A Survey on Tie Strength Estimation Methods
in Online Social Networks
Isidoros Perikos
and Loizos Michael
Open University of Cyprus, Nicosia, Cyprus
CYENS Center of Excellence, Nicosia, Cyprus
Keywords: Tie Strength, Social Networks, Social Relations, Social Interaction.
Abstract: Social networks constitute an important medium for social interaction where people communicate and
formulate relationships in a way similar to what they do in real life. The analysis of the users’ relationships
in social networks can lead to new insights into human social behavior. Tie strength constitutes a core
aspect of social relationships, which represents the importance of a relationship and the closeness of
individuals. Understanding the key features of tie strength in social networks can assist in formulating more
efficient user-centric services. This survey paper examines the advances in the area of the analysis of tie
strength in social networks. We study the dimensions of tie strength and review the key predictive features
for each dimension. We, then, undertake a comparative study of methodologies to model tie strength and
examine the key findings. Finally, we discuss open issues and challenges in specifying tie strength.
During the last years, the advancement of social
networks has completely redefined the way that we
conceive our social relationships and has created the
sensation of having broken the barriers of time and
geography that are limiting people’s social world
(Pappalardo et al., 2012). With the rising expansion
of the social networks the capacity of the people to
interact, communicate and network has been greatly
increased (Liberatore and Quijano-Sanchez, 2017).
Social networks create new online environments
where social relationships can not only map and
develop preexisting relationships that are established
face-to-face in the physical world, but can also
facilitate the development of new relationships that
may exist and evolve only in the world of the social
network (Arnaboldi et al., 2015).
Social networks constitute environments where
social ties among individuals are developed and
have become a predominant medium for social
interaction that have changed completely the way of
human communication. People connect and
formulate relationships in social media in a similar
way they do in the real world (Dunbar et al., 2015).
Actually, the formulation and the development of
social relationships is actually what makes social
media ‘social’ (Gilbert and Karahalios, 2009).
Although individuals have different types of
relationships with other ones that differ in kind and
closeness, social networks do not distinguish and
diversify them. So, they treat all connections the
same, even if one relationship refers to a trusted
friend and another to a total stranger. In social
networks, the online connection between users is
often defined as a ‘friendship’ or ‘follow’ or
‘connected’ manifestation and a connection between
two individuals is considered to either exist or not. So,
different types of friendships best friends, total
strangers and long acquaintances are all grouped
under the label of ‘friend’, ‘follow’ or ‘connection’. In
this regard, social networks do not make distinctions
between best friends, that are the relationships that
one trusts, and mere acquaintances and so all the
relationships are uniformly labeled. However, some
social relationships and connections are stronger than
others. It is natural for people to have not only friends
but also best friends and also to distinguish friends
from acquaintances (Jones et al., 2013). Social
scientists highlighted this point of the social
connections and researchers utilize the expression of
the tie strength to pertain to this concept (Granovetter,
1973; Marsden and Campbell 2012).
Tie strength is a key concept in social networks
associated with the value which is placed by
individuals on their relationships referring to the
Perikos, I. and Michael, L.
A Survey on Tie Strength Estimation Methods in Online Social Networks.
DOI: 10.5220/0010845100003116
In Proceedings of the 14th International Conference on Agents and Artificial Intelligence (ICAART 2022) - Volume 3, pages 484-491
ISBN: 978-989-758-547-0; ISSN: 2184-433X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
general sense of closeness with another individual
(Granovetter, 1983). So, relationships in social
networks can be measured with the currency of the tie
strength (Gilbert and Karahalios, 2014), a concept
introduced by Granovetter (1973) which has become
the metric to measure the relationships in social
media. In this context, two types of ties are specified,
strong ties and weak. In general, when the sense of
closeness between two individuals is strong, a strong
tie is defined and, in the same regard, when it is weak,
a weak tie is defined (Granovetter, 1983). Strong ties
are considered to exist with people one trusts, and that
their social circles can highly overlap with his own.
Weak ties are mainly considered acquaintances.
Measuring and predicting tie strength, and
moreover, understanding the factors that drive tie
strength, has been an expanding area of interest in
social sciences, with increasing utility in the analysis
of social networks (Mattie al., 2018). Analyzing and
predicting tie strength in social networks can lead to
new insights into human social behavior and assist
in designing novel user-centric services (Arnaboldi
et al., 2013). So, the analysis of the social
relationships and the accurate specification of the tie
strength is highly desired.
In the context of this work, we examine the
advent of the last decade in predicting tie strength in
social networks. The survey reviews the elements
and the characteristics of tie strength and categorizes
the research works with respect to the dimensions
that are involved in the modelling of the
relationships’ tie strength. The reminder of the paper
is structured as follows. Section 2 examines the
dimensions and manifestations of tie strength in
social networks while Section 3 examines the
predictive variables that can be extracted from
interaction data and associates the variables to
dimensions. Section 4 examines prediction methods
and models for tie strength estimation, categorizes
them according to the dimension of tie strength that
are considered and reviews the main findings
regarding the components of the tie strength and the
good predictors. Section 5 examines the utilization
in a wide spectrum of social network analyses.
Section 6 examines challenges and open research
directions. Finally, Section 7 concludes the work.
Tie strength constitutes a factor of high importance
in the analysis of social networks and it is
considered to be a complex factor that is hard to
accurately estimate. The main reason for this is that
tie strength is a multidimensional factor where
different forms and levels of interaction need to be
considered. Tie strength is highlighted to have many
dimensions and different manifestations.
Granovetter (1973), in his landmark work on The
Strength of Ties, specified four main dimensions for
tie strength: the time spent connecting and
interacting with others, the emotional intimacy, the
intensity and the reciprocal services. After that, three
additional dimensions were proposed and the list of
dimensions was extended with the proposal of the
emotional support (Wellman and Wortley, 1990) the
social distance (Lin et al., 1981), and the structural
topology of the social network (Xiang et al., 2010).
Each dimension captures different elements of the
social relationships. The dimension of time captures
the duration and the frequency of the
communication. In general, the more frequent and
higher the interaction between a pair of individuals
is, the stronger the sentiment of friendship and tie
people feel (Luarn and Chiu, 2015; Mathews et al.,
1998). Strong tie is bound up with the constant and
frequent communication and the amount of time can
promote other dimensions too (He et al., 2012).
The intensity represents the recognition of entities
producing emotions that stresses on the cognition of
others. It is relative to the absolute strength and
individuals with highly intensive relationships is
expected to spend more time with each other, that is
greatly more than individuals with relationships that
are less intensive (Kwon et al., 2013).
The intimacy concerns the affection between two
individuals and acts as a sense of security and
reliance (He et al., 2012). It is stated that intimacy
relationships are willing to talk with open mind and
demonstrate great support and recognition. It
necessitates considerably more commitment and
presumably a greater amount of positive affect
between each other (Lewis et al., 2008)
The dimension of the reciprocal services
represents the different forms of communication and
the services utilized in interaction. An important
parameter to develop a relationship is revenue that
can be measured by the cost and the profit including
energy, time emotion, and others. Social networks
can reduce the cost of the social activities (He et al.,
2012). Strong ties can easily share the information
and the resources they possess and also they can
provide access to information circulating in their
dense network. So, strong tie includes more
reciprocity services in exchanges (Granovetter,
A Survey on Tie Strength Estimation Methods in Online Social Networks
The emotional support dimension represents a tie
on an emotional level and concerns cases of
discussions and advice offering on personal and
family problems, something that can indicate a
strong tie between the users (Gilbert and Karahalios,
2009). The dimension refers to providing messages
that involve emotional content, re-assuring that the
one is valuable and care about. Strong ties provide
powerful emotional support that unites to face
challenges and overcome crises.
Figure 1: Overlap of the four main dimensions defined by
Granovetter (1973).
Structural dimension represents factors such as
the topology of the social network and social circles
of the users in it (Xiang et al., 2010). Strong ties and
more likely to connect similar people and similar
individuals tend and are more likely to cluster
together. So, given that strong ties connect
individual A to B and also to C, then it is very likely
that C and B will develop a friendship once they
meet (Granovetter, 1983).
Social distance is also highlighted to influence tie
strength and factors like gender, race,
socioeconomic status, education and political views
and affiliation and can affect the tie strength
development between individuals. Research studies
have indicated that strong ties are more common
between individuals of the same age, interests and
who share certain life activities (Gilbert and
Karahalios, 2009; Luarn and Chiu, 2015).
The dimensions have facilitated the definition and
the quantification of possible factors and predictive
variables of tie strength (Mattie et al., 2018). These
variables derive from the social information in the
networks that relate to the profiles of individuals as
well as to the interaction with their peers and which
will be used as predictors of tie strength between
two individuals. Table 1 categorizes the predictive
variables used in research works in the literature.
The predictive variables are mapped into the seven
dimensions of the tie strength. Given that different
social networks provide their users with different
means of interaction, some variables generalize to
any network, while some other may be specific to a
number of social networks (Mattie et al., 2018). For
example, photo tags variables and check-in denoting
that individuals appear together in photos. User
social profiles and interaction activities with their
peers in a social network need to be analyzed in
order to identify relative variables that can be
quantified and be used in order to infer tie strength
between individuals.
In the dimensions of time, time since first
communication measures the length of the connection
while the time since last communication captures the
recency. The frequency is a proxy for the volume of
the interaction between two individuals.
In the dimension of intensity, exact
communication aspects and messages are measured
like the number of the messages exchanged, the
posts, comments, likes. The variables of this
dimension rely heavily on the characteristics and the
communication means of each social network.
In the intimacy dimension, intimacy words
measure the topics of the messages exchanged while
the relationship status captures specific types of
relationships that may be denoted by the users such as
married with each other, family members, etc.
Common appearances in photos is another
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
measurement of the intimacy counting photographs
that the two individuals appear together and the
common check-ins measures places that they have
been together.
In the dimension of reciprocal services, common
applications measure the services and the applications
that both the user and the friend share. The same
stands for the links exchanged where URLs passed
between two users can be indicative of the reciprocal
services both use (Gilbert and Karahalios, 2009).
In the dimension of structural topology, variables
capture aspects of the network structure and the
groups that the individuals belong to. So, common
groups variable measures the groups that the two
individuals belong to, while the overlapping networks
Table 1: Categorization of variables.
Dimension Main Predictive Variables
Time since first communication
Time since last communication
Frequency of communication
Communication aspects with frien
Messages exchanged
Comments exchan
Relationship status
Appearances together in photos
Common check-ins
Intimacy words in the
Common applications
Links exchanged by wall posts
Common groups
Mutual friends
TF-IDF of interests
Listed in overlapping networks
Text analysis and specification of
emotional context and emotional
words. Specification of positive
and negative contexts
Age difference
Occupation difference
Education difference
Political difference
Religion difference
capture the social circles, organizations and networks
like universities and companies that both individuals
are members. Mutual friends can also indicate clues
for tie strength and having mutual friends can foster
relationship development (Adamic and Adar, 2003).
Structural variables can be measured by the interests
individuals have in common and the normalized TF-
IDF of the interests too.
In the dimension of emotional support, deep
analysis of text messages and interaction of the
individuals aims to specify emotional support
indicators and predictive variables like positive
emotional words and negative emotional words.
Dictionaries and linguistic resources like LIWC can
provide indicative information about the categories
of the words and the context messages.
In the social distance dimension, variables
measure the age difference, the education discipline
and level of the individuals, the political view and
the occupation status. The identity information of
the profiles as well as the language (location) beliefs
(philosophy, political view) are used to measure the
social distance of the individuals (Luarn and Chiu,
In the literature, several works study tie strength
with the aim to formulate predictive methods to
estimate it. The first direction for the estimation of
tie strength was indicated by Granovetter (1973)
who stated that tie strength can indeed be quantified
and that the strength of a tie is a probably linear
combination of the amount of time, the intimacy, the
intensity and the reciprocal services (Granovetter,
1973). During the last years, research works have
examined different methods based on the
characteristics of the particular research domain
examined, the aims of the study and the access to the
predictive variables that can be calculated in the
domain examined. Typically, users are involved and
participate in surveys in order to label the nature of
each one of their social relationships.
Methods to obtain the ground truth data of the tie
strength of relationships in a social network is a
process of special attention. In the literature, two
main stream of approaches prevail. The first
approach which is the most common is to survey the
users who accepted to participate in the study and
collect feedback about the strength of their
relationships with their friends (Jones et al., 2013).
A Survey on Tie Strength Estimation Methods in Online Social Networks
The second approach is to use trusted networks in
order to determine strong ties (Rotabi et al., 2017).
An example is the use of telephone network which is
considered to be a trusted network and the tie
strength between two individuals who have phone
contacts is determined as strong one.
Kahanda and Neville (2008) examined the nature
and the dimensions of the relationship strength in
Facebook and utilized a set of characteristics like
marital status, gender, topological features like user
connectivity, graph of friendship, shared posts on the
wall to specify special friends. The authors applied a
supervised learning approach and the results of the
study concluded that the network transactional
features like shared posts on the wall are the most
prominent in predicting tie strength.
Gilbert and Karahalios (2009) defined indicators
of tie strength that are specific to Facebook users
and formulate a regression predictive model which
reports 85% accuracy for the classicization of binary
tie strength. The authors conclude that the dimension
of intimacy makes the greatest contribution to tie
strength specification and that educational difference
strongly assists in the prediction of tie strength with
tie strength diminishing as difference grows.
Pappalardo et al. (2012) present a model to
estimate tie strength that is based strongly on
variables of the network topology dimension and the
intensity. The authors describe a quantitative
measure of tie strength that is domain-independent
and can be generalized and applied to any social
network. The findings indicate that the strength of a
tie is strictly related to the number of interactions
among the people involved and that it is also related
to the number of different contexts in which those
connections take place.
Arnaboldi et al. (2013) present a linear model
which estimates tie strength in Facebook. The
authors’ model takes into account variables from the
dimensions of time, network structure, intensity and
intimacy and describing different aspects of user
interaction. The liner model reports quite good
performance and has accuracy higher than 80%. The
work indicates that the recency of contact is the most
indicative predictor of tie strength.
Jones et al. (2013) determine tie strength from
users’ behavior in Facebook. The authors’ model
takes into account variables from the dimensions of
intimacy, reciprocal services, intensity, structure
topology and social distance. The authors in their
study surveyed Facebook users asking them to
specify their closest friends, a piece of information
that is used as ground truth. An additive logistic
regression model was formulated which achieved an
accuracy of 84% on the context of the study. The
authors report the frequency of online interaction
was the most indicative information for strong ties.
Servia-Rodriguez et al. (2014) present a model to
assess strength and classify it within four categories
of social spheres. The model assesses tie strength by
taking into account users’ interactions and predictive
features from the dimensions of intensity, reciprocal
services, intimacy and structural topology. The work
also points out the importance of using information
from as many social networks as possible in order to
avoid losing data in estimation of the tie strength.
Table 2: Analysis of works with respect to the dimensions.
Work Time Intensity Intimacy Reciprocal
(Granovetter, 1973) x x x x
(Kahanda and Neville,
x x x
(Gilbert and Karahalios
x x x x x x
alardo et al., 2012) x x
(Arnaboldi et al., 2013) x x x x
(Jones et al., 2013) x x x x x x
(Servia-Rodriguez et al.,
x x x
(Liberatore and Quijano-
Sanchez, 2017)
x x x x x x x
(Mattie et al., 2018) x x x
(Stolz and Schlereth,
x x
(Ureña-Carrion, 2020) x x x x x
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
The reason for this is the fact that people usually
possess accounts in different social platforms and it
is possible not to interact with their peers with the
same frequency in all of them.
Liberatore and Quijano-Sanchez (2017) present a
computational model for tie strength that is
independent of the domain of the social network. In
the context of the work, users were requested to
participate and define their relationships. The
authors analyzed their personal and friendship
interaction data and a linear model was examined.
Predictive variables from the seven dimensions were
examined and authors in their study indicate that
relying exclusively and solely on intensity or
intimacy may not be enough to efficiently calculate
the tie strength.
Mattie et al. (2018) present a method for tie
strength estimation that is based mainly on the
structure dimension. The bow tie framework is
proposed which consists of a focal tie and all actors
connected to either or both of the two focal nodes on
either side of the focal tie. The authors utilize
machine learning techniques as well as regression
methods and study which variables are most useful
in predicting tie strength. The results show that the
more the friend that two individuals share the
stronger their tie is and also that geographical
location can increase the tie strength of individuals.
Stolz and Schlereth (2020) present an approach
for the prediction of tie strength that takes into
account predictive variables from network, social
distance and intensity dimensions. The proposed
approach relies heavily on ego network structures
that are user connections and interlinkage among
them. The authors include also user similarity
variables such as the matching gender and language
of the users. The precision of the authors’ method in
identifying all observed strong ties is 45%. The work
also indicates that individuals react stronger to
suggestions that are made of a close friend compared
to the suggestions made by an acquaintance.
Ureña-Carrion et al. (2020) study how
communication events and contact time can be an
indicative predictor for tie strength. The authors
study tie strength through the four main dimensions
of Granovetter’s theory. The results of the authors’
work indicate that the number of days and hours
with contracts are quite indicative variables in the
estimation of tie strength. Also, the time of the first
and last communication can provide indicative
information of tie strength and perform better than
the communication-intensity variables.
Tie Strength is a prevalent feature in social network
research and various studies are examining the
impact of tie strength in various procedures.
Understanding of the tie strength is essential in order
to study the dynamics of social behaviors in network
as well as the relationships of the users in it
(Arnaboldi et al., 2013). Tie strength can to affect
career advancement and the word-to-mouth
propagation of information (Mittal et al., 2008). It is
highlighted that the tie strength has a general impact
on behavior outcomes and intentions in the social
network contexts (Ureña-Carrion et al., 2020).
Specifically, strong ties are the ones that are most
likely to transit norms and behavior change (Kim et
al., 2015) and the identification of strong ties in a
social network can assist in focus targeting these
relationships in a wide spectrum of positive
interventions that can have great multiplier effects as
they spread from an individual to another (Jones et
al., 2013). Research studies have indicated the
impact of tie strength on decision making and that
strong ties can greatly affect users in opinion
seeking as well as in adoptions of stances (Stolz and
Schlereth, 2020). The information about the tie
strength and the knowledge of the social dynamics
that affect and contribute to tie strength is reported
to increase the efficiency of link prediction in social
networks (Mattie et al., 2018). Tie strength
estimation in the context of online social networks
can assist in more efficiently detect communities in
social networks. Tie strength is also assistive in
modeling information diffusion in social networks
(Bakshy et al., 2012). The literature suggests that
novel information comes mainly from weak ties.
Weak ties can provide access to novel information
that is pieces of information that are not circulating
in the dense network formulated by strong ties
(Gilbert and Karahalios, 2009). Weak ties might be
more frequently to convey new pieces of
information, so even though someone is somebody
you don't interact much with, and you might have
less in common with, they might connect you to a
part of the world that you don't normally have access
to, and so they can still be very important in
accessing information that might not be redundant
with people that you often interact with. So, weak
ties can play a very important role in information
diffusion and to provide access to novel pieces of
A Survey on Tie Strength Estimation Methods in Online Social Networks
information. Moreover, of importance is also the
bandwidth that a connection possesses.
Although the methods in the literature achieve quite
promising performance in predicting tie strength
there are many challenges and open issues to be
addressed. A first challenge concerns the detection
and the proper handling of salient users. Most of the
methods in the literature merely focus on active
users and the salient users will be inappropriately
considered as acquaintances due to their inactiveness
(Li et al., 2018).
Another challenge concerns the emotional
support dimension of tie strength and the fact that
emotional factors are not directly measurable and are
quite hard to quantify (Arnaboldi et al., 2013;
Gilbert and Karahalios, 2009). The more efficient
measurement of indicative variables from the
emotional dimension could greater impact tie
strength prediction.
The social distance dimension needs special
handling too. An analytic framework for the fine-
grained measurement of the social distance. Most of
the existing methods do not properly capture the
social distance as it derives from differences in
education, in political views and so the specification
of an analytical and fine-grained framework could
be assistive in measuring social distances and reflect
the diversity in the population.
Furthermore, a fine-grained estimation of the tie
strength will also be a next. Most of the existing
works model and estimate tie strength on a binary
scale where strong and weak ties are specified
between the users in a social network. Although
research studies of the previous years have provided
advances and valuable insights into the dimensions
and the predictive variables, the analytical and
numerical specification of tie strength constitutes a
direction for research (Mattie et al., 2018).
Reproducibility issues constitute a great challenge
too since most of the approaches in the literature
utilize so many features and predictive variables from
social networks (Facebook, Twitter) that their models
are almost impossible to reproduce mainly because of
recent APIs restrictions (Liberatore and Quijano-
Sanchez, 2017). In addition, in the literature, there is a
lack of public benchmark data sets for the study of
methods and for systematically appraising the
performances of models. The availability of such
benchmark, pseudonymized or anonymized datasets
for the fair assessment of new methods and models is
a next important step too.
Kin relationships need special attention too.
Research studies indicate that kin relationship
remain stable over time even if they interact rarely
or even not at all (Roberts and Dunbar, 2011). So,
such relationships need special handling in modeling
user interactions and formulating models for tie
strength prediction. The proper identification of kin
relationships and the formulation of social models to
handle them, could further enhance the performance
of prediction models and provides another direction
for further research.
Tie strength constitutes a primitive challenge in the
domain of social networks and the modeling and
prediction of the tie strength of users’ relationships
has attracted increased research interest. This paper
presents the advent of the last years in predicting tie
strength in social networks and examines methods
for the estimation of tie strength. The dimensions
and the manifestations of the tie strength are studied
and state-of-the-art research works in tie strength
estimation are examined with respect to the different
tie strength dimensions. Meta-analyses on the results
and the findings of the studies in the literature are
performed. Last but not least, main challenges and
open issues in designing methods for the estimation
of tie strength are discussed.
This work was supported by funding from the EU's
Horizon 2020 Research and Innovation Programme
under grant agreements no. 739578 and no. 823783,
and from the Government of the Republic of Cyprus
through the Deputy Ministry of Research,
Innovation, and Digital Policy.
Adamic, L. & Adar, E. (2003) Friends and Neighbors on the
Web. Social Networks, 25(3):211–230.
Arnaboldi, V., Passarella, A., Conti, M. & Dunbar, R.
(2015) Online Social Networks: Human Cognitive
Constraints in Facebook and Twitter Personal Graphs.
ICAART 2022 - 14th International Conference on Agents and Artificial Intelligence
Arnaboldi, V., Guazzini, A. & Passarella, A. (2013)
Egocentric Online Social Networks: Analysis of Key
Features and Prediction of Tie Strength in Facebook.
Computer Communications, 36(10–11):1130–1144.
Bakshy, E., Rosenn, I., Marlow, C. & Adamic, L. (2013)
The Role of Social Networks in Information Diffusion.
In Proceedings of the 21st International Conference on
World Wide Web, pp. 519–528.
Dunbar, R., Arnaboldi, V., Conti, M. & Passarella, A.
(2015) The Structure of Online Social Networks Mirrors
Those in the Offline World. Social Networks, 43:39–47.
Flamino, J., DeVito, R., Szymanski, B. K. & Lizardo, O.
(2021) A Machine Learning Approach to Predicting
Continuous Tie Strengths. arXiv preprint
Granovetter, M. (1983) The Strength of Weak Ties: A
Network Theory Revisited. Sociological Theory, 1:201–
Granovetter, M. (1973) The Strength of Weak Ties. The
American Journal of Sociology, 78(6), 1360–1380.
Gilbert, E. & Karahalios, K. (2009) Predicting Tie Strength
with Social Media. In Proceedings of the SIGCHI
Conference on Human Factors in Computing Systems,
pp. 211–220.
Gilbert, E. & Karahalios, K. (2014) Computing and
Building around Tie Strength in Social Media.
Foundations and Trends in Human-Computer
Interaction, 7(3):237–349.
He, Y., Zhang, C. & Ji, Y. (2012) Principle Features for Tie
Strength Estimation in Micro-Blog Social Network. In
Proceedings of the 12th IEEE International Conference
on Computer and Information Technology, pp. 359–
Kahanda, I. & Neville, J. (2009) Using Transactional
Information to Predict Link Strength in Online Social
Networks. In Proceedings of the AAAI International
Conference on Web and Social Media.
Khan, N. A., Zhang, S., Zhou, W., Almogren, A., Ud, D. &
Asif, M. (2020), Inferring Ties in Social IoT Using
Location-Based Networks and Identification of Hidden
Suspicious Ties. Scientific Programming.
Kim, D., Hwong, A., Stafford, D., Hughes, A., O'Malley, J.,
Fowler, J. & Christakis, N., (2015) Social Network
Targeting to Maximise Population Behaviour Change:
A Cluster Randomised Controlled Trial. The Lancet,
Kwon, O., Kim, C. & Kim, G. (2013) Factors Affecting the
Intensity of Emotional Expressions in Mobile
Communications. Online Information Review.
Jones, J., Settle, J., Bond, R., Fariss, C., Marlow, C. &
Fowler, J. (2013) Inferring Tie Strength from Online
Directed Behavior. PloS One, 8(1):e52168.
Larson, J. (2017) The Weakness of Weak Ties for Novel
Information Diffusion. Applied Network Science,
Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A. &
Christakis, N. (2008) Tastes, Ties, and Time: A New
Social Network Dataset using Facebook.com. Social
Networks, 30(4):330–342.
Li, X., Fang, H., Yang, Q. & Zhang, J. (2018) Who Is Your
Best Friend? Ranking Social Network Friends
According to Trust Relationship. In Proceedings of the
26th Conference on User Modeling, Adaptation and
Personalization, pp. 301–309.
Liberatore, F. & Quijano-Sanchez, L. (2017) What Do We
Really Need to Compute the Tie Strength? An
Empirical Study Applied to Social Networks. Computer
Communications, 110:59–74.
Lin, N., Ensel, W. & Vaughn, J. (1981) Social Resources
and Strength of Ties: Structural Factors in Occupational
Status Attainment. American Sociological Review,
Luarn, P. & Chiu, Y. P. (2015) Key Variables to Predict Tie
Strength on Social Network Sites. Internet Research,
Marsden, P. & Campbell, K. (2012) Reflections on
Conceptualizing and Measuring Tie Strength. Social
Forces, 91(1):17–23.
Mathews, K. M., White, M. C., Long, R. G., Soper, B. &
Von Bergen, C. W. (1998) Association of Indicators and
Predictors of Tie Strength. Psychological Reports,
Mattie, H., Engø-Monsen, K., Ling, R. & Onnela, P. (2018)
Understanding Tie Strength in Social Networks Using a
Local Bow Tie Framework. Scientific Reports, 8(1):1–
Mittal, V., Huppertz, J. & Khare, A. (2008) Customer
Complaining: The Role of Tie Strength and Information
Control. Journal of Retailing, 84(2):195–204.
Pappalardo, L., Rossetti, G. & Pedreschi, D. (2012) How
Well Do we Know Each Other? Detecting Tie Strength
in Multidimensional Social Networks. In Proceedings of
the IEEE/ACM International Conference on Advances
in Social Networks Analysis and Mining, pp. 1040–
Rotabi, R., Kamath, K., Kleinberg, J. & Sharma, A. (2017)
Detecting Strong Ties Using Network Motifs. In
Proceedings of the 26th International Conference on
World Wide Web, pp. 983–992.
Roberts, S. & Dunbar, R. (2011) The Costs of Family and
Friends: An 18-Month Longitudinal Study of
Relationship Maintenance and Decay. Evolution and
Human Behavior, 32(3):186–197.
Servia, R., Diaz, R., Fernandez, V., Blanco, F. & Pazos, A.
(2014) A Tie Strength Based Model to Socially-
Enhance Applications and its Enabling Implementation:
mySocialSphere. Expert Systems with Applications,
Stolz, S. & Schlereth, C. (2020) Predicting Tie Strength
with Ego Network Structures. Journal of Interactive
Marketing, 54:40–52.
Ureña-Carrion, J., Saramäki, J. & Kivelä, M. (2020)
Estimating Tie Strength in Social Networks Using
Temporal Communication Data. EPJ Data Science,
Wellman, B. & Wortley, S. (1990) Different Strokes From
Different Folks: Community Ties and Social Support.
American Journal of Sociology, 96(3):558–588.
Xiang, R., Neville, J. & Rogati, M. (2010) Modeling
Relationship Strength in Online Social Networks. In
Proceedings of the 19th International Conference on
World Wide Web, pp. 981–990.
A Survey on Tie Strength Estimation Methods in Online Social Networks