While psychologists analyze network game-playing behavior in terms
of players' social interaction and experience, understanding user
behavior is equally important to network researchers, because how
users act determines how well network systems, such as online games,
perform. To gain a better understanding of patterns of player
interaction and their implications for game design, we analyze a
1,356-million-packet trace of ShenZhou Online, a mid-sized commercial MMORPG.
This work is dedicated to draw out hints and implications of player
interaction patterns, which is inferred from network-level traces,
for online games.
We find that the dispersion of players in a virtual world is
heavy-tailed, which implies that static and fixed-size partitioning
of game worlds is inadequate. Neighbors and teammates tend to be
closer to each other in network topology. This property is an
advantage, because message delivery between the hosts of interacting
players can be faster than between those of unrelated players. In
addition, the property can make game playing fairer, since
interacting players tend to have similar latencies to their servers.
We also find that participants who have a higher degree of social
interaction tend to play much longer, and players who are closer in
network topology tend to team up for longer periods. This suggests
that game designers could increase the "stickiness" of games by
encouraging, or even forcing, team playing.
Design Recommendations, Internet Measurement, MMORPG, Online Games,
Overlay Networks, Social Interaction
With an exponentially growing population and the increasing
diversity of network gamers, the virtual worlds constructed by
MMORPGs (Massive Multiplayer Online Role Playing Games) are
gradually becoming a field for the study of social
behavior [5,15]. While psychologists
analyze network game-playing behavior in terms of players' social
interaction and experience, understanding user behavior is equally
important to network researchers, because how users act determines
how well networked systems, such as online games, perform. For
example, the dispersion of players across a virtual world affects
how well an algorithm performs in distributing the workload to a
number of servers in terms of bandwidth usage, load balancing, and
users' perceived quality of games.
To gain a better understanding of the patterns of player
interaction and their implications for game design, we analyze
how players interact. Analyzing user behavior based on
network-level traces is particularly useful for our purpose, since
the inferred user behavioral patterns would naturally connect to
network-level factors, such as IP addresses and network latency
between participating parties. Also, it is easier for commercial
game operators to provide network-level traces, because unlike
logging application-level activities, collecting traffic traces
does not increase the load of, or require modification to, game
Based on an empirical analysis of player interaction inferred from
network traces, this work aims to put forward architectural design
recommendations for online games.
We develop an algorithm that derives patterns of player
interaction from a 1,356-million-packet trace of
ShenZhou Online , a commercial MMORPG. The inferred
interaction patterns are then analyzed in the following aspects:
the dispersion of players in a virtual world, the correspondence
of network locality and in-game locality, and the "stickiness"
of game-playing in terms of social interaction. Our main objective
is to draw design implications of player interaction for online
games, especially system-level design issues. Our major findings
are as follows:
The dispersion of players in a virtual world can be well
modeled by Zipf-like distributions, where 30% of players gather
in the top 1% of popular places. This implies that static and
fixed-size partitioning of game worlds is inadequate for both
server-cluster and peer-to-peer
infrastructures [10,18], and dynamic
partitioning algorithms should therefore be
Players who are neighbors or teammates tend to be
closer to each other in network topology. This property is an
advantage to network games with either client-server or
peer-to-peer architecture, as the message delivery between the
hosts of interacting players can be faster. In addition, the
property improves the fairness of game playing, as
interacting players tend to have similar latencies to their
Players who have a higher degree of social interaction tend
to stay in the game world much longer. This suggests that
game companies could increase the "stickiness" of games by
encouraging, or even forcing, team playing. Furthermore, the
duration of group play correlates with a group's size and the
network distance between players. This implies that real-life
relationships carry over into the virtual world, and/or real-life
interaction plays a key role in game play. The latter also
suggests that enriching in-game communication would encourage
players to be more involved in team play.
Larger groups generally lead to longer collaboration due to
the enjoyment derived from player interaction and social bonds.
This suggests that a game could be made stickier by encouraging
the formation of large groups.
The remainder of this paper is organized as follows.
Section 2 describes related works. We introduce
the studied game and summarize the collected traces in
Section 3. In Section 4, we present
the methodology used in mining player-interaction from
packet-level traces. Next, in Section 5, we
analyze the group structure formed by players and discuss their
implications for game design. Finally,
Section 6 draws our conclusions.
2 Related Work
In , the authors observed player-to-player
interaction in two locations in the game Star Wars
Galaxies. They analyzed user interaction patterns, mainly
gestures and utterances, and discussed how they are affected by
the structure of the game. In , the authors
conducted an online survey to study the demographics of groups.
The relationship between demographic factors and the willingness
to participate in a group, or be a group leader, was analyzed. The
main result was that older players were more likely to prefer
playing solo, either because of schedule constraints or because
they simply preferred to play alone. In the PlayOn
project , the same authors analyzed the
relationship of grouping time with leveling time and the class and
levels of characters based on a set of population data for
World of Warcraft.
Similar to the above studies, this work also analyzes the
interaction between players in the game world. However, our study
is different from previous works in that it mainly focuses on
system design issues, rather than psychological factors or design
of game content. Our approach is also distinctive because it
infers players' group structure based on out-of-game
observations only. This strategy enables us to correlate in-game
user behavior with out-of-game variables, such as network latency,
so that we can evaluate system-level design alternatives from a
high-level perspective of user interaction.
Table 1: Summary of Game Traffic Traces
Pkt. (in / out / both)
Bytes (in / out / both)
342M / 353M / 695M
4.7TB / 27.3TB / 32.0TB
325M / 336M / 661M
4.7TB / 21.7TB / 26.5TB
† This column gives the kernel drop count reported by tcpdump.
‡ The reported kernel drop count was zero,
but we found that some packets were actually dropped at the
3 Trace Collection
ShenZhou Online is a mid-scale, commercial MMORPG that is popular in
Taiwan , where there are thousands of players
online at any one time. To play, the participants purchase game
points from a convenience store or online. A screen shot of ShenZhou Online is shown in Fig. 1. The character played by the
author is the man in the center of the screen with a smiling face
above him. He is in a typical market place, where other players
keep stalls. As is normal in MMORPGs, a player can engage in
fights with random creatures, train himself in special skills,
participate in marketplace commerce, or take on a quest.
Figure 1: A screen shot of ShenZhou Online
With the help of the ShenZhou Online staff, we set up a traffic monitor
beside the game servers. The monitor was attached to a layer-4
switch upstream of the LAN containing the game servers (we call
it the "game LAN"). The port forwarding capability of the
tapped layer-4 switch was enabled so that all inbound/outbound
game traffic was forwarded to our monitor as a copy. To minimize
the impact of monitoring, all remote management operations were
conducted via an additional network path, i.e., the game traffic
and management traffic did not interfere with each other. The
network topology and setup of the game servers and the traffic
monitor are shown in Fig. 2.
The traffic monitor was a FreeBSD PC equipped with 1.5 GHz
Pentium 4 and 256 MB RAM. We used tcpdump with the
built-in BPF to obtain traffic traces.
We randomly chose a subset of game sets in each trace, and only
packets belonging to the selected game sets were logged. A game
set, which is logically a "game server" from a player's
viewpoint, comprises an entry server, several map servers, and a
database server. All game sets are equivalent in content, but
isolated. The reason for providing identical game sets is
to distribute the workload over a number of servers with limited
game content, e.g., terrain, missions, and creatures in the
virtual world. We took two packet traces, N1 and N2,
which recorded traffic for two and three game sets, respectively.
The collected two traces, N1 and N2, which spanned 8
and 12 hours respectively and contained more than 1,356
million packets, are summarized in Table 1.
Figure 2: Network setup for traffic measurement
4 Deriving Player Interaction
In this section, we discuss how player interaction is derived from
packet traces. As logging in-game activities of all game
characters would place an extra burden on game servers, which are
already busy, inferring user behavior from network-level traces
offers a number of advantages:
The sampling does not increase the workload of game
servers, since packet traces can be recorded by a separate
No modification to game servers is required.
Player behavior inferred from traffic traces naturally
connects to network-level factors, such as IP addresses and
network latency between conversational parties.
The first two benefits are of particular importance for commercial
games, where overloading and possible instability caused by
"inessential tasks" (from the viewpoint of game operators) are
not tolerated. The last point, on the other hand, is especially
relevant to system-level design issues for online games. Because
of these advantages, we believe methodologies that derive in-game
user activities from out-of-game observations are valuable and
We infer player interaction based on the correlations
between packet arrival processes; so that the decision about
whether two players interact is based on observations over a
period of time.
To make the analysis tractable, we adopt a session-based
correlation analysis to determine the degree of interaction
between a pair of players. That is, two players are deemed to be
interacting if they maintain contact throughout the
overlapped time of their sessions; otherwise, they are considered
unrelated. This strategy is conservative, since some players may
interact with other players for a period of time, and be
independent hereafter. However, as our objective is to draw hints
and implications from player interaction for network game design,
the session-based analysis is sufficient as it is competent to
elicit the group structure formed by players.
In the following, we describe the terminology and methods used to
determine the relationships between players. Hereafter, we use the
terms "players" and "sessions" interchangeably.
Co-presence Time: The overlap of the sessions of two
or more players. Players are co-present if their
co-presence time is longer than ten minutes.
Neighbor: Two players are neighbors if they are
co-present and their characters are close throughout their
co-presence time. A player is called social if s/he has
a neighbor(s), and independent otherwise.
Partner: Two players are partners if they are
neighbors and act in synchrony throughout their co-presence time. A
player is called grouped if s/he has a partner(s), and
Horde: A set of players in which every two players
are neighbors if they are co-present. Each player in a
horde must have at least one neighbor in that horde. Players in a
horde always stay together-either at a site, or moving as a
cohort in the virtual world.
Group: This term is similar to "horde," except that
the interacting subjects are "partners" instead of "neighbors."
A group corresponds to a team of players, which is formed for
journey or certain quests. Players in a group always move and act in
Place: A relaxed definition of "horde." This is a
superset of one or many hordes that stay at the same location;
therefore, a place semantically corresponds to a location in the
To clarify the above terminology, the relationships between social
players, independent players, and group players are depicted in
Fig. 3. Note that social players and independent
players are complementary, and group players are a subset of social
Figure 3: The Venn diagram of player classification
4.2 Identification of Player Interaction
We have developed an algorithm to identify how players interact in a
virtual world from packet traces. The only input of this algorithm
is each session's packet arrival processes, which are formed by
counting the number of client data packets and server data packets
observed in successive 30-second periods. For the sake of brevity,
hereafter, the term "packets" refers to "data packets" unless
otherwise stated. Server packets primarily convey the state and
position updates of nearby characters; therefore, they possess the
property of spatial locality . In other
words, game servers release an approximate number of server packets
for the sessions whose characters are in the vicinity of each other.
We exploit this property to detect players who are close to each
other. On the other hand, client packets convey user commands;
therefore, the packets' transmission rate reflects the degree
of player activity. Based on this property, we treat a cohort of
players as a team if their activity levels are synchronous during
their co-presence time.
The pseudo code for determining the neighbors and partners of all
players is listed in Algorithm 1. The procedures ensure
that the relationships of neighbors and partners follow the law of
communication and the law of transition.
The identification of relationships proceeds in a pairwise fashion.
Two players who have server packet arrival processes Sa and Sb respectively during their co-presence time are deemed to be
neighbors if and only if they meet all the following conditions: 1)
there is a high Pearson's correlation coefficient between Sa and
Sb; 2) the ratio between mean(Sa) and mean(Sb) is small; 3)
the ratio between sd(Sa) and sd(Sb) is small, where
sd(·) denotes the standard deviation; 4) the 95% percentile
of |Sa−Sb| is small; and 5) when the law of transition cannot be
satisfied, neighbors with higher correlation coefficients are
Based on observation, we set the threshold of the correlation
coefficient condition to 0.6, the threshold of all ratio tests to
2, and the threshold of the deviation test to 2 pkt/sec.
Even the choice of thresholds affects the decision about
relationships, the algorithm has been re-run with different
thresholds and similar results were obtained. We argue that the
desired result-the qualitative property of player
interaction-holds as long as the thresholds are kept
within a reasonable range.
Once all neighbor-pairs have been identified, we determine whether a
pair of neighbors are partners according to their activity levels.
By the observation that the distribution of client packet rates is
bi-modal with a trough around 1.5 pkt/sec, in each epoch (30
seconds), a session is considered "active" if the average client
packet rate is above 1.5 pkt/sec, and considered "idle"
otherwise. Two players with co-presence time t epoches are treated
as partners if both are active for at least ⎡ t/2⎤ epoches, and their activity levels are the same for at
least ⎡ 0.9×t ⎤ epoches. The rationale
behind this judgement is that a team of players should be active or
inactive synchronously-it is unlikely that one player would rest
while his/her teammates are involving in fierce combat. Because many
players remained idle during most of their online time, we avoid
misidentifying them as a large "idle-team" by not including them
in any group. This is reasonable since players join groups for a
journey, not for a rest.
#1Identification of neighbors and partners
compute packet counting processes
Ci and Si for client data packets and
server data packets, respectively (sampled every 30 seconds)
AIi← Ci > 1.5 pkt/sec
Ω← all session pairs (a,b) that are
co-present and have Pearson's correlation coefficient ρ > 0.6
if is.neighbor(a,b) and
is.neighbor(a,b's all neighbors) and
is.neighbor(b,a's all neighbors) then
set a and b as neighbors
else continueend ifif is.partner(a,b) and
is.partner(a,b's all partners) and
is.partner(b,a's all partners) then
set a and b as partners
\rhd check coincidence between Sa and Sb
\rhd Sa and Sb refer to their intersectional portion
ratio.avg ← mean(Sa) / mean(Sb)
ratio.sd ← sd(Sa) / sd(Sb)
result← quantile(|Sa−Sb|, .95) < 2 and
(ratio.avg < 2 and ratio.avg > .5) and
(ratio.sd < 2 and ratio.sd > .5)
\rhd AIa, AIb refer to their intersectional portion
τ← co-presence time of session a,b
result← count(AIa=true) > 0.5×τ and
count(AIb=true) > 0.5×τ and
count(AIa=AIb) > 0.9×τ
After the relationship between each pair of players has been
determined, we divide players into clusters: players who are
neighbors (must be mutually) form a horde, while players who are
partners (must be mutually) form a group. Note that a player may
simultaneously belong to a number of groups, because s/he can form a
group with some players and form another group with some other
players in different periods. Finally, hordes containing the same
players are merged into a place. This operation forms disjoint
places, where each player uniquely belongs to a place.
To demonstrate the principle behind the identification algorithm, in
Fig. 4, we depict the packet arrival processes of a
four-player group. We can see that the fluctuations of server packet
arrivals follow very similar patterns during the co-presence time,
which is longer than 40 minutes. Meanwhile, the client packet
arrivals exhibit similar patterns in their activity levels. The
figure also shows that the duration of a group may be longer than
the session time of any member. In other words, even the members in
a group come and go, a group remains if at least two members are still
in the game.
Figure 4: Packet arrival processes of a four-player
5 Analysis of Player Interaction and Its Implications
In this section, we analyze players' interaction patterns and their
implications for network game design. We first present a summary of
the group structure, which is inferred using the
Algorithm 1 in Section 4. We then analyze
player interaction patterns and discuss their implications in a
number of aspects, including player density, network proximity,
session duration, and interaction levels.
5.1 Basic Statistics
Of the 16,528 players who had potential to interact with others,
i.e., players with session time longer than ten minutes, 67% were
social players, and 17% were group players. On average, a social
player interacted for 70% of his online time, while a group
player teamed up with others for 50% of his/her online time.
Meanwhile, a social player had 3.5 neighbors and a group player
had 1.4 partners on average, as shown in
Fig. 5 shows how players were scattered in
different places and groups. We can see that 40% had at least
four neighbors during the game, while 20% stayed in places where
more than one hundred players had gathered. The most popular place,
where 1,816 players gathered, shows that more than 10% of
players spent the whole session in the same location. On the other
hand, groups are frequent but mostly small-sized. Among 1,389
groups, 85% were "duos," and only 2% had more than four
members. We believe the prevalence of "duos" is not unique to this
game, as it reflects the fact that some players are not really
social and usually interact primarily with one other person in real
life. This echoes the following comments by gamers reported
"Distinct from `grouping' in
that [sic] (as I see it), the `duo-group' is most often the same two
people (perhaps with a relationship offline), who don't typically
group with people other than each other."
"My answers to questions in this vein would show I often
group, but it [sic] would likely be grouped with others who are
honestly much more social than I, since I predominantly duo with
Table 2: The average number of neighbors and partners of a
# of Neighbors
# of Partners
Figure 5: The distribution of groups and places
5.2 The Skewness of Place Populations
As the number of concurrent players in a MMOG are usually exceeding
tens of thousands, even a powerful server could not handle the vast
computational requirement of an entire game. A straightforward
solution for solving the scalability problem is to distribute the
workload among multiple servers. There are two common approaches:
the server-cluster approach [1,20], and the
peer-to-peer approach , both of which normally
divide the game world into a number of regions with each of which
may be managed by different servers. With this approach, the key to
an efficient load balancing depends on whether it can
correctly delegate a region (of the game world) to an
appropriate server, while minimizing the overall transmission
latency and bandwidth utilization and subject to the constraint that
no server is overloaded.
Furthermore, one of the deciding factors that affects the outcome of
region delegation is how players are distributed across the
virtual world. If players tend to be uniformly distributed, then a
straightforward fixed-size partitioning of the game world is
sufficient, given the players in each region is not too many to
overload any single server. On the contrary, if players tend to
aggregate in certain hotspots that change from time to time, then a
and adaptive partitioning algorithm must be used.
From the distribution of place popularity
(Fig. 5), there were a number of crowded places,
e.g., places where more than fifty players gathered, which indicate
the existence of hotspots. Further analysis shows that the
distribution of populations in different places can be well modeled
as the concatenation of two Zipf-like distributions .
Suppose a place with rank i and population pi follows a
Zipf-like distribution; then pi=C/iα, where C is a
normalizing constant and α > 0 is the power-law exponent, i.e.,
the population of a place is inversely proportional to its rank with
a log-log transform. The smaller the α, the heavier tail the
distribution will have.
Fig. 6 depicts the populations of places
versus their ranks on a log-log plot, where the points are
scattered along two line segments. This evidences that the
populations of the most popular twenty places follows a Zipf-like
distribution with α ≈ 1.4, while the populations of
the less popular places follows the same distribution with
α ≈ 0.5. Even the populations of popular places have a
lighter tail than a standard Zipf distribution (α = 1), the
most crowded hotspot has about 10% of the players aggregated,
and the most popular 1% of places (out of 7,712) contain
about 30% of the players. This manifests that the
dispersion of players is far from uniform and is closer to a
power-law distribution. The high variability in player density
across a virtual world implies that static world partitioning
strategies [10,18] are inadequate for MMOGs.
On the other hand, we remark that dynamic partitioning algorithms,
which adaptively adjust the number, size, and ownership of regions
based on the constantly changing player density, should be
employed for online games with either
server-cluster [14,2] or
peer-to-peer architecture [7,11].
Figure 6: Popularity versus rank of places
5.3 Network Proximity of Interacting Players
A number of peer-to-peer infrastructures for online games and
network virtual environments (NVEs) have been proposed in recent
years. However, a fundamental design question remains unsolved:
How should the participating hosts interconnect for better
networking performance? Currently, there are three common
approaches for constructing overlay networks: 1) numeric-ID
based ; 2) network topology-based ,
e.g., optimizing network latency; and 3) in-game
location-based [18,8]. Although the first
two approaches are adequate for general purpose applications, such
as file sharing, they may be less appropriate for NVEs. For
example, with a latency-optimized overlay, a message sent from a
host can be delivered to any other host in the network with
generally low latencies. However, if game players only
communicate with their neighbors in the virtual world, then a
naive approach, where the hosts of each pair neighbor are
interconnected, would achieve better performance in terms of
message transmission latency. A complete evaluation of the design
alternatives (i.e., different strategies to connect overlay nodes)
requires a detailed trace involving inter-host measurements and is
beyond the scope of this study.
Instead, using a comparative approach, we
investigate the degree of correspondence between the locality
in the network and the locality in the virtual world, which implies
the similarity of the overlay networks built with the second
approach and the third approach.
5.3.1 Similarity in Network Addresses
Fig. 7 shows the proportion that a pair of
players with a certain kind of relationship share the same IP
address prefix. We found that about 7% of partner-pairs
co-located in the same /16 network, whereas only 1.7% of
unrelated-pairs were in the same network. At the same time,
approximately 5% of partner-pairs used the same IP address, while
very few unrelated-pairs did so. This indicates that a proportion of
group members played at the same physical location (in an apartment
or Internet café) through a NAT device. Comparing the three
levels of relationship, we can see that players with a higher level
of interaction tend to be closer in network topology. These findings
suggest that overlays built on in-game localities correlate
with topology-aware overlays to some extent, i.e., whether the
overlay is based on in-game closeness or network topology does not
make so much difference.
5.3.2 Similarity in Network Latencies
Fig. 8 provides another view to examine the
network proximity of players. We use the difference in minimum RTTs
(round-trip times) and the difference in average RTTs between the
players' hosts to gauge their network distance from the perspective
of game servers. The figure shows that interacting players are much
closer to each other compared with unrelated players. This echoes
the agreement between in-game locality and network locality based on
Figure 7: The correspondence between IP addresses and
5.3.3 Advantages of the Property
Client-server based games would benefit from the network proximity
property of interacting players in terms of proxy placement
and fairness of game playing. A client-server architecture
with proxy support is a design that alleviates the single bottleneck
problem of, and retains the manageability of, the centralized server
architecture . In such architecture, players connect to
their nearest proxy server, which processes and responds to user
commands in place of game servers. Proxy servers are usually close
to users; thus, the transmission latency and the user-perceived
response time could be significantly reduced. However, additional
proxy-to-proxy communication is required when players who connect to
different proxy servers interact. In this case, the overall message
delivery latency could be longer due to detours between proxys. To
provide high responsiveness and interactivity, two seemingly
contradictory conditions need to be met: 1) players should connect
to their nearest proxy server, and 2) players who are neighbors in
the game should connect to the same proxy server. Fortunately, the
network proximity property of interacting players resolves the
contradiction between the above conditions. The reason is that
players are likely to connect to the same proxy as their in-game
neighbors (with a non-significant probability), even if proxy
selection is only based on the network distance.
Fairness is also an important issue in network game design. Normally
there is a bias against players with longer latencies (between
clients and servers), since their screens tend to update later than
others if the server releases an update message for all participants
at the same time. Furthermore, the actions of players who are far
from servers are likely be processed (by servers) later than others,
even if all commands are issued simultaneously (on their respective
In the case of client-server based games, a set of players can have
fairer play if their network latencies to game servers are
similar. It is because that servers will process their actions
approximately at the same time if they trigger commands
Thus, the network proximity for interacting players makes their
interaction, such as fighting each other, fairer, as their network
latencies to game servers are similar.
For the above reasons, we consider that the network proximity of
interacting players is a "benign" property, as it is favorable to
the design of network games with both peer-to-peer and client-server
architectures. We remark that as player interactions are generally
spatially localized, the mapping of spatial locality between
the game world and the network topology would be rather important
and could be a key to the optimization of network latency in online
Figure 8: The correspondence between round-trip times and
5.4 Game Playing Time and Social Interaction
As social interaction is one of main attractions of
MMORPGs , a factor that encourages time
investment and personal attachment, one may ask: Does social
interaction "tie" players to a game and encourage them to play
longer than independent players who do not have any interaction with
To answer this question, we plot the survival
curves  for sessions grouped by interaction levels,
and check the significance of the differences between the groups. In
Fig. 9, the survival curves for three groups of
sessions are plotted. Visually, the curves diverge significantly
from each other; and the log-rank test reports
p=1−Prχ2,2(1532) ≈ 0, which indicates the sessions in
different groups are far from equivalent. The median session time
for independent players, social (but solo) players, and group
players is 40, 112, and 190 minutes, respectively, which gives
a high ratio of nearly 5:1 for group players and independent
players. This figure implies that there is a strong relationship
between social interaction and time investment in a game. Game
designers, therefore, may provide more facilities to encourage
teamwork, or even force players, to form groups for quests, which
would increase the "stickiness" of a game and eventually generate
more revenue through subscription fees.
Figure 9: Comparison of game session duration between players
with different levels of interaction
Figure 10: The relationship between grouping time and
various related factors
5.5 Degree of Group Interaction
Now that the existence of network proximity and the tendency of
group players to be "tied" in a game have been established, in
this subsection, we focus on group players and investigate the
relationship between the level of group interaction and some
potentially relevant factors. We will show that the former is
related to the degree of network proximity and group
size. To quantify the level of group interaction, we define the
"group time" of a group as the average pairwise co-presence
time among its members.
We plot the relationship between group time and three potentially
relevant factors in Fig. 10. As can be
seen in Fig. 10(a), the closer
group members were in network topology, the longer they played
together. The property of network proximity can also be examined
in terms of the network latency between clients and servers. The
smaller the difference in minimum RTT experienced by group
members, the longer the group playing time they shared, as shown
in Fig. 10(b). Assuming that network
proximity reflects geographical locality, the above relation
implies that 1) real-life relationships carry over into the
virtual world, and/or 2) real-life interaction plays a key role
in game playing. The latter explanation suggests that enriching
in-game communication supports, e.g., audio and video chat
facilities, would encourage players to be more involved in team
Fig. 10(c) shows that players in larger
groups tend to stay longer. This phenomenon can be explained by the
enjoyment derived from interaction and social bonds.
For example, the latter may prevent a player leaving a game
prematurely, as it would affect the group's adventure. This suggests
that encouraging players to form larger groups would also increase
the "stickiness" of the game. However, the effect diminishes for
groups with more than five members, possibly because the role of
each player in a large group is less important. In other words, the
social bond is weak when a player leaves a large group than leaving
a smaller group.
In this paper, we have analyzed player interaction for ShenZhou Online, a
mid-sized commercial MMORPG, and drawn out a number of hints and
implications for network game design. The analysis reveals that the
dispersion of players in a virtual world is heavy-tailed, which
implies that static and fixed-size partitioning of game worlds is
inadequate. We have shown that neighbors and teammates tend to be
closer to each other in terms of network topology, a property that
is beneficial to both client-server and peer-to-peer based games,
because message delivery between the hosts of interacting players is
faster. In addition, this property makes games fairer, as
interacting players tend to have similar latencies to their servers.
We have also found that participants who have a higher degree of
social interaction tend to play much longer than independent or solo
players. This suggests that game companies could increase the
"stickiness" of games by encouraging, or even forcing, team
playing. Furthermore, the duration of group play correlates with the
size of the group and the network distance between the players.
Specifically, players who are closer in network topology tend to
team up for longer periods. Larger groups generally lead to longer
collaboration due to the enjoyment derived from interaction and
social bonds, which suggests that games could also be made stickier
by encouraging the formation of such groups.
We believe that only through observation can we fully understand
what users desire and therefore be able to design appropriate
network systems for them. For instance, via an inspection of how
players react to network conditions, we can understand how players
perceive unfavorable network QoS (e.g. packet loss and delay
jitters are more intolerable than network latency). Such
understanding could be very helpful in the design of network game
systems, especially in the message delivery subsystem, to diminish
the impact of unavoidable network impairment on players as
possible . Designing more efficient network
gaming systems which take account of user behavior and perception
and be optimized for user satisfaction will be one main theme of
our future work.
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