[Nrg-l] Database seminar is tomorrow

Feifei Li lifeifei at cs.bu.edu
Mon Feb 13 15:32:51 EST 2006

Hi All:

This is a remind for tomorrow's talk at the weekly database seminar. Please
find the details below.


-----Original Message-----
From: Feifei Li [mailto:lifeifei at cs.bu.edu] 
To: 'dbg-s at cs.bu.edu'
Subject: FW: paper

Hi All:

I am pleased to announce that Georgios will present the following paper in
the next week's database seminar. As usual, we will meet at 4pm on Tuesday
at grad. lounge. 
Another short remind will be sent on the day before the talk.

See you there!

-----Original Message-----
From: Georgios Zervas [mailto:zg at bu.edu]
To: Feifei Li
Subject: paper


How do real graphs evolve over time? What are "normal" growth patterns in
social, technological, and information networks? Many studies have
discovered patterns in /static graphs/, identifying properties in a single
snapshot of a large network, or in a very small number of snapshots; these
include heavy tails for in- and out-degree distributions, communities,
small-world phenomena, and others. However, given the lack of information
about network evolution over long periods, it has been hard to convert these
findings into statements about trends over time.Here we study a wide range
of real graphs, and we observe some surprising phenomena. First, most of
these graphs densify over time, with the number of edges growing
super-linearly in the number of nodes. 
Second, the average distance between nodes often /shrinks/ over time, in
contrast to the conventional wisdom that such distance parameters should
increase slowly as a function of the number of nodes (like O(log n) or
O(log(log n)).Existing graph generation models do not exhibit these types of
behavior, even at a qualitative level. We provide a new graph generator,
based on a "forest fire" spreading process, that has a simple, intuitive
justification, requires very few parameters (like the "flammability" of
nodes), and produces graphs exhibiting the full range of properties observed
both in prior work and in the present study.

and link:


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