=Paper=
{{Paper
|id=Vol-1452/paper4
|storemode=property
|title=Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?
|pdfUrl=https://ceur-ws.org/Vol-1452/paper4.pdf
|volume=Vol-1452
|dblpUrl=https://dblp.org/rec/conf/aist/ErfanGSS15
}}
==Approximating Social Ties Based on Call Logs: Whom Should We Prioritize?==
https://ceur-ws.org/Vol-1452/paper4.pdf
Approximating Social Ties Based on Call Logs:
Whom Should We Prioritize?
Mohammad Erfan, Alim Ul Gias, Sheikh Muhammad Sarwar, and Kazi Sakib
Institute of Information Technology
University of Dhaka, Dhaka–1000, Bangladesh
{bit0326,alim,smsarwar,sakib}@iit.du.ac.bd
Abstract. Telecommunication service providers could analyze the call
logs of individual users to provide personalized services like recommend-
ing Friends and Family (FnF) numbers, that could be registered for hav-
ing lower tariff during communication. However, suggesting such num-
bers is not simple as the decision making process can often be misled
by factors like overall talk-time or call frequency. This paper proposes
a technique to assist mobile phone users for effectively identifying FnF
numbers using the underlying social network of a call graph. It resem-
bles to a star network, having the user at the center, where each of the
edges are weighted using three call log attributes: call duration, call lo-
cation and call period. The weighted call graph is then used to calculate
user’s social closeness based on which FnF numbers are suggested. The
experiment was conducted on a set of real life data and it is seen that
the proposed technique can effectively suggest FnF numbers with an F-
measure value of 0.67.
Keywords: Social network, mobile phone call graph, call log attributes,
social closeness
1 Introduction
Personalized services can be defined as context-specific services to each individ-
ual client [1]. Since mobile phone can always be carried by the client, it has the
potential to be an ideal medium of such personalization. This potential allows
telecommunication service providers to offer multiple personalized services like
customized ring tone suggestions or different location based information. Pro-
viding such services can increase the popularity of the service providers [2] and
help them to sustain in the competitive market. Thus, it is high time telecom-
munication companies focused more on providing personalized services to each
user for eventually increasing their overall revenue.
Recommending Friends and Family (FnF) numbers [3], that can be registered
by the user for having lower call rate, could be an example of customized personal
services. To provide such service, it is essential to retrieve each user’s calling pat-
tern by analyzing their respective call logs. However, analysis and interpretation
of these call logs is particularly difficult and introduces major research issues [4].
28
A possible solution could be, utilizing the underlying social network [5] of those
call logs. Nevertheless, constructing such network involves multiple challenges
like relative prioritization of in and out degrees, and assigning weight to edges
by using different call log attributes.
Although multiple research has been conducted based on mobile phone call
logs, none has focused directly to provide personalized services to each individual
user. There have been some work that focus on investigating social ties based on
different call attributes [6], [7]. Some of the researchers have focused on predicting
user behavior [8] and community dynamics [9]. Moreover, there are works which
forecast incoming calls [10] and correlate international calls with the export-
import business of the country [11]. However, it is arguable that whether these
state of the arts can be used to provide effective personalized services that can
satisfy each individual user.
This paper proposes a methodology to create a weighted call graph based on
individual call logs and use the graph’s underlying social network for predicting
FnF numbers. To weight the edges, three call log attributes are used which are -
call duration, call period and call location. The technique empirically measures
the effect of these attributes towards social closeness and calculates their relative
significance. The summation of these relative significances, that prevents any of
the attributes being predominated, is considered as the weight. A closeness score
is then calculated by aggregating the product of the weights with values respect
to their call being either incoming or outgoing. This score is used by the method
to rank and suggest numbers for registering as FnF.
The method was implemented and its performance was evaluated based on
a real life dataset. The dataset includes all required call log attributes along
with a user provided score which indicates the strength of social tie with the
person of that particular call. These scores were considered as the ground truth
when weights for edges of the graph are being learned. The social closeness was
then measured, based on which numbers were ranked, to predict FnF numbers.
The performance of the proposed technique was compared along with traditional
approaches based on overall talk-time and call frequency. It seen that the method
outperforms the call frequency and overall talk-time based approaches with 38%
and 10% higher F-measure value.
Rest of the paper is organized as follows: Section 2 highlights state of the
art techniques for analyzing call logs to derive different social contexts. The
proposed technique to predict FnF numbers is presented in Section 3. Section
4 includes the experimental details and performance of the proposed technique.
Finally, this paper is concluded in Section 5 with a discussion about our work
and future research directions.
2 Literature Review
Although log analysis has been a major research concern for a considerable
amount of time [12], its application in mobile phone call pattern analysis has
started comparatively later. To the best of authors’ knowledge, no work directly
29
focus on utilizing the underlying social network obtained from a single user’s call
logs to provide customized services. The work that have been done mostly focus
on constructing social networks from call logs, predicting the user behavior and
identifying multiple social contexts like social affinity or community dynamics.
This section highlights these contributions and discusses how they contrast to
our problem domain.
There have been some significant work which are concerned with predicting
the user behavior. For an example, Dong et. al. proposed a technique to create an
undirected binary graph from the call log to find the human behavior of different
age and gender [8]. The researchers in [13] proposed a method to derive the user
relation using Bayesian network. Phithakkitnukoon et. al. analyzed multiple call
log attributes such as call location, talk-time, calling time and inter-arrival time
to infer behavioral dependencies [10]. The researcher in [14] inferred friendship
network by utilizing the behavioral and survey data. Zhang et. al. have focused on
information retrieval techniques to find human behavior patterns and community
dynamics [9]. There have also been work to assess the strength of social tie based
on call duration [6].
Identifying different social contexts has been another field of major research
interest. Phithakkitnukoon et. al. proposed a method to identify social network
based on the user phone call log [15]. They have used correlation coefficient to se-
lect call log features and then extracted user’s social behavioral pattern from that
network. Another work of Phithakkitnukoon et. al. has been presented in [16]
where they created a network graph to derive socially closest and distant mem-
bers. The researchers in [11] analyzed international phone calls from Rwanda,
between January 2005 to December 2008, to derive the interpersonal connec-
tions between the people of different countries. They have used those findings to
measure and evaluate the social tie between nations.
Researchers have also focused on the construction of call graph by utilizing
different call log attributes. The researchers in [17] created a social network graph
using call duration, source number, destination number and physical location of
the user. They have extracted these information from Call Detail Record (CDR)
[18] files and and generated the adjacency matrix. The edges were weighted using
the call duration value. Motahari et. al. have also used the CDR files and created
multiple affinity networks such as family members, utility network, friends and
co-workers [7]. They have used CDR files containing 4.3 million phone call data
which was collected from 360,000 subscribers.
Review of the state of the art shows that these works are generally focused
on predicting users’ behavioral pattern rather than utilizing those patterns for
providing better user experience. It is arguable that whether these methods can
be used for determining user’s social tie along with utilizing the findings in a
separate strategy to suggest FnF numbers. From different research, it is also
seen that the edges of the constructed call graph is weighted using call duration.
Nevertheless, it may not be the correct representation of social affinity. The
reason behind this is though frequent or long communication likely indicates a
strong tie, little or no communication does not necessarily indicate a weak tie [6].
30
Thus it is required to incorporate other call log attributes, to assign weights on
graph edges, for actual representation of individual’s social tie. This weighted
graph can be easily used to infer the socially closest members and their numbers
can be suggested as FnF.
3 Proposed Method
This research proposes a methodology to create a social network graph using the
phone call log. The network will resemble to a star network where the phone user
will be the center node and individuals with whom communication has occurred
are the peripheral nodes. There will be weighted edges between the central and
peripheral nodes which will indicate the strength of their social ties.The graph
will have two type of edges, one for the incoming calls and other for the outgoing
ones. The frequency of calls will not effect the number of edges since it will be
considered during the weight calculation. A sample social network is presented
in Figure 1. From the figure it can be seen that communications are represented
by weighted edges where the outgoing call to individual pn has a weight wn1 and
the incoming call has a weight wn2 .
ͳ
ͺ ʹ
User ͵
Ͷ
ͷ
Fig. 1. Call graph generated from a single user’s call logs
Initially call log records will be taken from the phone. The call logs should
have the following information: call duration, call period (a period of the day
like 3:00 AM to 6:00 AM) and call location (the place from which the call was
made). It is essential to utilize all these information for weighting graph edges
since using only call duration can often misled regarding the approximation of
social closeness. For an example, a user may talk with a person at his office for
a long time. However, that person could be a client instead of being a friend
31
or relative. So it will be wrong to consider that person as a socially close one
based on the call duration. Moreover, talking with a someone during the office
hours (like 9:00 AM to 5:00 PM ) for a long time might misled in estimating the
nature of social affinity. Thus it would be more reasonable to assign a weight by
combining all these three information. The proposed method utilizes these call
log attributes to assign a weight which is defined in Equation (1).
N
X
ωij = (α · δij k + βq(ijk ) + γr(ijk ) ) (1)
k=1
In Equation (1), ωij refers to the weight of the edge between the person pi
and phone’s user for all calls of type j (like incoming, outgoing or missed). The
set of all j type calls, with respect to person pi is represented by Cij . If |Cij | = N ,
the weight ωij will be the summation of N values. The values will depend on
call duration (δ), call period and call location. The value of call duration is used
by multiplying it with a weight α that reflects the significance of call duration
towards social ties. To represent the significance of call period and call location,
two sets of values, S and L has been introduced. The elements of those two sets
are represented by β and γ respectively. To define their relation with the set of
calls Cij , two mapping functions q : Cij → S and r : Cij → L are defined.
In a nutshell, the weight will be calculated by considering each of the calls
of type j from person pi . For each particular call k, its duration will be mul-
tiplied by a significance factor α. This multiplied value will be added with the
corresponding β and γ value of that particular call. However, the system must
at first calculate those significance values. The social closeness should then have
to be calculated for suggesting the FnF numbers. The procedure for these tasks
are discussed in details in the following subsections.
3.1 Determining the significance values
Since longer communications likely indicate a strong tie [6], the proposed method
uses the correlation coefficient between the call duration and social closeness as
the value of α. However, information regarding the social closeness of people from
each call is a pre-requisite to calculate the coefficient. To determine the values of
β and γ, one has to first fix the number of call periods and locations that should
be considered. The set of these periods and locations are defined as T and L
respectively. For each of the elements in set T and L, the method generates a
separate value of β and γ respectively. These values, combined together, are then
represented by two sets S and L respectively.
The first step in calculating the values of β and γ requires to define a threshold
value σ. This value is used to identify the people who are socially closest to the
phone user. If the social closeness value of a person is above the threshold,
that individual is considered as having a strong social tie. The second steps
involves calculating the percentage of people over σ for each of the call periods
and locations. These percentages provide an insight regarding the significance of
each period and location. However, to consider those as weights, their relative
32
significance is needed to be considered which is calculated at the final step. This
whole procedure is illustrated in Algorithm 1.
Algorithm 1 Determining the significance values
Input: The set of all selected call periods T and locations L
Output: α, S and L
1: Begin
2: Set the value of α to the correlation coefficient between call duration and social
closeness
3: Define a threshold σ for social closeness to determine socially closest persons
4: Calculate the percentage of calls from people whose social closeness value is over
σ on each call period ti ∈ T and location li ∈ L
5: Initialize S ← ∅ and L ← ∅
6: for each ti ∈ T do
percentage of people over σ for period ti
7: βi =
summation of percentage values ∀ti ∈T
8: S ∪ {βi }
9: end for
10: for each li ∈ L do
percentage of people over σ for location li
11: γi =
summation of percentage values ∀li ∈L
12: L ∪ {γi }
13: end for
14: End
3.2 Calculating Social Closeness and Suggesting FnF numbers
After determining the values of α, β and γ, the weight ωij of call type j from
person pi can be calculated using Equation (1) . The weights ωij for each of the
individuals are included in the set Wij which is used by the proposed method to
represent a social network as shown in Figure 1. To calculate the social closeness
of the person pi , the method uses a mapping function φ : J → I, where J
and I represent the set of call types and their significances respectively. This
mapping function φ is used to determine the weight of different call types. As-
signing different weights to different call types is important since the significance
of different call types (incoming or outgoing) could be different. During the cal-
culation of social closeness for person pi , as shown in Equation (2), the weights
of that person ωij for call type j is multiplied by its respective weight provided
by the mapping function φ.
M
X
θi = φ(j) · ωij (2)
j=1
The process of determining the social closeness and ranking the individuals
based on that score is presented in Algorithm 2. Initially, as used in Algorithm 1,
33
a threshold σ is defined to identify socially closest persons. This threshold is used
to calculate the percentage of socially closest persons for each call of type j. The
percentage values are then used to determine the relative significance of different
call types. Using these relative significance values, the method calculates the
social closeness as defined in Equation (2). Each of the individuals, with whom
communication has occurred, are sorted in descending order of social closeness.
The method then suggests the numbers of certain individuals, at the top of the
list, as FnF numbers.
Algorithm 2 Calculating Social Closeness
Input: The set Wij , Cij , J and the mapping function φ
Output: List of individuals sorted in descending order of social closeness
1: Begin
2: Define a threshold σ for social closeness to determine socially closest persons
3: Calculate the percentage of calls from people whose social closeness value is over
σ for each j type of calls
4: Initialize I ← ∅
5: for each j ∈ J do
percentage of people over σ for call type j
6: λj =
summation of percentage values ∀j∈J
7: I ∪ {λj }
8: end for
9: Use Equation (2) to calculate social closeness for each of individual
10: Sort the list of individuals in descending order of social closeness
11: End
4 Experimental Setup and Results
This section discusses about different experimental parameters that were used
during the evaluation of our proposed approach. It also includes the performance
of the proposed technique with a comparison along with two baseline approaches
to suggest FnF numbers.
4.1 Experimental Parameters
For experimental purpose the call logs were collected from 18 individuals. The
call logs included the person’s mobile phone number, call duration and call time.
The call location was manually collected from them. The participants had also
provided a social closeness score, to each of the persons from the call log, in a
scale of 1-10. Moreover, each of them were also asked to select 3 numbers from
these call logs which they could register as FnF numbers. 5 of the participants
provided those numbers and thus their data were used for the testing purpose.
Rest of the data were used to train the proposed method that involves learning
the values of α, β, γ and λ. During the calculation of these weights, the value of
34
σ was set to 8 which is considerably higher enough in the social closeness scale
for recognizing socially closest persons.
The value of α was calculated by determining the correlation coefficient be-
tween call duration and social closeness. It has been found that the call duration
and social closeness has a positive relationship, though not that much strong.
This has been illustrated in Figure 2 by a scatter diagram where social closeness
is plotted against call duration. The value of correlation coefficient was found to
be 0.14 and it was set as the value of α.
14
12
10
Call Duration
8
6
4
2
0
0 2 4 6 8 10
Social Closeness
Fig. 2. Scatter diagram representing the correlation coefficient between social closeness
and call duration
For the calculation of β, we have divided a day into 8 time periods. The per-
centage of socially closest calls and their relative weight is presented in Table 1.
A scatter diagram is presented in Figure 3 to illustrate the percentage of socially
closest people in two different time periods.
Table 1. Relative significance of different time periods used in the experiment
Period Percentage of calls above σ Relative weight
12:00 AM to 3:00 AM 60% 0.163
3:00 AM to 6:00 AM 67% 0.18
6:00 AM to 9:00 AM 25% 0.07
9:00 AM to 12:00 PM 55% 0.15
12:00 PM to 3:00 PM 26% 0.071
3:00 PM to 6:00 PM 41% 0.112
6:00 PM to 9:00 PM 56% 0.153
9:00 PM to 12:00 AM 37% 0.101
For calculating the value of γ, the locations were categorized before collecting
the data. For each of the call logs, the location was selected as either home or
work place. Although there were instances where users were neither in home nor
35
14 14
12 12
Social Closeness
Social Closeness
10 10
8 8
6 6
4 4
2 2
0 0
0 5 10 15 20 25 30 35 40 0 5 10 15 20 25 30
Number of Instances Number of Instances
(a) Time period 3:00 PM to 6:00 PM (b) Time period 9:00 PM to 12:00 AM
Fig. 3. Visualization of calls from socially closest persons in two different time periods
in work place, still they had used various measures two provide the location in-
formation. Examples include selecting the place nearest to their current position
or based on the nature of call and its period. These data, as presented in Figure
4, was used to calculate the value of γ. For the home and workplace, the value
turned out to be 0.44 and 0.56 respectively.
14 14
12 12
Social Closeness
Social Closeness
10 10
8 8
6 6
4 4
2 2
0 0
0 20 40 60 80 100 120 0 10 20 30 40 50 60 70 80 90
Number of Instances Number of Instances
(a) Location - Home (b) Location - Workplace
Fig. 4. Visualization of calls from socially closest persons in two separate locations
In this work, ignoring the missed calls, incoming and outgoing calls has been
considered to measure the value of λ. The percentage of calls from socially closest
persons for outgoing and incoming calls can be visualized from Figure 5. Since
there were only two call types, after calculating the weight of work place, the
other weight was calculated by Equation 3. The weights for the incoming and
outgoing calls has been calculated as 0.44 and 0.56 respectively.
λ if the value of j is 1
φ(j) = (3)
1 − λ otherwise
36
14 14
12 12
Social Closeness
Social Closeness
10 10
8 8
6 6
4 4
2 2
0 0
0 20 40 60 80 100 120 140 160 0 20 40 60 80 100 120 140 160
Number of Instances Number of Instances
(a) Outgoing calls (b) Incoming calls
Fig. 5. Visualization of incoming and outgoing calls from socially closest persons
4.2 Performance Evaluation
The proposed technique tried to match 2 out of 3 FnF numbers provided by each
individuals. The system suggested 5 numbers, one after another, and stopped
at the point where two numbers had being matched. Thus for 5 individuals,
the system tried to match 10 FnF numbers with at most 25 attempts. We have
compared the performance of our system with two baseline approaches which are
based on overall call duration and call frequency. The evaluation technique for
these two approaches remained the same. It is seen that the proposed method
has identified 7 FnF numbers with 22 attempts. On the other hand, the duration-
based and frequency based approaches has identified 6 and 3 numbers with 22
and 25 attempts respectively.
Table 2. Performance comparison of the proposed technique with respect to duration-
based and frequency based approaches
Approaches Precision Recall F-measure
Duration-based 0.27 0.6 0.57
Frequency-based 0.8 0.2 0.19
Proposed technique 0.32 0.7 0.67
The performance of the technique, as shown in Table 2, is interpreted in terms
of precision, recall and F-measure. It is seen that proposed method outperforms
the other two approaches though the value of the precision is low. However, in
this case, recall is more important than precision. This is because for each of the
individuals, we are suggesting at most 5 numbers to match with 2 numbers. Due
to this, the precision will always be lower due to having more false positives.
Nevertheless, the user will always be satisfied if the system chooses more from
the set of relevant numbers. So during the calculation of F-measure, as done in
[19], the method needs to vary the weights of precision and recall. Thus in our
37
case, recall has been weighted five times higher than the precision during the
calculation of F-measure using Equation (4).
precision · recall
FB = (1 + B 2 ) · (4)
B 2 · precision + recall
For the individuals with whom the performance was evaluated, the pattern
of score provided by the system and the social closeness score provided by the
phone users is illustrated in Figure 6. Although the scale of those two scores
are different, it is seen that their curves are very much similar. As the system
suggested FnF numbers based on calculated closeness scores, the performance is
better than the other two approaches.
1.2
Collected
Calculated
1
0.8
Score
0.6
0.4
0.2
0
0 20 40 60 80 100 120 140 160
Number of Instances
Fig. 6. The pattern of the scores collected from the individuals and calculated by the
proposed method
5 Conclusion and Future Work
This paper introduces a technique to prioritize individuals based on call logs
and suggest FnF numbers using that prioritize list. The method utilizes the
underlying social network of the call graph for a single phone user. The edges of
the call graph has been weighted during the prioritization process to incorporate
different call log attributes like duration, period and location. The proposed
methodology was implemented and tested against a set of real life data. The
system has illustrated its effectiveness in identifying FnF numbers with an F-
measure value of 0.67. This research also introduces a set of new research ideas
for the research communities. This includes assessing the existence of a nonlinear
relationship, between duration and social closeness, to improve the performance
of the system. Moreover, one could utilize other call log attributes and optimize
their total number to get better performance.
Acknowledgement
This work is supported by the University Grant Commission, Bangladesh under
the Dhaka University Teachers Research Grant No-Regi/Admn-3/2015/48747.
38
References
1. Ho, S.Y., Kwok, S.H.: The attraction of personalized service for users in mobile
commerce: an empirical study. ACM SIGecom Exchanges 3(4), 10–18 (2002)
2. Witteman, M.: Efficient proximity detection among mobile clients using the GSM
network. Master’s thesis, University of Twente (2007)
3. Dey, B., Newman, D., Prendergast, R.: Analysing appropriation and usability in
social and occupational lives: An investigation of bangladeshi farmers’ use of mobile
telephony. Information Technology & People 24(1), 46–63 (2011)
4. De Melo, P.O.V., Akoglu, L., Faloutsos, C., Loureiro, A.A.: Surprising patterns
for the call duration distribution of mobile phone users. In: Machine learning and
knowledge discovery in databases, pp. 354–369. Springer (2010)
5. Chin, A., Zhang, D.: Mobile Social Networking. Springer (2014)
6. Wiese, J., Min, J.K., Hong, J.I., Zimmerman, J.: Assessing Call and SMS Logs as
an Indication of Tie Strength. Tech. rep., Human-Computer Interaction Institute,
School of Computer Science, Carnegie Mellon University (2014)
7. Motahari, S., Mengshoel, O.J., Reuther, P., Appala, S., Zoia, L., Shah, J.: The
impact of social affinity on phone calling patterns: Categorizing social ties from
call data records. In: Proceedings of the Sixth Workshop on Social Network Mining
and Analysis. pp. 1–9. ACM (2012)
8. Dong, Z.B., Song, G.J., Xie, K.Q., Wang, J.Y.: An experimental study of large-
scale mobile social network. In: Proceedings of the 18th International Conference
on World Wide Web. pp. 1175–1176. ACM (2009)
9. Zhang, D., Guo, B., Yu, Z.: The emergence of social and community intelligence.
Computer 44(7), 21–28 (2011)
10. Phithakkitnukoon, S., Dantu, R., Claxton, R., Eagle, N.: Behavior-based adaptive
call predictor. ACM Transactions on Autonomous and Adaptive Systems (TAAS)
6(3), 21 (2011)
11. Blumenstock, J.E.: Using mobile phone data to measure the ties between nations.
In: Proceedings of the 2011 iConference. pp. 195–202. ACM (2011)
12. Oliner, A., Ganapathi, A., Xu, W.: Advances and challenges in log analysis. Com-
munications of the ACM 55(2), 55–61 (2012)
13. Park, H.S., Cho, S.B.: Building mobile social network with semantic relation using
Bayesian network-based life-log mining. In: Second International Conference on
Social Computing (SocialCom). pp. 401–406. IEEE (2010)
14. Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by
using mobile phone data. Proceedings of the National Academy of Sciences 106(36),
15274–15278 (2009)
15. Phithakkitnukoon, S., Dantu, R.: Inferring social groups using call logs. In: On
the Move to Meaningful Internet Systems: OTM 2008 Workshops. pp. 200–210.
Springer (2008)
16. Phithakkitnukoon, S., Dantu, R.: Mobile social group sizes and scaling ratio. AI &
society 26(1), 71–85 (2011)
17. Zargari, H.: Social network modelling: Retrieval correlated graphs by mobile
phone’s chronological billing files. In: The Second International conference “Prob-
lems of Cybernetics and Informatics”. pp. 67–70 (2008)
18. Horak, R.: Telecommunications and Data Communications Handbook. John Wiley
& Sons (2007)
19. Li, X., Wang, Y.Y., Acero, A.: Learning query intent from regularized click graphs.
In: Proceedings of the 31st Annual International ACM SIGIR Conference on Re-
search and Development in Information Retrieval. pp. 339–346. ACM (2008)
39