6. Results
Crawled for ~10 hours
Number of samples: 48000
Number of age samples: 36363, not all users
show their age
7. Results - Ages
RW
estimates
lower After about 25k
average age samples, the There is a big
values. age stabilizes. correlation
between age
and the
degree
8. Results - Playlists
Most users do
not have
playlists.
RW estimates higher
numbers of playlists. Users
with higher degrees tend to
have more playlists.
9. Results - Playcounts
We found some
users having
playcounts in the
order of millions.
RW estimates higher
playcounts. Users with
higher degree tend to
have higher playcounts
10. Results - IDs
Not yet stable.
RW estimates a lower
average ID compared to
RWRW. An user with lower
ID has generally a higher
degree
11. Results - Degrees
RWRW reduces the bias of nodes
with higher probability to be visited
due to the high degree. This is
indeed close to the expected degree
value.
12. Conclusion
● A simple random walk in a social network
generally results into biased averages.
○ A node with higher degree has a higher probability of
being discovered.
● RWRW normalizes the averages.
○ High variations do not abruptly impact the
estimation.
○ RWRW reduces the biases of RW.
● Low variance means lower difference
between RW and RWRW.
● Crawling lastfm produces many challenges
○ e.g.: 0 degree, banned user, huge playcounts