feature. This is a protocol that a
responder can use to notify the author that he(she)
has cited a particular article of the author in his(her)
own blog. The notification is achieved by sending a
ping message to the original blog entry, which will
then update its trackback list to include the senders’s
URL. Trackback links make it very easy to get all
incoming links of a particular blog entry. However,
this feature is not used extensively at present.
4 METHODOLOGY
In this study, we first take a micro scale approach
and closely investigated links in a few individual
weblogs and the three different types of associations
between blogs. The micro level is more appropriate
here since we are not going to study the general
structure of the whole blogspace. Rather, we are
more interested in the individual weblogs, and their
communication pattern with other weblogs.
WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT
14
The sample we traced consists of eight Weblogs.
The main criterion we use to choose the sample
cases is that the weblogs should have been running
actively for some time to make sure there are enough
data. In addition, the sample weblog should have
extensive comments to ensure the activeness of the
interactions. The cases chosen include two
cooperate weblogs, four pure technology weblogs
and two weblogs on personal opinions. These are:
1. MSN Search's Weblog (http:// blogs.msdn.
com/msnsearch/): an official weblog on MSN's
search related products and discussion since
November 2004.
2. Yahoo! search weblog (http://ysearchblog.
com): a weblog written by Yahoo! staff on
Yahoo! search product since August 2004.
3. Fabulous Adventures In Coding (http://
blogs.msdn.com/ericlippert/): a weblog written
by Eric Lippert discussing all sorts of coding
issues, .NET technology and a few other
things. It is started on September, 2003.
4. Sorting it all out (http://blogs.msdn.com/
michkap/): a weblog written by Michael Kaplan
mainly about locales, keyboards, Unicode and
other language related techniques. It is started
on November 2004.
5. Micro persuasion (
http://www.micropersuasion.
com/): a weblog written by Steve Rubel on “how
new technologies are transforming marketing,
media and public relations”. The weblog started
since since April 2004. There are many articles
regarding blogging news, practices and systems
in micropersuasion.
6. Schneier on Security
(http://www.schneier.
com/blog/) : Bruce Schneier’s weblog on
security and security technology since October,
2004.
7. BuzzMachine(http://www.buzzmachine.com/):
started since July 2005, buzzmachine is a
weblog written by Jeff Jarvis, currently the
president and creative director of Advance.net.
Jeff Jarvis is a high profile media people and
long time supporter of weblogs. Buzzmachine
has a wide coverage on many topics including
weblogs, newspapers, open source and politics.
8. Hot Points with BOB Parsons (http://
www.bobparsons.com/): a weblog written by
Bob Parsons, the president and founder of
GoDaddy.com, a company that provide Internet
domain name registration, web hosting, email
accounting and lots of other Internet related
services. The weblog contains his thoughts and
opinions on Internet innovations and lots of
other things. It is started on December 2004
All the chosen weblogs have different layouts. A
few specialized web crawlers were developed to
extract and collect the required information from
them. The customized crawlers can accurately
extract specified information, which is different
from the simple heuristic based crawling algorithm
used in Kumar et al. (2003) study. Information
collected include blogroll links, links appeared in
each entry, and comments made on each entry. Each
entry's publishing date and time as well each
comment's publishing date and time are also
collected. The links referenced in entries are placed
in two broad categories: blog link and other link.
Blog link points to another webblog or webblog
entry. We use the algorithm proposed by Ceglowski
(2003) to judge if a link point to a blog entry. The
data collection was carried out in October 2005. All
data were current up to that month.
5 RESULTS
5.1 Basic Demographic Information
Table 1 gives a summary of the basic information
based on the target weblogs. For all weblogs except
Schneier, we collected all data from the very first
posting. The number of occurrences for each type of
interactions is given to provide an overall picture of
the intensity of the communication coming in and
going out of a particular weblog. All of our target
weblogs have significant levels of communication
with other sites or weblogs. They referenced many
web pages and also attracted lots of discussions on
their own sites.
Table 1: Basic demographic information.
Blogroll
Link
No. of
Entries
Entry Link Comments
MSN Search 9 77 468
( 6)**
1545
(20)
YSearchblog 21 55 320
(5.9)
634
(11.7)
MichKap 0 887 4398
(4.9)
4272
(4.8)
EricLippert 12 605 889
(1.5)
4995
(8.3)
Micropersuasio
n
0 2548 7252
(2.8)
4672
(1.8)
Schneier* 0 86 200
(2.3)
3354
(39)
buzzmachine 1 580 4211
(7.3)
8485
(14.6)
BobParsons 0 68 197
(2.9)
5924
(87)
* Only 3 months data of this weblog is collected
** average number
ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE
15
5.2 Tie Strength Distribution
The blog community can be viewed as a special type
of social network. A social network is a set of people
or groups connected with each other under a
particular relationship (Wasserman & Faust 1994).
Examples of typical social networks include the
friendship network of high school students, email
network of employees, and scientific co-authorship
network of academics (c.f. Barabasi et al. 2001,
Newman 2001). In social network terminology, the
people or groups are called "actors" and the
connections are called "ties". In a blog community
context, the bloggers who write weblogs are the
actors and the hyperlinks between weblogs are the
ties between two bloggers. If we consider each link
as a communication instance between two bloggers,
we can measure the strength of the tie according to
the number of links between two particular weblogs.
In this section, we will use links appearing in blog
entries and links created by comment authors to
study the distribution of tie strength for each
particular weblog.
Figure 1 shows the tie strength distribution for
the outgoing links found in weblog entry. The x-axis
represents the number of times a weblog has been
referenced (strength score of tie) and the y-axis
represents the number of weblogs that have been
references for a particular number of times (number
of ties with a certain strength score). For instance, in
Michkap weblog, we have 15 other weblogs being
referenced by Michkap once (15 weak ties), 1
weblogs being referenced by MichKap 10 times (1
strong tie with strength score 10) and so on.
Figure 1: Outgoing tie strength distribution.
All links appearing in the blog entry of blog type
are extracted. Those links usually point to a blog
entry of another weblog. To construct the tie
between two weblogs, we need to find weblog home
page URL of those links. This is achieved through
the RSS or ATOM feed of a weblog. The <link>
value of the <channel> element in RSS usually gives
the homepage of a weblog. By replacing the actual
links with its home page URL we can easily count
the number of links between two weblogs and
discover how many strong and weak ties exist for a
weblog.
Five weblogs are included in the diagram. The
rest do not have enough links pointing to other blog
entries. The chart on the bottom left panel contains
data for two weblogs. The data from all five weblogs
follows Zipf-like distribution. From the social
network point of view, the observation can be
interpreted as a weblog has many weak ties (being
referred once or twice), and only a few strong ties
(being referred many times) to other weblogs.
Similar procedures are performed on the
incoming ties reconstructed from the comment
section of each weblog entries. Each unique
comment author is considered as an agent in a social
network and one comment is considered as a
communication instance between the comment
author (usually a blogger) and the blogger. We use
the number of comments left by the same author to
measure the strength of the tie between comment
author and blogger. Figure 2 shows the tie strength
distribution for the incoming ties. The x-axis
represents the number of comments a reader left
(strength score of tie) and the y-axis represents the
number of readers that have left a particular number
of comments (number of ties with a certain strength
score). All weblogs show clear Zipf-like
distributions. All weblogs have large number of
occasional visitors who only made one or two
comments and a few frequent visitors who made lots
of comments.
The Zipf like distribution of the tie strength is
consistent with Granovetter (1983) view of social
world. In his description, social world is structured
with highly connected clusters (strong ties) with
many external weak tie connecting these clusters.
The observation implies the existence of clusters
consisting of weblogs with strong ties to each other
in the blogspace. These clusters are actually weblog
communities of interest. It also indicates that we can
discover these blog communities by measuring the
link strength between weblogs.
WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT
16
5.3 Blog Entry Life Span
The study of weblog community would involve both
spatial and temporal dimensions. In the previous
section, we investigate the dynamics of weblog
interactions and discovered that around each weblog,
there is a small circle with highly frequent
interaction. These would form the basis for closely
knit weblog communities. Yet the questions
regarding how stable these small circles might be
along a weblog's life span and how frequently those
communities might evolve remain unanswered.
Weblogs, nicknamed as online diary, share many
features of news sites. They both focus on current
events, be it a social, political event or a technical
problem recently raised. They are both updated
frequently and have some established readership
base. This raises the problem of the value of back
issues. If we are going to study the community
around a particular topic, how much historical data
do we need to collect to ensure the accuracy of our
community information.
To develop a better understanding of the
problem, we used the publishing date and time
information recorded on each blog entry and
comment to get some preliminary idea of the
average life span of a blog entry. We used the latest
time a comment was made by somebody other than
the author and the blog posting time to estimate the
average life span of a blog entry. The life spans of
entries vary significantly with most posts having a
one day life span and a few having life spans as high
as nearly two years. For instance, one entry in Eric
Lipert’s blog (http://blogs.msdn.com/ericlippert/
rchive/2003/10/06/53150.aspx ) originally posted in
November, 2003 achieved some recent discussion
around two years later. We discovered quite a few
posts in Eric’s weblog with more than 600 days’ of
life span. Table 2 give the descriptive statistics of
entry life span for each weblog in the sample.
Although varying enormousely in terms of average
life span, a consistent message coming through all
those cases is that historical entries are not
completely ignored by blog readers. This suggests
that in analyzing weblog communities, we do have
to include some historical data.
Table 2: Weblog entry life span.
Unit: day Mean S.D Range
MSN Search 31.4 50.1 210 (330)*
YSearchblog 60.3 86.5 343 (420)
MichKap 25.7 54.1 282 (330)
EricLippert 75.0 168.9 704 (760)
Micropersuasion 6.0 36.7 482 (550)
Schneier 7.8 9.3 47
buzzmachine 7.2 13.3 87 (110)
BobParsons 84.9 71.6 295(300)
* Weblog running days to October, 2005
5.4 Temporal Aspects
The concept of time graph and temporal community
introduced in (Kumar et al. 2003) suggests certain
bursty patterns along a weblog’s life time. These
may be generated by totally different groups of
people or the same group of people. On the one
hand, if bursts are generated by different groups, a
natural conclusion would be that a weblog may be in
Figure 2: Incoming tie strength distribution.
ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE
17
different communities from time to time. In that
case, it would be difficult to measure the stability of
a community and predict any future interaction
among different weblogs. On the other hand, if
most bursts are generated by relatively stable groups
of members, this suggests that communities around a
weblogs are fairly stable and it is reasonable to use
historical data to predict future trends.
We took a few weblogs in our sample with
relatively long life time and extract the most active
five comment authors along with all instances of
their communications. The purpose was to see if
there was clustering pattern along the time line, that
is, comment authors tend to comment with in a short
duration and may never come back. Figure 3 shows
the monthly communication intensity distribution of
the top five comment authors in MichKap,
EricLippert and Micropersuasion weblogs. The size
of the area is proportional to the number of
communications in that month. We do see peaks
from time to time which support findings in (Kumar
et al. 2003). In Figure 3, different colors represent
different authors. The numbers in the legend indicate
the total number of communications made by that
author. A consistent pattern is that majority of
authors interact with the target weblog for a
relatively long period of time. Except for a few
cases, many authors maintain the commenting
relationship for more than one year. However, some
may be more active in the early stage, for instance,
Dan Shappire in EricLippert Weblog. Others may be
more active in late stage, for instance, Nicolas Allan
in EricLippert Weblog. Both covered a period of
more than one-and-a-half year. This suggests that
the communities of readership for most weblogs in
our sample are quite stable for a certain period of
time.
6 MACRO-SCALE APPLICATION
The above case study indicates the existence of
community of webloggers and the types of link data
that need to be collected to discover the weblog
communities. To test those findings on a much
larger scale, a web crawler was developed to collect
link data from large collection of weblogs.
Clustering is then applied on the data to identify the
community structure.
The weblog crawler takes a weblog URL as seed
and incrementally adds linked pages in the
collection. Table 1 illustrates the main crawling
algorithm. This weblog crawler can extract complete
set of links from a weblog. This includes links on
sidebar, links in blog entry and links in comment
section. If any of those links points to another
weblog, a complete link set from that weblog will be
extracted as well. The crawling depth is controlled
by the maxDepth variable and is currently set to 6 to
reflect the “six degrees of separation” rule (Barabasi
2002).
Internet was reported to have 19 degree of
separation between any individual webpages
(Barabasi 2002). Since our unit of analysis is the
weblog and not the individual page, and each
MichKap
0
50
100
150
200
250
N
o
v-04
J
a
n-05
Mar-0
5
May
-
05
Jul-05
Se
p-
0
5
M aurit s (221): ht t p :/ / www.g eo cit ies.co m/ mvaneerde/
Dean Harding(109):http://www.codeka.com/blogs/
Mihai(93)
Norman Diamond (49)
Shaun B ed ingf ield ( 4 8 )
EricLippert
0
10
20
30
40
50
60
Aug-03
No
v
-
0
3
F
eb-04
M
ay
-
04
A
u
g
-
0
4
N
ov-04
Feb-05
M
ay
-0
5
A
u
g
-
0
5
Mike(48):http://w w w .mikepope.com/blog
Peter Torr(108):http://blogs.gotsotnet.com/pto
r
Norman Diamond(53)
Dan Shappir(193)
Nic holas A llen
(
146
)
Micropersuasion
0
10
20
30
40
50
60
Apr-04
Jun-04
Aug-04
Oct-04
Dec-04
Feb-05
Apr-05
Jun-05
Aug-05
Oct-05
Robert Scoble(45):http://scoble.w eblogs.com
david parmet(50):http://w w w .parmet.net/pr/
Tom(70):http://w w w .themediadrop.com/
Randy Charles Morin(117)
Jeremy Pepper (135):http://pop-pr.blogspot.co
m
Figure 3: Communication Strength Distribution.
WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT
18
weblog represents a person who writes it, we think it
is more appropriate to follow the six degrees of
separation concept in our research.
The crawling result is a collection of relational
records with two fields, the source weblog url and
the target weblog url. Each record indicates a link
from source to target, with a value of 1.
Table 3: Web crawler algorithm.
1 Url = seed url;
2 depth = 0;
3 method crawl (Url, depth)
4 if (depth < maxDepth)
5 for all hyper links link in Url
6 if link belongs to the same weblog
7 crawl (link, depth)
8 else if link is a weblog post
9 find the home page of link as link.home
10 add a record Url.home and link.home
11 crawl(link.home, depth + 1)
12 end if
13 end for
14 end if
15 end method
Figure 4: Web Services blog communities.
We take Savas Parastadist’s weblog
(Savas.parastatidist.name) as seed in the experiment
to run the web crawler. The key theme of Savas
Parastadist’s weblog is web services standards and
products. The result contains around 3800 unique
weblogs and over 33000 links. Multiple links
between two weblogs are removed by summing up
the number of links and use it as the link value
between two weblogs. We then use Pajek
(http://vlado.fmf.uni-lj.si/pub/networks/pajek/) to
perform clustering on the collection. The clustering
algorithm tries to discover clusters within a large
graph so that each node in a cluster should have
more communication with other nodes inside the
cluster than with nodes outside the cluster.
Figure 4 gives a visual display of the
communities extracted from the data and the
structure within each cluster. In total, 15
communities are identified from the data.
We inspected members in those communities.
Table 4 give a list of members in the community
that contains the original seed. The topics of those
member weblogs include .NET, XML and general
web services. Some communities are more focused
on general web services while others focused on
certain web services-related technologies such as
XML and .NET. The result shows that it is possible
to identify communities based on complete link data
of weblogs.
However, we can collect more recent data to
build communities based on clustering technique as
well. From the result on individual comment author,
not all comment authors are active throughout the
life span of a weblog. Some may be more active in
the early stage, while others may be more active in
late stages. The clustering algorithm can be applied
on feed data rather than on entire weblog pages (as
what Google BlogSearch does). A sliding time
window can be used to update the blogspace with
new feeds and to remove obsolete data. However,
we believe that there will not be a standard window
size that can fit all sorts of welbogs. Different
weblogs usually have very different average life
spans.
7 CONCLUSIONS
This research examined eight weblogs as special
cases to study the link structure within weblogs.
Two different types of links were examined: links
embedded on blog entry and links created by
comment authors. The result shows that most
weblogs cited a wide range of other weblogs with
approximate Zipf-like distributions. Many weblogs
have large number of readers commenting on their
writings. The distribution of commenting
communication intensity also follows Zipf like
curve. Majority of the readers left one or two
comments on the average with a small number of
readers left large number of comments. These Zipf
like distributions observed with respect to entry links
and comment authors suggest clustering patterns
(communities) within the whole blogspace. We also
examine the temporal features of weblogs. The
average life span of a weblog entry is fairly long in
most our sample cases. In addition, analysis of
ANALYSIS OF WEBLOG LINK STRUCTURE – A COMMUNITY PERSPECTIVE
19
individual comment authors shows that in average,
some active comment authors maintain a rather long
relationship with a certain weblog. This suggests
that historical data may be useful in understanding
weblog communities. To test the above findings, we
developed a program to collect complete link data
from large number of interconnected weblogs and
performed clustering analysis on it. Communities
with common topics are successfully extracted from
these link data.
Table 4: Community member list.
1 http://savas.parastatidis.name
2 http://research.microsoft.com/news/msrnews/
3 http://pluralsight.com/blogs/dbox/
4 http://pluralsight.com/blogs/craig/
5 http://pluralsight.com/blogs/tewald/
6 http://pluralsight.com/blogs/aaron/ ***
7 http://pluralsight.com/blogs/keith/
8 http://pluralsight.com/blogs/fritz/
9 http://pluralsight.com/blogs/mgudgin/
10 http://msdn.microsoft.com/msdnmag/
11 http://unboxedsolutions.com/sean/
12 http://glazkov.com/blog/
13 http://devauthority.com/blogs/csteen *
14 http://weblogs.asp.net/ericjsmith/
15 http://blogs.msdn.com/smguest
16 http://samgentile.com/blog/
17 http://weblogs.asp.net/rhurlbut/
18 http://jcooney.net/
19 http://blogs.msdn.com/yassers
20 http://weblogs.asp.net/cweyer/
21 http://www.innoq.com/blog/st/
22 http://www.jonfancey.com/
23 http://codebetter.com/blogs/jeffrey.palermo
24 http://blog.whatfettle.com/
25 http://blogs.msdn.com/brada
26 http://blogs.msdn.com/mpowell
27 http://weblogs.asp.net/jgaylord/
28 http://blogs.msdn.com/robcaron **
29 http://weblogs.asp.net/despos/
30 http://www.theserverside.net
31 http://blogs.msdn.com/mfussell
32 http://weblogs.asp.net/mnissen/
33 http://blogs.msdn.com/trobbins
34 http://blogs.msdn.com/tomholl
35 http://weblogs.asp.net/wallym/
36 http://dotnetjunkies.com/WebLog/barblog
REFERENCES
Ada, E. & Zhang, L (2004) Implicit Structure and the
Dynamics of Blogspace. In Workshop on the
Weblogging Ecosystem, WWW2004, New York City.
Adamic, A.L and Huberman, B.A. (2002), Zipf’s law and
the Internet. Glottometrics 3, 143-250
Barabasi, A.L., Jeong, H., Neda, Z., Ravasz E., Schubert
A., & Vicsek T. (2001, April), Evolution of the social
network of scientific collaborations. arXiv:cond-
mat/0104162 v1. 10.
Barabasi A.L. (2002) Linked: the new science of
networks. Perseus Books Group
Bar-llan Judit. An ousider’s view on “topic-oriented”
blogging. WWW2004, May 2004, New York, USA
M.Ceglowski. (2003) Www::identify-identify blogging
tools based on url and content. Retrieved from
http://search.cpan.org/~mceglows/WWW-Blog-
Identify-0.06/Identify.pm,
Downes. S. (2003 July/August) Web logs at Harvard Law.
The Technology source. Retrieved from
http://ts.mivu.org/default.asp?show=article&id=2019
Flake G.W., Lawrence S, & Giles C.L. (2000) Efficient
identification of web communities. In Proc. 6
th
ACM
SIGKDD Intel. Conf. On Knowledge Discovery and
Data Mining, page 150-160
Granovetter, M. (1983) The Strength of weak ties:a
network theory revisited. Sociological Theory, Vol. 1,
201-233
Gruhl, D., Guha, R., Liben-Novell D. & Tomkins A.
(2004 May) Information diffusion through blogspace.
WWW2004, , New York, USA
Kumar, R. Novak, J., Raghavan, P. & Tomkins, A. On the
bursty evolution of blogspace. (2003 May)
WWW2003, Budapest, Hungary
Kumar, R., Novak J., Raghavan, P., & Tomkins, A. (2004
Dec) Structure and evolution of blogspace,
Communications of the ACM, Vol.47/No.12
Nanno, T., Fujiki, T., Suzuki, Y. & Okumura, M. (2004
May) Automatically collecting, mornitoring and
mining Japanese Weblogs. WWW2004, New York
City
Newman, M.E.J. (2001) Scientific collaboration networks.
I. Network construction and fundamental results,
Physical Review Vol. 64, 016131,
Rubel, S. (2004, Oct) Finding Influential Blogs That
Reach Your Key Audiences, Retrieved from
http://www.micropersuasion.com/2004/10/finding_infl
uen.html
Searls, D. & Sifry, D. (2003 Mar), Building with Blogs,
Linux Journal, Issue 107
Tedeschi, B. (2005 Jul. 4) Blogging while browsing, but
not buying”, The New York Times.
Wasserman, S. & Faust, K. (1994) Social Network
Analysis, Cambridge University Press, Cambridge
Winer, D. (2002 May) History of weblogs, Retrived from
http://newhome.weblogs.com/historyOfWeblogs.
WEBIST 2006 - SOCIETY, E-BUSINESS AND E-GOVERNMENT
20
