Capturing Interactions in Face-To-Face Social Networks
Francesco Ficarola and Andrea Vitaletti
Department of Computer, Control, and Management Engineering,
Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy
Keywords:
Mobile, Interactions, Social Networks, RFID, Proximity, Face-To-Face.
Abstract:
Online social networks, formed by cyber interactions between users, are nowadays explored in a number of
papers. In this work, we present our experimental activity on Face-To-Face (F2F) social networks tracing
physical interactions of humans in real-world scenarios. We briefly present the technologies to observe F2F
social networks focusing on the SocioPatterns platform that we have employed in our real-world experiments.
Motivated by the requirements of heterogeneous applications, we discuss how to tune the SocioPatterns col-
lection protocol parameters in order to better capture fast interactions between users; carried out experiments
confirm the effectiveness of such tuning.
1 INTRODUCTION
The participation in online social networking is con-
tinuously growing and more and more people use
websites such as Twitter, Facebook or Google+. This
new online phenomenon has led to a greater atten-
tion of the research community to social networks,
also motivated by relevant application scenarios, such
as reputation management (Yu et al., 2010), recom-
mendation systems (Adomavicius and Tuzhilin, 2005;
Walter et al., 2008) or information sharing platforms
such as Quora. However, a new trend involving our
“real life” relationships is becoming more and more
contemporary due to the continuous development of
new devices capable to track physical interactions. In
this paper we are interested in Face-To-Face (F2F)
social networks, namely networks made of evolving
graphs in which nodes are humans and edges between
nodes dynamically appear whenever a F2F interaction
between humans takes place. Over the last decade,
some papers such as (Hui et al., 2005), have focused
on tracking physical proximities, however the size
of those experiments is relatively small and some of
them were deployed employing unsuitable technolo-
gies; so far, the main reason behind the lack of anal-
ysis of real-world interactions is mainly due to hard-
ware limitations (e.g., bluetooth is too inaccurate to
obtain an efficient face-to-face measure between peo-
ple). One of the first attempt to effectively track face-
to-face interactions has been made by the SocioPat-
terns researchers (soc, nd; Barrat et al., 2008) using
the Radio Frequency IDentification (RFID) technol-
ogy. We believe SocioPatterns tags are among the
most promising devices to track F2F interactions in
real-world scenarios. For this reason, our experi-
ments have been conducted using such devices with
the main purpose of tuning the multiple access con-
trol (MAC) (Wu and Pan, 2008) protocol programmed
into the tags to better suit heterogeneous application
scenarios. Notice that, standard MAC protocols for
Wireless Sensor Networks (WSNs) are not suitable in
real-world social scenarios where the dynamics of in-
teractions are extremely fast and unpredictable when
compared to the WSNs ones.
Contribution. The ability of capturing F2F interac-
tions opens the possibility of quantitatively exploring
the way in which humans interact in the real world.
In this paper, we compare
1
the SOCIOMAC protocol
proposed by the SocioPatterns initiative and already
used in monitoring activities, with the new PROXMAC
protocol. Our experimental results show that a proper
tuning of the protocol parameters can better support
the rich heterogeneity of F2F real-world scenarios and
guarantees an effective trade-off between interaction
resolution and network life-time.
Roadmap. In the next section we introduce more for-
mally F2F social networks, presenting some of their
applications and the technologies employed to track
the interactions. In section 3 we discuss the issues
in tracking F2F interactions in heterogeneous applica-
1
This work has been possible thanks to the cooperation
with the SocioPatterns collaboration (soc, nd).
613
Ficarola F. and Vitaletti A..
Capturing Interactions in Face-To-Face Social Networks.
DOI: 10.5220/0005432206130620
In Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 613-620
ISBN: 978-989-758-106-9
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
tion scenarios and the corresponding MAC protocols.
In section 4 we show the results of the experiments
conducted on F2F social networks and on a simula-
tion environment to test the performance of the pro-
posed MAC protocols. Conclusions are discussed in
section 5.
2 F2F SOCIAL NETWORKS
A Face-To-Face (F2F) social network is a dynamic
network made by linking nodes (i.e., humans) that in-
teract at short range (e.g., 1-1.5 meter of distance) for
a sufficient amount of time to potentially exchange
meaningful information. More formally, a F2F social
networks is a dynamic evolving network G made of a
finite sequence G
0
, G
1
, G
2
, . . . , G
t
of t static networks
over the same vertex set V and a variable edge set E
i
(Avin et al., 2008); a link connecting nodes hu, vi E
i
iff at least a F2F interaction between u and v occurred
in the interval in time between G
i
and G
i+i
. We call
the resolution of the network.
Technologies. At present, there are a limited num-
ber of solutions able to track interactions between in-
dividuals in a distributed way that include sensors and
wireless technologies, such as Bluetooth, WiFi and
RFID. One of the first experiments to collect infor-
mation from a real group of people was made in (Hui
et al., 2005), where 54 individuals participating in a
conference were given Intel Imote devices, equipped
with a Bluetooth radio to discover nodes in proxim-
ity. However, Bluetooth does not allow a fine-grained
recording of social interactions because of two rea-
sons: 1) the discovery process to identify potential
F2F neighbors is slow and 2) since the radio range is
5-10 meters, it is possible to record as “social in-
teraction” the simple fact of being in the same room,
even if the users are not interacting in any way.
Jayagopi et al. (Jayagopi et al., 2010) deployed
some experiments involving 24 groups of 4 members
each. Every participant was asked to wear a sociomet-
ric badges capable of recognizing speech activity and
line-of-sight presence. However, all the experiments
performed in (Jayagopi et al., 2010) are based on rel-
atively small groups in which interactions are limited
inside the group, while we are interested in a technol-
ogy to study the dynamics of larger groups in which
members are free to move.
SocioPatterns is an interdisciplinary research col-
laboration that supports the development of the So-
cioPatterns Sensing Platform, an infrastructure in-
cluding new experimental RFID sensors that can be
worn by humans in order to track their mobility and
F2F interactions in real-world scenarios. The So-
cioPatterns platform is made of two main entities: ac-
tive RFID tags and RFID readers. When two per-
sons are in proximity within 1-1.5 meter (see Fig-
ure 1), their tags exchange proximity packets con-
taining their IDs (process 1) so that each tag is able
to know who is talking to. Eventually, tags send the
received proximity packets to close-by readers (pro-
cess 2), which in turn will forward those messages
to a central server running a UDP logger (process
3). Proximity ranges can be controlled via firmware
by setting the transceiver’s transmission at 4 differ-
ent levels of powers: 0, 6, 12, 18 dBm. Low-
power transmissions entail lower ranges of proximity.
The SocioPatterns platform has proved to work well
in a number of F2F application scenarios that are dis-
cussed in the next section. For this reason the experi-
ments presented in this paper have been implemented
using such a platform.
Figure 1: The communication process.
Applications. SocioPatterns performed several in-
stallations in different social contexts analyzing the
obtained dynamics, such as the study of relationships
between attendees in conferences (Barrat et al., 2010;
der Broeck et al., 2010) or the spread of disease in
hospitals and schools (Isella et al., 2011; Vanhems
et al., 2013; Stehl
´
e et al., 2011). One of the most
important work is certainly Live Social Semantics
(LSS) (Barrat et al., 2010), which is an applications
that relates virtual and real interactions among indi-
viduals during conferences. Furthermore, since sens-
ing human behaviors in real-world scenarios opens
new frontiers in ubiquitous areas, SocioPatterns have
started studying and analyzing characteristics in this
kind of social networks (Barrat et al., 2013). Anal-
ogous experiments to the SocioPatterns’ ones were
deployed by Chin et al. (Chin et al., 2012) giving
each person an active RFID badge during the course
of a conference. They were interested in realizing
a system able to find and connect people to each
other. Analyzing such an experiment, they discov-
ered that more proximity interactions result in an in-
creased probability for a person to add another as a
social connection. Finally, Becchetti et al. deployed
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two large-scale social experiments (Becchetti et al.,
2012), one at the Department of Computer Engineer-
ing in Rome and another one at the MACRO museum
during the opening of the NEON exhibition. They
collected data coming from active RFID tags worn by
around 120 volunteers normally moving and interact-
ing in the monitored areas.
3 CAPTURE F2F INTERACTIONS
A number of protocols nowadays exists for wireless
sensor networks applications (Demirkol et al., 2006),
opportunistic networks contexts (Conti et al., 2010)
or mobile ad hoc networks (Taneja and Kush, 2010).
Nevertheless, most of those protocols are not well
suited to track fast-changing F2F networks where in-
teractions are very dynamic and potentially might
change the structure of the network at every time-step.
We stress that the main purpose of our experiments is
to accurately track F2F interactions rather than to al-
low users to exchange messages. In this perspective,
the proposed MAC protocols are primarily designed
to quickly exchange small packets to build a neigh-
borhood map rather than to exchange relatively big
packets.
In this section we introduce SOCIOMAC and
PROXMAC, two MAC protocols specifically designed
to track F2F interactions, and we discuss how the
proper tuning of the protocol parameters can allow
us to effectively meet the needs of heterogeneous F2F
tracking scenarios. In particular, depending on the ex-
pected dynamics of the underlying social graph (e.g.,
the interaction time), the protocols have to strike a
balance between accuracy and network life-time.
SocioPatterns tags were initially provided with
SOCIOMAC (soc, 2014) that has been designed for ex-
periments characterized by long-lasting F2F interac-
tions. SOCIOMAC allows to identify nodes in the prox-
imity and to deliver the position and the list of en-
countered nodes to the readers. Communication is en-
crypted to support user privacy and packets are broad-
cast to the readers in a best-effort fashion with no ac-
knowledgment mechanism to minimize the overhead
of the communication. The three types of packets
used by the protocol are: contact (or proximity), bea-
con (or sighting), report. Contact packets are used
to identify close-by tags. This information, as well as
the position of the node, is then delivered to the reader
using the report and beacon packets, respectively.
Figure 2 illustrates an entire phase of the default
SOCIOMAC protocol. Each phase, composed of 8 cy-
cles, represents the main policy of the protocol. Every
cycle starts with a random sleeping period S, between
Figure 2: The SOCIOMAC protocol.
50 and 91 ms, to avoid periodic collisions, then it is
concluded alternatively by the transmission of con-
tact packets TX(C) or by the transmission of beacon
packets TX(B). Before performing a TX(C), tags go
in receiving mode RX for 5 ms so as to receive con-
tact packets from possible neighbors. A phase is con-
cluded with the 8th cycle, when a report packet TX(R)
is sent to the reader. It can be computed that the aver-
age duty cycle of SOCIOMAC is less than 10% making
this protocol suitable for long-lasting deployments;
however the probability P
succ
of exchanging a con-
tact packet between two neighbors in a phase “only”
ranges between 10% and 17%.
According to the analysis and evaluation given in
(Cattuto et al., 2010), a resolution of = 20 seconds
is considered sufficient to accurately track F2F inter-
actions and to consequently assess proximity. More
specifically, a contact between two individuals is es-
tablished with a probability in excess of 99%, namely
there is a link in G
i
, as long as at least one of them
receives a contact packet from the other every 20 sec-
onds. However, such a time scale could be not suffi-
cient in some cases. In particular, we consider a sce-
nario to investigate the so called “wisdom of crowds”
(Surowiecki, 2004), where a group of people is asked
to answer a set of questions in two separate phases. In
the first phase participants have to guess the answer
without having any kind of relationship with others,
while in the second phase they are allowed to interact
and exchange their opinions. Since users are already
aware of the questions asked in the first phase, in the
second round fast interactions, such as “What did you
answer the first question?”, are likely to happen. In
this case, a higher resolution (i.e., a lower time scale,
such as 5 seconds) may be required in order to cap-
ture all those kinds of interactions, consequently it is
crucial a fine-tuning of the SOCIOMAC parameters to
increase P
succ
.
Figure 3: The PROXMAC protocol.
CapturingInteractionsinFace-To-FaceSocialNetworks
615
Since in short-lasting deployments energy con-
straints are less demanding and interactions tend to
be very fast and dense, at the cost of a higher duty
cycle, the first tuning that had to be made was the in-
crease of the receiving interval so as to collect with
a higher probability incoming packets from fast inter-
actions. We recall that the SOCIOMAC protocol sets
the RX interval to 5 ms; because the transmission of a
packet approximately lasts the same order of magni-
tude, the protocol is not always able to capture packets
in their first exchanges. For that reason, we decided
of redesigning the main policy of the protocol, named
PROXMAC and shown in Figure 3, so that a fine-tuning
of the parameters would have been achievable. In the
next section we will show how P
succ
increases, mak-
ing this protocol more suitable for detecting fast dy-
namics. Values of the sleeping period and the receiv-
ing interval can be configured according to the sce-
nario which is going to be deployed.
A sketch of the phase is shown in Figure 3. Notice
that, in this new policy, the report packet can be sent
after every receiving operation only if the tag has pre-
viously received some contact packets, otherwise this
transmission is skipped. This feature guarantees com-
munications broadcasting only useful packets and re-
ducing contingent collisions towards the readers. Fur-
thermore, to better discriminate F2F links, TX(C) are
transmitted with signal strengths 0 and 1, namely 18
and 12 dBm, while the default SOCIOMAC proto-
col uses values of 1 and 2, making it more prone to
false positives. Finally, a tag running PROXMAC sends
a beacon packet only every t phases, so as to avoid to
incessantly flood the channel. The t parameter is usu-
ally configured to receive at least one beacon packet
at each time-step. That ensures a continuous moni-
toring of the tag inside the network. Both the beacon
and report packets are sent using a signal strength of 3
in order to ensure with a good probability the collec-
tion of the packet by close readers. Some other minor
optimizations of the protocol have been implemented
but will not be discussed in this paper for the sake of
space.
Of course, depending on the chosen parameters,
we can experience with different duty cycles and
probabilities P
succ
of successfully receiving a packet.
As sample of setting, also used in the experiments dis-
cussed in the next section, we pick out a sleeping pe-
riod ranging in [20, 30) ms and a receiving interval
in a range between 30 and 39 ms. Such values, as
well as other settings, were tested in our labs and, af-
ter a long time of measurements, we found that such
a configuration was a good compromise in terms of
performance for our purposes and social experiments.
Indeed, we were interested in deploying social exper-
iments where a high number of iterations of the main
phase in a time-step was required to capture fast in-
teractions as much as possible in dense real F2F so-
cial networks. Other values of S and RX are clearly
available and allowed; they only depend on the pur-
pose of the deployment. However, you should take
into account the fact that the minimum value of the
receiving interval should always be equal or greater
than the maximum value of the sleeping period, so
that the PROXMAC policy is still guaranteed. Indeed, if
that condition is not fulfilled, then it may happen that
a whole receiving interval completely falls in the mid-
dle of the sleeping period, thus compromising P
succ
.
As we will better see in the next section, using
those parameters, PROXMAC can almost always en-
sure the reception of at least one proximity packet
every 2 or 3 time-steps if two tags are facing each
other at 1-1.5 meter. Therefore, people having very
fast interaction can be now tracked and logged. Un-
like SOCIOMAC where the recommended resolution
is = 20 seconds and the maximum signal strength
for collecting contacts is 2, PROXMAC can exploit a
more fine-grained and better manage false positives
thanks to a reduced signal strength. An improvement
of the performance even depends on the new length of
the phase being very short, so that the protocol can it-
erate the main cycle several times within a time-step,
which is the most fine-grained resolution.
4 EXPERIMENTS AND
EVALUATION
In this section we evaluate the SOCIOMAC and
PROXMACs performance under different conditions in
real-world testbeds.
4.1 Preliminary Experiments
The first experiment measures the number of contact
packets exchanged in 10 minutes between two F2F
tags 1 meter apart and then collected by a reader.
This kind of setting is used to simulate an interac-
tion as happens in real-world scenarios. Of course, in
real-world testbeds, communications are more chal-
lenging with respect to this preliminary experiment
because of overlapping transmissions and dense net-
works. However, this first experiment allows us to fig-
ure out how protocols perform in an ideal setting, so
that we can understand when to use determined set-
tings in real-world social networks in which perfor-
mance measures are too difficult to explore and an-
alyze in detail. The plots depicted in Figure 4 show
the results of the experiments. The x-axis represents
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the time of the testbed, while the y-axis the total num-
ber of distinct proximity packets collected by both the
protocols. Notice that, the slope of both the plots is
less than 45 degrees. This means that for both the
protocols, a resolution of 1 second (i.e., the minimum
time-step in real-world scenarios) cannot be achieved.
However, as expected, PROXMAC provides better per-
formance in terms of proximity collection. In this
simple setting it is able to support an average reso-
lution of = 1.8 seconds, while SOCIOMAC supports
a resolution of = 2.9 seconds.
Figure 4: All the packets exchanged over time.
The second experiment is shown in Figure 5 and
is used to evaluate the number of false positives re-
ported by both the protocols, namely all those con-
tact packets exchanged by two tags not facing each
other. In this experiment two couples of nodes are
placed at a distance of 2.5 meters. As in the previ-
ous experiment, the two tags in a couple are 1 meter
apart. In principle, a tag should detect only the other
one in the couple (i.e., a F2F interaction). However,
due to the proximity of the two couples, false posi-
tives may occur. Of course, similar behaviors occur
in real-world scenarios whenever there are high net-
work densities. For instance, it can happen that two
independent groups of people at close range wrongly
exchange contact packets.
Figure 5: Correct contacts and false positives.
As shown in Figure 5(b), most of the PROXMAC
contact packets are correctly exchanged (blue arcs)
between the tags in the couple. On the contrary, that is
not the same for SOCIOMAC (see Figure 5(a)), where a
number of false positive occurs (red arcs). The thick-
ness of the edges is proportional to the number of con-
tact packets exchanged between the tags. A ticker
edge indicates a longer interaction, while a thin arc
shows a short-lasting interaction. This feature helps
to visualize what kinds of contacts were common. In
the SOCIOMAC case, the formed network is the re-
sult of 5 edges, including two regular interactions and
three false positives. The two more thin false posi-
tives could be excluded using a filter that analyzes the
structure of the network enforcing a certain thresh-
old, but the third arc has a thickness pretty similar
to one of the two regular. Therefore, there is a sig-
nificant problem in selecting which arc to keep and
which arc to discard. The main cause of this issue
in SOCIOMAC depends on the signal strength used by
the protocol and the corresponding policy. As already
described in section 3, SOCIOMAC allows tags to ex-
change contact packets using three level of power, in-
cluding the signal strength 2. This brings to a wider
range of communication catching farther tags which
cannot be considered face-to-face. This issue is rather
reduced using PROXMAC. Indeed, the only false pos-
itive registered during the experiment was the thin
edge between the nodes 1014 and 1244. However,
when the thickness of false positives is quite thin with
respect to other regular edges, then that arc can be
easily rejected using some techniques of filtering in
post-processing.
4.2 Real-World Social Experiments
WSDM 2013 Conference. On February 2013 we
deployed a real-world social experiment in Rome
during the WSDM Conference, where 69 attendees
agreed to wear our tags running the SOCIOMAC proto-
col. The experiment lasted more than 1 hour, where
50 minutes were allocated for the social interaction,
thus collecting data from a large area, including sev-
eral rooms, the corridor and common spaces. The
main purpose of the experiment was to collect data
to study how F2F interactions can possibly influence
the wisdom of a group of people, which is usually
called the “wisdom of crowds” phenomenon (Lorenz
et al., 2011). People were not constrained to talk with
a restricted group of other participants, but they were
allowed to interact with anyone they wanted.
At the end of the experiment we collected more
than 23000 single proximity packets. Figure 6 shows
the social network built aggregating all graphs col-
lected over time. Each of them was built with a reso-
lution of = 3 seconds and an edge threshold of θ = 5
proximity contacts. This means that an edge was built
only if at least 5 proximity packets were collected,
each of which had to be captured within a 3-second
CapturingInteractionsinFace-To-FaceSocialNetworks
617
time interval. Such a resolution was chosen accord-
ing to the results obtained in section 4.1, while that θ
empirically gives a good chance to record a real inter-
action. Indeed, assuming to choose a lower θ, an edge
may be established by the simple fact that two users
can bump into each other just for a moment. Darker
and bigger nodes of the graph represent people that in
the whole experiment accumulated the bigger number
of F2F interactions, while the thickness of an edge is
proportional to the number of interactions observed
between two nodes over the whole time.
Figure 6: The social graph of the network formed during
the experiment in Priverno.
The graph is made of 69 nodes and 133 undirected
edges. The average degree is 3.855, while the net-
work diameter and the average path length are 7 and
3.222, respectively. Only 6 nodes are isolated, while
the maximum degree is 13. Finally, the average den-
sity of the graphs in the evolving network is 0.003.
Priverno’s Country Fair. Similarly to the previous
experiment and with the same modalities, on May
2014 we deployed another real-world social experi-
ment in Priverno (LT), Italy, during a country fair,
recruiting 60 volunteers. As usual, each participant
wore a tag, but this time running the PROXMAC proto-
col. They were free to move within a large monitored
area (around 10 x 15 meters) in a green park. The to-
tal experiment lasted around 30 minutes to study the
same social phenomenon of WSDM. The interaction
part of the experiment lasted around 10 minutes, time
in which we collected more than 11000 single prox-
imity packets. The graph in Figure 7 depicts the ag-
gregated result of the social interaction. Since the pre-
liminary experiments showed that PROXMAC was able
to collect almost twice the number of proximity pack-
ets than SOCIOMAC, in this experiment we leave a res-
olution of = 3 seconds, but we set an edge threshold
of θ = 10. The same resolution allowed us to compare
this experiment to the previous one, while a double
Figure 7: The social graph of the network formed during
the WSDM experiment.
edge threshold tested the performance of PROXMAC
measured in the ideal experiments.
The graph formed of 60 nodes and 128 undirected
edges has an average degree of 4.267, a network di-
ameter and the average path length of 9 and 3.713,
respectively. 7 users result isolated, while the maxi-
mum degree is 10. Finally, the average density of the
graphs is 0.012. Despite this network is quite similar
in terms of size with respect to the WSDM’s one, we
can notice how the Priverno’s density is an order of
magnitude greater than the WSDM’s one. This fact,
besides a likely higher participation, is also due to
PROXMAC which could collect many more proximity
packets. Clearly, these small real-world social net-
works are only a starting point to test such collection
protocols. For this reason, in the next section, we sim-
ulated the behavior and the performance of both the
protocols in larger graphs and higher densities.
4.3 Simulation on Larger and Denser
Graphs
A simulation on graphs having many more nodes
and higher densities allows us to analyze what kind
of situation we may encounter in future and bigger
real-world scenarios. First of all, we remind that
SOCIOMAC sends report packets to the readers only
after 8 cycles, while PROXMAC can report after ev-
ery receiving operation if new contact packets have
been received. Due to this policy, in dense networks
SOCIOMAC can more easily experience a buffer over-
flow (tags have room for only 4 packets) and conse-
quently discard new incoming contacts. We set up a
simulation for different graph densities and sizes for
a total of 1200 time-steps. For each step, we gener-
ate a random graph based on the Erdos-Renyi model
(Erds and Rnyi, 1960), with n = {60, 100, 200, 500}
and p = {0.01, 0.02, 0.05, 0.1}, where n is the num-
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Figure 8: The percentage of lost packets for different graph densities and number of nodes.
ber of nodes and p is the probability of including an
edge in the graph. Notice that for a sufficient large
amount of time-steps, the probability p approximates
the average density of the whole evolving network.
Figure 8 shows the results of our simulation.
The equivalent case to the real-world experiment
deployed in Priverno is depicted in Figure 8(a), where
the number of nodes is 60 and the density is 0.01.
PROXMAC turned out to be very efficient in this case,
where the percentage of lost packets is around 7%.
Vice versa, SOCIOMAC was not able to collect almost
the 40% of the total possible packets. A similar be-
havior can be observed also in the other cases con-
sidered in the simulation where PROXMAC is able to
better support denser graphs. Indeed, we recall that
PROXMAC has a possible forwarding action after ev-
ery receiving operation, while SOCIOMAC forwards
its contact packets only every 8 cycles. Also, the
whole phase lasts less than the SOCIOMACs one. This
brings to a faster actions cycle in PROXMAC, which
is able to execute each operation several times in ev-
ery time-step. Still considering the plot with n = 60,
PROXMAC has a similar percentage of lost packets for
higher densities as well, while the SOCIOMACs trend
slightly increases. Their behavior changes little for
the graph having 100 nodes, but increases in graphs
with n = 200 and n = 500. However, for a density
of 0.01, PROXMAC was able to maintain a very low
level of loss. On the contrary, SOCIOMAC starts with
a level of loss equals to 40% and 60%, respectively.
Then, their trends increase and reach more than 80%
for SOCIOMAC with a density 0.1, while PROXMAC
amounts to 40% in n = 200 and 60% in n = 500. We
recall, however, that very high densities are unlikely
in real-world scenarios, as already observed in our so-
cial experiments.
5 CONCLUSION AND FUTURE
WORK
The ability of capturing F2F interactions opens the
possibility of quantitatively exploring the way in
which humans interact in real-world scenarios. The
heterogeneity of such scenarios, requires a proper tun-
ing of the collection protocols in order to achieve the
most effective trade-off between resolution and net-
work life-time.
In this paper we compared the SOCIOMAC protocol
proposed by the SocioPatterns initiative and already
used in monitoring activities, with the new PROXMAC
protocol, specifically designed to support the moni-
toring of fast-changing interactions. The experimen-
tal results, performed both in real-world social exper-
iments and in denser and bigger simulated networks,
confirm that PROXMAC can collect twice the number
of proximity packets than SOCIOMAC, thus provid-
ing a better resolution for capturing fast interactions.
Thanks to an optimized protocol policy, PROXMAC de-
livers report packets more efficiently than SOCIOMAC,
which suffers in denser networks.
In our vision, the collection of reliable F2F in-
teractions will support a better understanding of
the so-called “wisdom of crowds” phenomenon
(Surowiecki, 2004). Indeed, all the proposed models
in this research area (such as (DeGroot, 1974; Fried-
kin and Johnsen, 1990; Bindel et al., 2011)) rely on
the availability of interaction graphs.
In the future, we plan to extend the real-world
testbeds considering a higher number of individuals
in order to study bigger and denser networks.
CapturingInteractionsinFace-To-FaceSocialNetworks
619
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