Weblogs (or blogs
are more commonly known) constitute a fascinating artifact within the evolving
web. Early hand-edited collections of blogs consisted of any page containing
sequences of dated entries. Nowadays, most people think of blogs as pages with
reverse chronological sequences of dated entries, usually containing a
persistent sidebar containing profile information (and often other blogs read by
the author) and usually maintained and published by one of the common variants
of public-domain blog software. They tend to be quirky, highly personal, often
consumed by regular repeat visitors and highly interwoven into a network of
small but active micro-communities. In short, blogs are perhaps the most
significant recent movement in end-user content creation on the web. We refer to
the collection of blogs with their links as Blogspace
are at least two important reasons for the systematic study of Blogspace:
- Sociological reasons: Blogspace differs from
traditional web pages structurally because blogs represent concatenations of
messages, as within newsgroups and bulletin boards, but authored by a single
individual. However the more significant differences are more than structural:
the culture of Blogspace focuses heavily on local community interactions
between a small number (say, between 3 and 20) of bloggers. Members of the
informal community might list one another's blogs in a ``blogroll'' and might
read, link to, and respond to content on other community members' blogs.
Often, these sequences of responses take place during a brief burst of heavy
activity as an interesting topic arises, jumps to prominence, and then
recedes. Naturally, this leads to the question: can we experimentally observe
and model this highly dynamic, temporal community structure?
- Technical reasons: Traditional studies of the web
and the web graph make use of a static snapshot derived from a crawl. All such
work raises the natural question: what happens over time? A number of works
have begun to address this question through creation and analysis of a series
of snapshots of the data [
4 , 15
]. The development of tools and methods to analyze these snapshots
is therefore a timely endeavor. However, Blogspace offers an additional
technical advantage over such approaches--if data is recrawled with a certain
frequency, there is no notion of the precise point in time a page or link was
created/updated. In contrast, Blogspace offers us a ready-made view of
evolution in continuous time: as each blog adds
an entry (together with links), there is a time stamp associated with that
event. By automatically extracting these time stamps we can piece together a
view of Blogspace evolving continuously from the beginning of blog archiving
to the present. We should stress that time is absolute (not merely relative as
in a sequence of crawls). Our work focuses on connectivity evolution and on
temporally concentrated bursts (in the sense of
11 ]) in this evolution of Blogspace.
- We introduce a combinatorial object we call a time
) for the study of graphs that evolve in continuous time. We build the
blog graph--the time graph for Blogspace--by
automatically extracting dates from blog page entries (
Section 4 ).
- We define a notion of communities in Blogspace and extend Kleinberg's
notion of temporal bursts in a sequence of documents [
11 ] to sets of blogs and the links between them, developing a
notion of bursty communities of blogs that are topically
and temporally focused (
Section 3 ).
- We conduct a series of experiments that develop properties and views
of the graph induced by Blogspace as a function of time, showing the
development of macroscopic and microscopic community structure, and the
evolution of burstiness (Section
- We show that Blogspace underwent a transition behavior around the end
of 2001, and has been rapidly expanding over the past year, not just in
metrics of scale, but also in metrics of community structure and
connectedness. This expansion shows no sign of abating, although measures of
connectedness must plateau within two years (
Section 5 ).
In this section we first provide
some background material on graphs, communities, and burst analysis of events
). Next we review the world of blogs and argue that blog
communities and web communities are different (Section
A directed graphG
= ( V
of a set V
and a set E
each edge is an ordered pair of nodes. The in-degree
of a node u
is the number of nodes v
; the out-degree
number of nodes v
. There is a directed path
if there is a sequence of nodes
strongly connected component
(SCC) of G
is a subset of nodes
such that for any ordered pair of nodes in the subset, there is a directed path
from the former to the latter. An undirected graph G
of a set V
of nodes and a set E
of edges, where each edge is an
unordered pair of nodes.
The notion of communities in the web graph (called web
) was defined in [13
] and the problem of extracting web communities was studied in [
]. Kumar et al. [13
] detected communities by enumerating all bipartite cliques (up to a certain
size) in the web graph. This approach was motivated by the co-citation
phenomenon rampant on the web [10
]. Their hypothesis was that any topically focused community on the web is
likely to contain a dense bipartite subgraph (the signature
almost every occurrence of the signature corresponds to a web community. Flake
et al. [9
] adopted a more sophisticated definition of a web community based on network
describes our approach to the community extraction problem for
We provide a brief review of
Kleinberg's recent work on identifying bursts in a stream of events [11
]. An event might correspond (for instance) to the appearance of an email
containing particular keywords (such as ``NSF grant''--a running example in [11
]). The crucial step is to model such bursts so that they can be identified
efficiently. Care must be taken to avoid identifying a large number of short
spurious bursts or fragmenting long bursts into many smaller bursts. Kleinberg's
approach is to model the generation of events by an automaton that is in one of
two states, ``low'' and ``high.'' The time gaps between consecutive events are
distributed independently according to an exponential distribution whose
parameter depends on the current state. Thus the high state is hypothesized as
generating bursts of events. There is a cost associated with any state
transition to discourage short bursts. Given an event stream we seek to find a
low cost state sequence that is likely to generate that stream. Finding an
optimal solution to this problem can be accomplished by dynamic programming. One
final extension is required. Consider the case in which each event in a sequence
is either relevant
. Kleinberg extends his basic
two-state model to this case as well. This augmented model generates events with
a particular mix of relevant and irrelevant events according to a binomial
distribution. A sequence of events is considered bursty if the fraction of
relevant events alternates between periods in which it is large and long periods
in which is small. Kleinberg defines a measure of weight associated with each
such burst and solves the problem of enumerating all the bursts by order of
According to slashdot
are ``... a new, personal, and determinedly non-hostile evolution of the
electric community. They are also the freshest example of how people use the Net
to make their own, radically different new media.'' Historically blogs date back
to 1996, but they exploded into popularity during 1999 with the emergence of blogger
and other easy-to-use publishing
tools. During 2000, they caught the public eye and articles began to appear in
e-zines and forward-looking publications. Most recently in 2002, a Newsweek article
estimating the number of weblogs to be half a million, and discussing the
emerging culture of blogspace:
While weblogs had always included a mix of links, commentary, and
personal notes, in the post-Blogger explosion increasing numbers of weblogs
eschewed this focus on the web-at-large in favor of a sort of short-form
journal. These blogs, often updated several times a day, were instead a record
of the blogger's thoughts: something noticed on the way to work, notes about
the weekend, a quick reflection on some subject or another. Links took the
reader to the site of another blogger with whom the first was having a public
conversation or had met the previous evening, or to the site of a band he had
seen the night before. Full-blown conversations were carried on between three
or five blogs, each referencing the other in their agreement or rebuttal of
the other's positions.
It is precisely this type of intense
interaction that is at the core of Blogspace and that we wish to analyze
algorithmically. For example, there is a ``
'' once a year in which people blog for 24 hours straight for
charity. Sponsors donate money and then during the blogathon, bloggers update
their blogs every 30 minutes for an entire day.
2.2.1. Bursty communities of blogs
blush, blog communities appear similar to web communities studied in earlier
]. But there is a distinctly different flavor to blog communities, both
qualitatively and (as we develop in subsequent sections) quantitatively: these
communities exhibit striking temporal characteristics. Within a community of
interacting bloggers, a given topic may become the subject of intense debate for
a period of time, then fade away. These bursts of activity are typified by
heightened hyperlinking amongst the blogs involved-- within a time
. Thus it no longer suffices (as in [
]) to extract subgraphs that are signatures of communities; rather, we must
extract such signatures while simultaneously identifying a time interval within
which this hyperlinking is concentrated. Note that a subgraph indicative of a
community of interest (in the traditional sense) may exist amongst a set of
blogs, without ever achieving this temporal focus. Conversely, heavy linkage
within a short period may appear less significant when viewed over a long time
span--suggesting that the criterion for inferring that a pattern of links is a
community be less stringent than for a static graph. Identifying such temporal
bursts is inspired by Kleinberg's recent work that was outlined in Section
. In Section
we extend Kleinberg's work to envelop sets
of blogs inducing a
bursty community through temporal bursts of hyperlinking. While deferring this
formal development and experimentation to Section
, we now give two examples of the bursty phenomena unearthed by our
In a community of blog poets, a burst occurs when one member Firda posts a series of daily poems
about other bloggers in the community and includes links to their blogs. This
burst occurs from March-April of 2000--for example http://www.wannabegirl.org/2000_03_01_log-archive.php
from March 2000 contains poems and links to http://trenchant.org/webloglog , http://www.premiumpolar.com/blog ,
and http://www.swallowingtacks.com/ ,
all members of the community.
In another community (this community may not be suitable for all
ages), a blogger Dawn hosts a poll
to determine the funniest and sexiest blogger. She conducts interviews with
other bloggers in the community, of course listing their sites (see http://up_yours.blogspot.com/2002_05_19_up_yours_archive.html
). She then becomes obsessed with one of the other bloggers Jim, which spurs
comments by many others in the community (see http://jimspot.blogspot.com/2002_07_28_jimspot_archive.html
In this section we first define the
notion of time graphs which will be be the basis for studying Blogspace. Time
graphs can also be used to study different evolving graphs such as the web,
e-mail graphs, call graphs, newsgroup graphs and so on. We anticipate that they
will prove to be of considerable independent interest for many other
mathematical and algorithmic studies. Next we focus on tracking bursty
communities on the time graph induced by blogs, henceforth the blog
. We accomplish this by adopting a two-step approach:
- Community extraction (
Section 3.2 ): We extract dense subgraphs from the blog graph;
these correspond to all potential communities (whether or not bursty).
- Burst analysis (
Section 3.3 ): Building on the work of
11 ] on bursts in event streams, we perform a burst analysis of
each subgraph obtained in step 1 to identify and rank bursts in these
The reason for this two-step approach is that the
problem we wish to solve is somewhat harder than that addressed by Kleinberg.
Whereas the elementary events he considers have simple, local characterizations
(e.g., does an email contain a given keyword?), our setting does not afford such
locality. A bursty community is not characterizable in terms of a single blog or
edge in the time graph. Rather, it entails an analysis of the entire blog graph.
Ideally, we must simultaneously identify subsets of blogs as communities
together with bursts in the events relevant to this subset. We instead break
this down into our two-step sequence; avoiding this two-step process remains a
challenging open problem.
3.1. Time graphs
We now introduce what appears to be a novel
combinatorial object: the time graph. A time graphG
) consists of
- A set V of nodes where each node
has an associated interval D
(v) on the time axis (called the
duration of v
- A set E of edges where each
is a triple (u,
u and v are nodes in
V and t is a point
in time in the interval .
A node v
said to be alive
at time t
. The interpretation is that each edge is created at a
point in time at which its two end-points are alive. The definition naturally
allows for directed time graphs. Note that in contrast to the well-established
algorithmic study of dynamic graphs (see, for instance, [
]), edge events have real-valued time stamps. Let G
) be a time graph. The
at time t
is also a time graph G t
Algorithms for community extraction
In the context of time graphs and blogs
in particular, we adopt a more relaxed definition of communities than in [
]. There are at least two motivations for doing this:
- Compared to the web, blogs are not characterized by the strong
distinction between ``authority-type'' and ``hub-type'' pages [
10 ]. Every node in the blog graph corresponds to a `human
being'; this is in contrast with the web where pages can be loosely classified
as `people' (the hubs) and `topics' (the authorities).
- In contrast with the web-scale experiments of [
13 , 12
], the scale of our work here permits us to operate entirely in
memory without streaming the data from disk. As a result, it is feasible for
us to seek dense (rather than complete) subgraphs as community
We therefore consider the undirected version of the
blog graph and say that a dense subgraph is a signature of a blog community. We
will make the notion of a dense subgraph more precise later (see Figure
for a simple example).
Figure 1: A typical signature of a blog
Unfortunately, finding the densest subgraph in an
undirected graph is NP-hard and appears notoriously difficult to even solve
]. We therefore resort to heuristics that are simple, efficient, and
effective. The blog graphs we deal with are small enough that we can perform all
the operations in memory, in contrast to the earlier work of Kumar et
First, following [
], we adopt the notion that pages linked-to by an enormous number of
other pages are too well-known for the type of communities we seek to discover;
so, we summarily remove all pages that contain more than a certain number of
in-links. Next, we remove templates from the graph--this has implications for
the extraction of communities and also for the burst analysis described below.
The details of template identification and removal specific to blog graphs are
presented in Section
Our algorithm consists of two steps--pruning and expansion.
Pruning corresponds to identifying the seed of a community and expansion
corresponds to growing the seed to a dense subgraph that forms the signature of
a community. We adopt the convention that that a node can participate in at most
We adopt the following algorithm for pruning, based
roughly on the original work of Agrawal et al. [
] and the approach of Kumar et al. [13
]. The graph is first scanned for all vertices of degree at most two. Vertices
of degree zero and one are removed, and vertices of degree two are checked to
determine whether they participate in a K3
; that is, whether
their two neighbors are connected. If so, they are passed through as a seed to
the expansion step (described below) and the resulting community is output and
removed from the graph, if it passes a certain threshold. After the entire graph
has been processed in this manner, certain vertices that previously had degree
three or more will now have degree two or less; hence, the pruning step is
repeated several times (specifically, three times in our case). Following the
pruning passes the graph is processed greedily as follows. An arbitrary edge of
the graph is extracted and then grown into a community according to the
expansion algorithm given below. If the resulting community passes a size
threshold, then it is output. In either case, it is removed from the graph and
the process is repeated until there are no remaining edges. Deletion of vertices
is performed by appending the vertex to a ``delete list,'' and checking this
list whenever edges are extracted from the edge data structure. Once the delete
list becomes sufficiently large, it is ``garbage collected'' back into the
The aim of the expansion step is to grow the seed
into a set of nodes that constitute a potential community. First, it determines
the vertex that contains most links to the current community. If that vertex
contains at least tk
such links where t
is a threshold depending only on the size k
of the community
grown so far, then it is added to the current community and the process repeats.
3.3. Burst analysis
In our context, the goal
is to identify communities that are bursty in the blog graph. There is a natural
interpretation of arrivals of edges in the blog graph as an event stream. Recall
that a community corresponds to an undirected dense subgraph. Given a
specific community C
= (V C
), the relevant events correspond to the arrivals of
the edges in EC
. Then, applying Kleinberg's algorithm
] (see Section
), we can obtain the weight of every burst of C
. We apply this
algorithm for each extracted community in the graph.
We collected the data for the blog graph from the following
seven popular blog sites: Blogger
, Globe of blogs
, blogs in Salon
, and Web_Logs
subtree of Yahoo
! Some of the above sites list members explicitly in a
directory while others categorize members by articles or topics. We crawled
these sites to obtain a list consisting of 24,109 urls corresponding to blog
member homepages. For each of the blog members, we crawled both their homepages
and their archives; while the homepages represent the latest entries in the
blog, the archives contain historical entries. Thus, for each blog, we are able
to extract the entire detailed history of every link ever added to the blog,
with the exact time at which it was added. We used a very simple heuristic to
identify if an out-link from the blog member's homepage was indeed an archive
link: the url must contain the prefix `archiv' or some indication of date and
must have an indication of the blog member (name, id number, etc.). The
out-links of a blog member are identified to be the (multiset) union of the
out-links from the homepage of the blog member and each of the archive page. The
is now defined to contain nodes that correspond to blog
members and a link from node p
to node q
if blog member p
created a link to blog member q
at some point in time. The resulting
graph consisted of 22,299 nodes, 70,472 unique edges, and 777,653 edges counting
multiplicity. Observe that the average edge multiplicity of Blogspace is 11, a
reflection of the highly interactive nature of linking. Note that we have not
associated time information with each edge as yet; this is done in the next
The subject of extracting specified entities from
documents is a subject of on-going research (see, for instance, [
]). In our case, the entities correspond to valid dates. Because we were
focused on date extraction from blogs in particular, we adopted a blog-specific
scheme. Most blogs are published using a blog publishing software package (say,
blogger that is available from http://www.blogger.com/
), and therefore
specify dates in a uniform format. However, there will also be additional dates
that occur textually within the blog, and we must be careful not to be misled
into believing that these dates represent a new journal entry. We created a
broad set of date patterns based on various numeric and alphanumeric date
formats, including only partially-specified dates (i.e., ``September 1'', which
does not contain a year). We applied these patterns to the text of each blog
page, and for each spotted occurrence we noted the family of pattern that
matched, and the surrounding context of the reference. We then processed the
entire sequence of extracted dates to determine whether a particular pattern
family and/or context was repeated frequently enough. If so, we adopted the
dominant scheme as the dates inserted by the blog publication software. We
introduced some special logic to match contexts for well-known blog publishing
tools such as blogger. Finally, we back-filled missing year information into
partially-specified dates to complete them. We did not implement the additional
heuristic of checking that all dates believed to be journal entries form a
decreasing or possibly increasing sequence, but this heuristic might have
increased our confidence. Using the existing algorithm, we were able to
associate dates with about 90% of the links extracted from post-template-removal
blog pages. The remaining 10% of edges occur due to various template links and
``blogrolls''--a list of links to fellow bloggers' sites. We assigned a time of
0 for these links. We consider a blog to be alive since the earliest non-zero
time tag of its out-links. Bucketing the time in terms of the number of months
since January 1999 lets us construct a sequence of 47 prefixes of the blog
graph. We refer to these 47 graphs as prefix graphs
We now discuss the specific settings of various
parameters while running our algorithms on the blog graph.
Detecting templates in web pages is in itself an active area of
research (see, for instance, [2
]). It is particularly acute in the case of blogs for the following reason.
Bloggers often modify a profile that appears as a template on all archive pages
corresponding to the blog, and may include a series of links (Recently, the Rich Site
Summary (RSS) XML format
has become a popular means to announce ``What's
new'' in the blogging community.) We wish to avoid our algorithm being misled
into thinking that the archive corresponding to a particular date includes those
links, when in fact the archive may be several years old and the template may
have been created only yesterday. We adopt the following simple heuristic for
removing templates: remove any sequence of three or more links occurring in a
blog for three or more days. To be conservative, we also removed links with time
0. As in [13
], we also removed any node with in-degree more than 1000 from consideration in
the community identification. In the expansion step of the community extraction,
the thresholds tk
were determined heuristically as
follows: edges must grow to triangles; communities of size up to six will only
grow vertices that link to all but one vertex; communities of size up to nine
will only grow vertices that link to all but two vertices; communities up to
size 20 will grow only vertices that link to 70% of the community; and larger
communities will grow only vertices that link to at least 60% of the community.
(It is possible that many vertices have at least t k
links to the current community. The algorithm as specified will add only the
best such vertex, but for much larger datasets, it may be necessary to expand
the current community by more than one vertex at a time. In such cases, we
recommend no more than doubling the size of the current community at each step,
to avoid adding large numbers of disjoint pages linked to a small central core.)
In burst analysis, we identified the links in each community as relevant
events (as in Section
) and divided them into chronological batches according to the week
each link was added. For each community identified in the previous step, we
calculated the number of links created between blog members in the community
during each week and the total number of links between pages in the community,
to use as input to a two-state automaton. Each state of our automaton
corresponds to a different fraction of relevant link generation: a lower rate
during calm periods and a higher rate during bursty periods. By adjusting the
scaling parameter which determines how much the high rate differs from the low
rate, we were able to control the length of typical bursts. We experimented with
various values for this parameter, and chose a value which resulted in the
majority of bursts ranging from one week to several months.
We begin in Section
with an analysis of the evolution of structural properties of the time
graph, as shown by analysis of the sequence of prefix graphs (as defined in Section
). This analysis shows surprising behavior: around the end of 2001, the
macroscopic structure (as measured by the formation of a giant component) and
the microscopic structure (as measured by the formation of a large number of
small communities) of the graph began to change dramatically. In Section
we argue that the change cannot be explained simply through the size,
density, and link arrival pattern of the graph, but in fact arises only because
of the particular linking decisions made by bloggers. In light of this
observation Section Section
then presents our analysis of bursty behavior within the blog
communities we have extracted, and shows that this burstiness is a fundamental
property of link creation in blogspace.
5.1. Analysis of prefix graphs
We first study the degree distributions of prefixes of the
blog graph. The results are shown in Figure
. Each line in the figure represents a prefix graph of the full time graph
corresponding to a snapshot of the graph at a particular point in time. Higher
lines correspond to more recent snapshots. The upper graph gives the in-degree
distribution; that is, the number of pages in the time graph with a given
in-degree. The lower graph gives the out-degree distribution. As the figure
illustrates, the distributions remains fairly stable over time, increasing
uniformly in y
value due to the growth in size of the graph, but
retaining the same overall shape. The later curves also become smoother, and it
is possible to note that the tail of the curves (to the right of the graph,
corresponding to nodes of higher degree) tracks fairly well to the power law
curve with exponent -2.1.
Figure 2: Evolving degree
We also study the evolution
of the strongly connected component (SCC) in the prefix graphs. The results are
shown in Figure
. For each of the three largest strongly connected components, the figure
shows what fraction
of the nodes in the prefix graph are part of that
SCC at each point in time. The results here are quite dramatic. In January of
1999, at the beginning of our study, the number of blog pages is significant but
there is no strongly connected component of more than a few nodes. Around the
beginning of 2000, a few components representing 1-2% of the nodes in the graph
appear, and maintain roughly the same relative size for the next year. But up to
this point, blogspace is not a coherent entity--the overall size has grown but
the inter-connectedness is not significant. At the start of 2001, the largest
component begins to grow in size relative to the rest of the graph, and by the
end of 2001 it contains about 3% of all nodes. In 2002, however, a threshold
behavior arises, and the size of the component increases dramatically, to over
20% by the present day. This giant component still appears to be expanding
rapidly, doubling in size approximately every three months. Clearly this growth
cannot continue and must plateau within two years.
Figure 3: SCC evolution.
now turn to an analysis of how many pages take part in a ``community,''
according to the definition implied by the community extraction algorithm of Section
shows the number of communities of each size discovered by the algorithm
on the underlying graph.
Table 1: Distribution of community sizes in the blog graph
shows the results of applying the same algorithm to the prefix graphs. The
upper figure plots for each time interval the total number of communities in the
prefix graph, and the total number of nodes that participate in one of those
communities. The lower figure plots for each time interval the fraction of nodes
that belong to some community. If this fraction is large, one can view the space
of all blogs at that time as consisting as a set of small inter-networking
communities, rather than a set of standalone pages. These graph show the same
striking pattern as earlier graphs in this section: a period of minimal
structure during 1999 and 2000, slow growth in 2001, and then rapid growth in
Figure 4: Community evolution.
To conclude, the
degree distributions match earlier observations from many communities, and do
not represent a surprise. The analysis of the largest SCC (a macroscopic
phenomenon) and of communities (a microscopic phenomenon) does represent a
surprise: by both measures, a fundamental change occurred in blogspace
approximately one year ago, and we are still experiencing the results of that
transition. To assess whether this observed behavior does in fact stem from the
sociology of blogspace, we must first study the alternate possibility: namely,
that the emergence of a giant component and a strong community structure would
occur naturally when the graph reached a certain size and density. We now
address this question.
5.2. How random is the blog graph?
We wish to determine whether the
prefix graph behavior we have seen is caused by a phenomenon similar to that of
a time-evolving version of Erdös-Rényi random graphs [6
], but tailored to produce graphs that match Blogspace in the source and arrival
time of every edge. To study this, we create a graph derived from Blogspace
called randomized Blogspace
. This graph is identical to the blog time
graph, except that the destination of every edge is replaced by a uniformly
chosen random node. Thus, the arrival time of each edge, the number of edges at
each time, and the exact profile of when a page chooses to add a link is left
untouched. The only difference is the destination of each new link. To be
precise, we sort the edges of the blog graph according to time (ties are broken
arbitrarily). We scan the edges sequentially and change each destination to be a
node uniformly chosen from among the nodes that have already been seen. Note
that this preserves the times at which links arrives at all sources, and
modifies only the destinations of those links.
Figure 5: SCC evolution in randomized
plot the same quantities as Figure
for randomized Blogspace instead of the original blog graph. Since we
included the time 0 edges, there is a substantial SCC to begin with. As time
progresses, this SCC seems to have a threshold growth as in blog graph case
(this is how a random graph would behave as well). For completeness, we also
evaluated the growth of the giant SCC without the time 0 edges; initially it was
of course much smaller, but it exhibited a similar threshold behavior and became
a larger fraction of the overall graph during the last timestep than in
Blogspace. However, comparing to Figure
, we see that the number of nodes in communities for randomized Blogspace
is an order of magnitude smaller than for Blogspace, indicating that the
community formation in Blogspace is not simply an emergent property of the
growth of the graph. On the other hand, comparing Figure
shows that the SCC in randomized Blogspace grows much faster than in the
original blog graph.
Figure 6: Community evolution in randomized
So randomized Blogspace actually attains a large
strongly connected component faster than Blogspace does; however, it does not
attain significant community structure. If bloggers added links to other blogs
without reference to topicality, the graph would still become well-connected,
but it would not exhibit the striking community focus that characterizes
5.3. Burstiness in blog communities
shows the number of communities that are in the high state during each
time interval, as described in Section
. The x
axis again shows time, but stops several months before
our most recently crawled pages, because we cannot effectively evaluate the
number of bursts occurring during the present without context about the near
future. Again, consistent with our earlier observations, there is a spurt of
bursty activity toward the end of 2001 that continues through to the present.
Figure 7: Burstiness of
Interestingly, the increase in number of bursts is not
explained by the increase in number of communities alone. Not only have the
number of communities in Blogspace been growing over the last year, the average
burstiness of each individual community has also been growing. This suggests
that the transition behavior we have observed in all our temporal analyses is in
fact correlated with a change in the behavior of the bloggers themselves. For
whatever reason, perhaps because of the richer set of available communities, or
the change in the population of Blogspace, content creators have increased their
participation in bursty community activity over the last year, and the trend
shows no sign of slowing.
5.4. Anecdotal evidence for the effectiveness of
community and burst extraction
We explored a large number of communities and
of bursts within communities using a web-based tool we created for the purpose.
The communities found by the extraction algorithm are almost universally on
topic. In all cases we examined, the communities contained many internal links,
and it was usually clear what bound the members together: be it an interest in
Flash, the law, or library science. Periods of bursty behavior require a deeper
investigation into blog content. In some cases, a burst occurs due to a spate of
activity by one or two bloggers during the time period, as when during August
and September of 2002 blogger Karen
started linking to her sister's blog
several times a day. Other
bursts are the result of many members of the community contributing new links to
each other. Although we are not always able to determine the reason for the
intense period of linkage, in most cases there is a clear identifiable event or
set of events. As the following closing example shows, the amount of information
in a blog burst, and the window it gives into the lives of the bloggers, can be
is part of a
group of artists in Seattle who form a blogger community. She's involved in
fringe theater, and some of the other members are in a band together (see June
28, 2002 on http://www.articulatebabble.org/archives/2002_06.php
). Several events surround the bursty link behavior during the four months from
June-Oct in 2002. Alicia decides to connect with old high school friends (see
June 24, 2002 on http://www.aliciadawn.com/blog/archives/2002_06.html
). She asks two members of the community to set up blogs for them (see June 10,
2002 on http://www.aliciadawn.com/blog/archives/2002_06.html
), which they do (see July 13, 2002 on http://melody.asc-soft.com/%20enigma/blog/archives/2002_07.html
). The event generates a mini-burst of blogging. She then convinces two high
school friends to visit Seattle on two different weekends. Lots of blogging
covers what to show them when they visit (see August 5, 2002 on http://www.aliciadawn.com/blog/archives/2002_08.html
), waiting at the airport (see July 14, 2002 on http://melody.asc-soft.com/%20enigma/blog/archives/2002_07.html
), picking them up at the airport, their reaction to Alicia's theater
performance, and so on. A third event during this same period occurs when two
members of the community get engaged. There's discussion about the engagement,
and the beautiful kids they'll have (see June 28, 2002 on http://www.jetlin.com/blog/archives/2002_06.html
In analyzing the space of weblogs, we have presented a
detailed picture of a web publishing phenomenon in the midst of explosive
growth. Around the end of 2001, Blogspace began a dramatic increase in
connectedness, and in local-scale community structure. Within those local
communities, it also began to exhibit dramatic increases in the occurrence of
bursty link creation behavior. Blogspace represents a clean, detailed, and
measurable instance of a hyperlinked corpus in evolution, and is thus an
excellent testbed for exploring evolutionary analysis, in addition to being of
significant interest in its own right. The tools we have developed are
applicable to other evolving hyperlinked corpora, including sequences of
snapshots of the web.
We thank Jon Kleinberg
for providing the burst analysis code. We thank Amit Kumar for bringing to our
attention the popularity of RSS XML among bloggers.
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