1. Evolution of User Activity with Time on
Third-Party Facebook Applications
Brian Estrada, Atif Nazir, (Leo) Xin Liu, Chen-Nee Chuah, Balachander Krishnamurthy
Abstract—We examine how user activity graphs on third-
party Facebook applications evolve over time. Using user activity
data spanning a year on three Facebook applications, we show
how graph-theoretic properties and their temporal variations
are influenced by the maturity, popularity, and user engagement
level of the applications. We also examine the heavy sub-graphs
consisting of strong links, i.e., user-pairs with large number of
interactions. We find that the general evolution trend of the graph
properties remain unchanged although the absolute values differ.
By analyzing individual usage patterns over time, we identify
super heavy users in our gaming application (i.e., 5% of top
users account for 80% of interactions), but not in social utility
applications. We also see ‘addictive’ behavior on our gaming
application, where heavy users remain dominant for months. Our
findings are useful for application developers and OSN providers
in devising provisioning and advertising strategies.
I. INTRODUCTION
Online social networks have evolved over the last five
years becoming increasingly relevant to our daily lives. With
Facebook’s userbase at nearly half a billion users worldwide, it
is poised to reshaping the Web by making it more social [11].
One dominant contributor to Facebook’s growth and increasing
user engagement are the social applications built on the
Facebook Developer Platform. Social applications, through
virtual goods, became a billion dollar industry in the U.S.
alone by 2009.
Previous studies have mostly leveraged datasets from a
number of popular OSNs to study online user interactions [4],
[1], [6]. They have highlighted the importance of considering
user activity over formation of social relationships in OSNs
such as Facebook [2], [13]. While these studies analyzed user
activity as a single snapshot in time, recent work emphasize
the dynamic nature of user interaction in OSNs: how OSNs
such as MySpace gained and lost popularity [8], how graph-
theoretic properties of activity networks remain stable over
time [10] although activity-based relationships on Facebook
are ep-hemeral, and how age of online photos does not affect
their popularity over time on Flickr [9]. Other studies include
modeling OSNs’ growth [3] and temporal distance metrics that
help quantify speed of information spread in OSNs [7].
A subset of user interactions on OSNs occur exclusively
through third-party social applications. Earlier, we pioneered
the study of these interactions on selected Facebook appli-
cations with a combined userbase of 8 million users [5].
We considered metrics such as community sizes, clustering
coefficients, and diameters in user interaction graphs, to show
that application dynamics are pivotal in defining the structure
of interaction graphs. But that analysis considered a single
snapshot of the graph based on several months of user activity
data.
Our goal in this paper is to account for evolution of user
activity characteristics over time, as applications mature and
decline in popularity. Our analysis is based on user interactions
through third-party applications as opposed to the underlying
OSN that [10] focused on. We extend our dataset from [5]
using three top Facebook applications that collectively account
for (i) more than 74 million active users globally, and (ii) a full
year of user activity. Among these three applications, Hugged
and iHeart are social utility applications, while Fighters’ Club
(FC) is a social game. Through our longitudinal study, we
investigate evolution of user activity graphs and address the
following questions:
• How do graph properties (such as clustering coefficients,
community sizes, etc.) evolve with time? How are temporal
behavior of these activity graphs influenced by factors
popularity or engagement level of the applications?
• How much do the properties of the interaction graphs vary
if we focus on sub-populations that generate a lot of pair-
wise interactions among themselves?
• What fraction of users interact with applications heavily
over time? How much is the churn of these ‘heavy’ users,
e.g., how frequently do the same users stay heavy (i.e.,
persist) across weeks?
We find that some properties of the activity graphs, namely
clustering and structure coefficients, stabilize early during an
application’s lifetime and remain stable throughout the length
of our activity traces, while the number and size of connected
components vary with application popularity. We oberve that
the heavy sub-graphs (comprising only heavy user-pairs) show
significantly higher clustering for our gaming application,
compared to the social utilities applications. Moreover, there
exist super heavy users (top 5% of the active userbase that
account for nearly 80% of user interactions) in our gaming
application, but not in our utility applications. These users
demonstrate low churn as compared to the general user base;
some even stay active in our gaming application for more than
250 days in a year!
Our analysis provides useful insights into how to more
accurately model user activity networks on third-party so-
cial applications. It helps application developers and OSN
providers to identify heavy (generating a lot of interactions)
and persistent (returning frequently to the application) users
for provisioning purposes. For example, given that heavy
users’ dominance occurs only in gaming application, it hints

2. at the need to provision differently for games and social utility
applications. Our results also show that information diffusion
occurs fastest on more engaging applications like social games.
We describe our data collection methodology and applications
in Section II, our results in Section III and conclude in Section
IV.
II. MEASUREMENT METHODOLOGY
We use data gathered from three third-party Facebook ap-
plications: Fighters’ Club (FC), Hugged, and iHeart. All three
were developed using the Facebook Developer Platform[12],
and have a combined userbase of 74.4 million users world-
wide. While FC is a social gaming application, Hugged and
iHeart are social utility applications. All our applications are
in the top 5% of Facebook applications, ranked by daily active
users (DAU), with iHeart being the most popular (ranked
in top 10), followed by Hugged (top 75) and FC (top 200)
over the period of our study. The data is collected at the
application server, where we record the time stamp for each
activity that the user performs through our applications. For
each application, we construct the associated activity graph
G=(V ,E). We say that an edge (e ∈ E) exists between two
unique users A and B (where A, B ∈ V ) if A performs an
activity on B through the application. What constitutes an
activity is explained in the following.
Fighters’ Club (FC): FC was launched on Jun 19, 2007, and
has 4.4 million users to date. It is a social gaming application
that allows users (offenders) to instigate virtual, timed fights
against their friends (defenders). The users can pick a fight
duration from 15 to 48 hours, during which time the instigator
and target’s friends can ‘support’ either player. Our data shows
that 90% of the games lasted for 15 hours, and the rest are
uniformly distributed between 15 and 48 hours. A user can
perform two types of actions in each fight:
• Support: A user must first ’support’ either the offender or
defender team before they can participate in the fight. Support
is a one-time required action and does not reflect active user
engagement over time.
• Hit: Once a user becomes a supporter in a fight, they can
decrease the opposing team’s strength1
through virtual ‘hits’
on users in the opposing team.
The team with the higher cumulative strength at the end
of the fight is declared the winner. Clearly, hits require more
effort than supporting users, and are the main cause for user
engagement in FC. To reduce noise in our analysis, we only
focus on hit activity data between a pair of users in FC.
We studied the 52-week hit activity data April 20, 2008 to
April 19, 2009, during which the application had matured and
stabilized (in terms of attracting and maintaining a steady user
population).
Hugged: Hugged was launched on Jan 29, 2008, and has 20.4
million users to date. It is a simple application that allows users
1The measure of strength is a point system ranging from 0 to 5 points
for each individual on FC. By default, each user’s strength is 3.0, and it
increases/decreases as the users win/lose fights, respectively. Individuals with
0.0 strength cannot be targeted in virtual hits.
to send virtual ‘hugs’ to their Facebook friends. One user may
send simultaneous virtual ‘hugs’ to 20 friends, and may send
‘hugs’ to the same friends repeatedly over time. Sending a
virtual hug to a particular user is considered an ‘activity’ in
our analysis. We use 52 weeks of activity traces on Hugged,
April 20, 2008 to April 19, 2009, which again represents the
time period where the application had matured and stabilized.
iHeart: iHeart was launched on Jun 19, 2009 and has been
used by 54.4 million users to date. Because of its immense
popularity with Facebook users, it has been ranked the third
most used Facebook application, and is the tenth most used
Facebook application at the time of writing this paper. Similar
in nature to Hugged, iHeart allows users to send decorative
‘hearts’ to their Facebook friends. However, we improved
user engagement on iHeart through a basic gaming dynamic:
rewards for receiving hearts. This provides for higher user
persistence (i.e., frequency of returning to the application) on
iHeart as compared to Hugged, but not FC. We employ user
activity traces from iHeart starting from June 19 to December
19, 2009. Note that since we logged activity starting with the
launch of the application, we are able to analyze the ramp-up
period of the application.
Data Statistics: Table I shows the total number of activities
recorded for each application and total number of active
users (who perform at least one activity). It also shows the
engagement ratio, i.e., ratio of Daily Active Users (DAU)
to Monthly Active Users (MAU) for both the start and end
of our study period. DAU/MAU is an indicator of how
many monthly users return daily. A higher value implies more
application visits per user.
TABLE I
DATA SET ANALYZED
FC Hugged iHeart
(52 wks) (52 wks) (26 wks)
Total Active Users 40.7K 4,708K 18,753K
Total Activities 14,975K 88,086K 535,270K
DAU/MAU (Start) 15.7% 3.7% N/A
DAU/MAU (End) 17.8% 5.2% 11.0%
III. RESULTS
We analyzed the global structure of user activity graphs on
our applications and then zoomed in on heavy users and user-
pairs that account for majority of the activities.
A. Temporal Evolution of Activity Graphs
Using weekly activity graphs G=(V ,E) for FC, Hugged,
and iHeart, we studied how the following graph properties
associated with the graphs evolve across time:
• Clustering Coefficient (CC): The CC of a node is the
ratio of actual edges between that node’s neighbors and the
number of possible edges between them. The CC of a graph—
the average of clustering coefficients across all nodes—reveals
how tightly the nodes are connected to each other. High
CC correlates positively with the user engagement level on
an application, and allows application developers and OSN
providers to predict activity levels and provision resources
accordingly.

3. 0 10 20 30 40 50
0.015
0.02
0.025
0.03
0.035
0.04
0.045
Time (Weeks)
ClusteringCoefficient
Fighters’ Club
Hugged
iHeart
Fig. 1. Clustering coefficients per week for FC,
Hugged and iHeart.
0 10 20 30 40 50
0
5000
10000
0 10 20 30 40 50
0
5
10
x 10
4
Time (Weeks)
NumberofComponents
Fighters’ Club
Hugged
iHeart
Fig. 2. Number of components per week for FC,
Hugged and iHeart.
0 10 20 30 40 50
0
500
1000
1500
2000
2500
3000
Time (Weeks)
ComponentSize
Fighters’ Club
Hugged
iHeart
Fig. 3. Minimum, average and maximum compo-
nent sizes per week.
0 10 20 30 40 50
0
0.1
0.2
0.3
0.4
0.5
0.6
Time (Weeks)
StructureCoefficient
Fighters’ Club
Hugged
iHeart
Fig. 4. Structure coefficients per week for FC,
Hugged and iHeart.
0 10 20 30 40 50
0.02
0.04
0.06
0.08
0.1
0.12
0.14
Time (Weeks)
ClusteringCoefficient Fighters’ Club
Hugged
Fig. 5. Clustering coefficients for FC’s and
Hugged’s heavy sub-graphs.
0 10 20 30 40 50
0
1000
2000
3000
4000
5000
0 10 20 30 40 50
0
0.5
1
1.5
2
x 10
4
Time (Weeks)
NumberofComponents
Fighters’ Club
Hugged
Fig. 6. Number of components for FC’s and
Hugged’s heavy sub-graphs.
0 10 20 30 40 50
0
500
1000
1500
2000
2500
Time (Weeks)
ComponentSize
Fighters’ Club
Hugged
Fig. 7. Minimum, average and maximum compo-
nent sizes for FC’s and Hugged’s heavy sub-graphs.
0 50 100 150 200
0
0.2
0.4
0.6
0.8
1
Edge Weight
CumulativeDistributionFunction
Fig. 8. Weekly edge weight distributions for FC
(52 lines in total).
0 5 10 15 20 25 30
0
0.2
0.4
0.6
0.8
1
Edge Weight
CumulativeDistributionFunction
Fig. 9. Weekly edge weight distribution for Hugged
(52 lines in total).
• Connected Components: Two users A and B belong to the
same connected component if there exists a path connecting A
and B in the graph. OSN-based friendship connections enable
social applications to grow virally, as a chain reaction from
one user to their friends and so on. Measuring the evolution
of the number (Ncc) and size (Scc) of components with time
highlights how quickly information diffuses through the social
applications.
• Structure Coefficients (SC): A network of nodes is said
to exhibit community structure if there exist subgraphs (or
communities) whose nodes are more densely connected within
that subgraph than with nodes outside that subgraph. SC is a
measure of how much a community structure exists within a
graph [5], e.g., SCs of 0.3 or greater indicate high community
structure in a graph. Existence of high community structure
is common in real-world networks such as groups based on
location, interests, occupation, as well as friendship connec-
tions on OSNs. The existence, and persistence, of community
structures (or natural divisions) in activity graphs makes data
segregation feasible for efficiently processing large, multi-
million user databases.
Our results show that clustering coefficients (CCs) are
higher for applications with more gaming dynamics. Among
our applications, FC has the highest number of gaming dynam-
ics such as timed fights and virtual currency, iHeart has one
(rewards for receiving virtual hearts), while Hugged has none.
The presence of gaming dynamics provide incentives for users
to return to FC and iHeart, and to interact more with friends,
leading to greater clustering of user activity. As seen in Figure
1, for the length of our trace, FC exhibits the highest clustering

4. of user activity, followed by iHeart and then Hugged. Apart
from iHeart’s initial ramp-up time after launch, CCs tend to
remain stable overall and only vary within 20% of the average
value for Hugged, iHeart and FC. It is interesting to note that
our newer application (iHeart) only took 2.5 months of ramp-
up time before user populations/activities stabilized (Figure 1-
4).
Next, we examined the number (Ncc) and size (Scc) of
connected components per week to study information diffusion
on our applications. Figure 2 shows FC has the lowest weekly
Ncc, followed by iHeart and then Hugged. While Hugged
and iHeart (after initial ramp-up time) exhibit stability in the
weekly Ncc, FC shows a decreasing trend with time as it loses
popularity. Also, FC provides the most incentives for user
interaction (thus increasing connectivity), and hence shows the
fewest disconnected components (Ncc), followed by iHeart
and Hugged. This is seen further in Figure 3, showing the
average size of weekly components, Scc. Figure 3 shows that
the application with the highest amount of gaming dynamics
(FC) has the largest groups of connected users throughout
the activity trace, followed by iHeart. Thus, if one piece
of information were to be injected to selected users of our
applications, it would travel to the most people in FC, followed
by iHeart and then Hugged within the same time frame.
However, contrary to Figure 2, Figure 3 shows a stable trend
for FC, as the users who stay tend to be more engaged and
remain connected with other similarly engaged users.
We also consider structure coefficients (SCs) and commu-
nities formed by weekly activity graphs on our applications.
While our results for number and size of weekly communities
were similar to those for connected components, we find that
for social applications that allow interaction only between
relationships as in the underlying social graph (i.e., between
friends on the OSN), the activity graphs tend to exhibit high
community structure. This is shown by the consistently high
SCs for Hugged and iHeart (friends-only interactions), and by
consistently low SCs for FC (non-friend interactions allowed),
in Figure 4. Moreover, the SCs, too, tend to remain stable over
time.
Our results so far have considered all activities equally.
That is, users that interact repeatedly with each other are
considered equal to users that interact only once. Further
insight can be provided if we highlight stronger links (or
heavier user-pairs) in our analysis. We do this by assigning
weights (based on number of activities for each week) to edges
between user-pairs in our activity graphs. We then select those
edges that account for 80% of total pair-wise interactions per
week. Weekly edge weight distributions are shown for FC and
Hugged in Figures 8 and 9, respectively. Figure 8 shows that
for FC, heavy user-pairs are clearly distinguishable, whereas
Figure 9 shows this is not the case for Hugged—an artifact
of the higher engagement offered by FC. We omit iHeart in
this analysis because we only have 26 weeks of data, part of
which spans the ramp-up time as iHeart matures and attracts
a stable user base.
By pruning our weekly activity graphs using thresholds to
select only heavy pair-wise interactions, we revisit the basic
graph properties. We find evidence of stronger clustering of
user activity among heavy users for FC and Hugged (Figure
5) in comparison with lower clustering coefficients for general
users (Figure 1). We also find that the number of connected
components per week reduced by half for FC, and about 4
times for Hugged (Figure 6), whereas the average size of
weekly components stayed nearly the same (Figure 7). Our
results imply that there exist groups of users that interact very
heavily among themselves on applications, and the proportion
of these heavy users compared to total userbase is dependent
on the engagement level (gaming dynamics) offered by the
application. Moreover, heavy users’ interactions are more
clustered with other heavy users on applications with higher
number/quality of gaming dynamics. Additionally, we found
structure coefficients for our heavy activity graphs to be similar
to those for general users (Figure 4).
B. Heavy, Persistent, and Addicted Users
We now analyze individual user activities over a period of a
year for the two stable applications, FC and Hugged2
. For all
unique users, we track the total number of activities performed
per day and per week. We can thus determine the number of
days that a particular user is ‘active’ (i.e., generate non-zero
interactions). We study the following categories of users:
• Heavy users: the top-ranked users accounting for majority
(70-80%) of the activities (over the full year).
• Persistent users: generating non-zero activites on an appli-
cation over many days, or even months.
• Addicted users: both heavy and persistent.
By identifying heavy, persistent and addicted users, OSN
developers can tune their application to better serve these users
by providing suitable incentives to enable them to continue
using the applications.
Figure 10 shows the Complementary Cumulative Distribu-
tion Function (CCDF) of the number of interactions performed
by all users over the 52-week period in log-log scale. We
see extremely active users and users who barely performed
any interactions. The number of interactions per user on FC
(average = 368) is an order of magnitude higher than Hugged
(average = 19)—somewhat expected given that gaming appli-
cation requires a higher number of interactions than Hugged
(as seen in the distribution of edge weights in Section III-A).
In Figure 11, each curve shows the fraction of users (ranked
based on number of interactions they performed in a week) on
the x-axis, and the associated fraction of interactions (y-axis)
they performed in that week. We include results for 52-week
analysis for both FC and Hugged, showing that the top 10%
of the FC users account for 70-80% of the interactions in any
given week, while 30-50% of the Hugged users account for
80% of the total activities. Clearly, an elite group of heavy
users on FC accounts for majority of the interactions, while
this phenomenon is not observed in Hugged.
2We exclude iHeart since we only have 26 weeks of data including ramp-up
time for popularity.

5. 10
0
10
2
10
4
10
6
10
8
10
6
10
4
10
2
10
0
Number of Interactions
ComplementaryCDF
Hugged
FC
Hugged:
Mean:19
Max: 3826
Min: 1
FC:
Mean: 368
Max:379050
Min: 1
Fig. 10. Log-log plot for CCDF of total number
of interactions (over 52 weeks) per user on FC and
Hugged.
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0
0.2
0.4
0.6
0.8
1
Fraction of Users
FractionofNumberofInteractions
FC
Hugged
Fig. 11. Fraction of users vs. fraction of interactions
over weekly period (average and standard deviation).
10
0
10
1
10
2
10
8
10
6
10
4
10
2
10
0
Number of Days
ComplementaryCDF
Hugged
FC
Hugged:
Mean:2
Max: 279
Min: 1
FC:
Mean: 7
Max: 324
Min: 1
Fig. 12. Log-log plot for CCDF of total number of
days a particular user is active on FC and Hugged.
0 10 20 30 40 50 60
0
0.2
0.4
0.6
0.8
1
Number of Weeks Users Make to Top 200
CumulativeDistributionFunction
Fig. 13. FC: CDF of the number of weeks users
make to the top-200-list within 52 weeks.
0
50
100
150
200
250
300
350
User ID Ranked by Number of Interactions
InteractionTime(Days)
Fig. 14. FC: Interaction time of unique users (in
increasing order based on number of interactions).
0 500 1000 1500 2000
0
200
400
User ID Ranked by Interaction Time
InteractionTime(Days)
0 500 1000 1500 2000
0
200
400
User ID Ranked by Number of Interactions
InteractionTime(Days)
Fig. 15. FC: Interaction time of heavy users in
increasing order of (a) interaction time, (b) number
of interactions.
Next, we ask how frequently do the users return to our appli-
cations (persistence of users)? A user is ’active’ on a particular
day if she performs at least one activity on our application. The
interaction time is measured in terms of number of days a user
is ‘active’ on an application. Figure 12 shows the interaction
time per user is generally an order of magnitude larger on FC
than Hugged (7 days on average for FC as compared to 2 days
for Hugged). In both applications, we have a few but highly
persistent users who use the application almost daily during the
year. We have previously commented on the engaging nature
of social games in general, and of FC in particular. A social
application with high engagement relies on existence of heavy
and persistent users, which leads to a longer-term success for
the application developers and the host OSN. It is evident from
our results that social utility applications such as Hugged do
not garner high user engagement.
A more meaningful study of third-party application users,
then, may be derived by considering only users that interact
heavily and consistently on the applications over time. This
study is meaningful for gaming type applications, where there
are consistent heavy users who use the application often. We
look at the top 200 users in terms of number of gaming
interactions during each week for FC. Out of 40,670 unique
users who used FC, only about 2,211 users ever make it to
top 200 in any of the 52 weeks. These 5.4% of users account
for about 86% of the total interactions. For non-engaging type
applications such as Hugged, there isn’t a super power group
of users whose interactions dominates all of the users (as seen
from Figure 11).
Figure 13 shows the CDF for the number of weeks these
users remain among the top 200 users on FC. 60% of the
power users are likely to stay on the top 200 list for more
than one week. About 20% stay on the list for more than a
month and the top 10% dominate the list from 3 months to
50 weeks. This implies that a high proportion of top FC users
are consistently active over time.
Next we take a closer look at the spread of user persistence
on FC. In Figure 14, the users are ordered based on total
number of interactions performed in all 52-weeks (in increas-
ing order on x-axis), and the y-axis shows the associated
interaction time (i.e., number of days a user is active on
FC). While there is a general trend where the interaction time
increases with the number of interactions (i.e., heavy users
are more likely to be persistent), there is a large variation of
interaction time even among the extremely heavy users. This
is made clearer in the bottom graph of Figure 15, which shows
the same plot for only the top 5% of FC users (users who are
on the top-200 list for any given week and collectively account
for 80% of the total interactions over the 52-week period).
To study the addicts (i.e., both heavy and persistent), we
refer to the top graph in Figure 15, which shows the same
data, but the x-axis shows user IDs ranked based on interaction
time (in increasing order). It shows that while some users are
binge addicts, users who generated a lot of activities for a short
period of time (e.g., days, weeks), there are a group of genuine
addicts. Around 3% of FC users interacted with the application

6. on at least 100 days out of the 52 weeks accounted for. This is
surprising in that even though there are no real rewards offered
directly by FC, people are motivated enough to dedicate signif-
icant amount of time to the game. Anecdotal evidence suggests
that top users on FC have formed real-world friendships (even
marriages) and communities around the game, and regularly
hold monthly/quarterly meetings dedicated to discussing and
celebrating events originating from FC. Most of these users
reside in or near Canada, enabling such gatherings.
IV. CONCLUSION
By studying evolution of user activity graphs on three
popular third-party Facebook applications, we highlight their
similarities and distinguishing features with varying degrees
of gaming dynamics. Most of the graph properties studied
remain stable across time, with only the number of connected
components varying with falling application popularity (FC).
Heavy user-pairs are more strongly clustered on applications
with more gaming dynamics. Super-heavy and addicted users
only exist on our social game with 5.4% of the user base
accounting for 80% of total activity on the application.
With high storage and processing requirements for social
applications, our findings indicate a need to provisioning for
addicts on social games differently than general users of social
applications.
V. ACKNOWLEDGEMENT
This work is supported in part by AT&T Labs, Inc.
REFERENCES
[1] CHUN, H., KWAK, H., EOM, Y., AHN, Y.-Y., MOON, S., AND
JEONG, H. Comparison of online social relations in volume vs. inter-
action: A case study of cyworld. In Proc. ACM Internet Measurement
Conference (IMC) (2008).
[2] GOLDER, S., WILKINSON, D., AND HUBERMAN, B. Rhythms of
Social Interaction: Messaging within a Massive Online Network. In
International Conference on Communities and Technologies (2007).
[3] KUMAR, R., NOVAK, J., AND TOMKINS, A. Structure and evolution
of online social networks. In International Conference on Knowledge
Discovery and Data Mining (2006).
[4] MISLOVE, A., MARCON, M., GUMMADI, K. P., DRUSCHEL, P., AND
BHATTACHARJEE, B. Measurement and Analysis of Online Social
Networks. In Proc. ACM Internet Measurement Conference (IMC)
(2007).
[5] NAZIR, A., RAZA, S., AND CHUAH, C.-N. Unveiling facebook: A
measurement study of social network based applications. In Proc. ACM
Internet Measurement Conference (IMC) (2008).
[6] SCHNEIDER, F., FELDMANN, A., KRISHNAMURTHY, B., AND WILL-
INGER, W. Understanding online social network usage from a network
perspective. In Proc. ACM Internet Measurement Conference (IMC)
(2009).
[7] TANG, J., MUSOLESI, M., MASCOLO, C., AND LATORA, V. Temporal
distance metrics for social network analysis. In ACM SIGCOMM
Workshop on Online Social Networks (2009).
[8] TORKJAZI, M., REJAIE, R., AND WILLINGER, W. Hot today,
gone tomorrow: On the migration of myspace users. In ACM SIGCOMM
Workshop on Online Social Networks (2009).
[9] VALAFAR, M., REJAIE, R., AND WILLINGER, W. Beyond friendship
graphs: a study of user interactions in flickr. In ACM SIGCOMM
Workshop on Online Social Networks (2009).
[10] VISWANATH, B., MISLOVE, A., CHA, M., AND GUMMADI, K. P. On
the evolution of user interaction in facebook. In ACM SIGCOMM
Workshop on Online Social Networks (2009).
[11] Facebook: F8 conference and like button.
http://techcrunch.com/2010/04/21/facebook-like-button/, Apr 2010.
[12] Facebook developer platform. http://developer.facebook.com/, Apr 2008.
[13] WILSON, C., BOE, B., SALA, A., PUTTASWAMY, K. P. N., AND ZHAO,
B. Y. User interactions in social networks and their implications. In
ACM EuroSys (2009).