=Paper=
{{Paper
|id=Vol-3741/paper41
|storemode=property
|title=Evaluating status and value assortativity in Threads
|pdfUrl=https://ceur-ws.org/Vol-3741/paper41.pdf
|volume=Vol-3741
|authors=Gianluca Bonifazi,Enrico Corradini,Domenico Ursino
|dblpUrl=https://dblp.org/rec/conf/sebd/BonifaziCU24
}}
==Evaluating status and value assortativity in Threads==
https://ceur-ws.org/Vol-3741/paper41.pdf
Evaluating status and value assortativity in Threads
(Discussion Paper)
Gianluca Bonifazi1,† , Enrico Corradini1,*,† and Domenico Ursino1,†
2
DII, Polytechnic University of Marche
Abstract
The concept of assortativity in complex networks indicates the preference of a node to relate to other
nodes that are somewhat similar. It is possible to think of different forms of similarity between nodes
that can give rise to different forms of assortativity. In this paper, along the lines of homophily (of which
assortativity can be seen as a special case), we define two categories of assortativity, namely status
assortativity and value assortativity. We then show that all definitions of assortativity introduced in
the past belong to one of the two categories. Afterwards, we define and evaluate two forms of status
assortativity and one form of value assortativity in Threads. Since this social network is relatively new,
we could not use existing datasets related to it, and therefore had to build one from scratch, which we
now make available to all interested researchers.
Keywords
Assortativity, Threads, Social Network Analysis, Value and Status Homophily
1. Introduction
Assortativity is a central concept in complex network analysis. It was introduced by Newman
[1] and quantifies the propensity of network nodes to connect with other nodes that are similar
in some way. Similarities can be of different type; for example they may involve structural
aspects or characteristics of the objects/people represented by the nodes. Very often structural
aspects are considered, and generally the focus is on degree of nodes, in which case we refer to
degree assortativity. Assortativity and its counterpart, disassortativity, play a critical role in
determining the structural and dynamic aspects of networks. They influence the coherence of
networks, their resilience to perturbations and the efficiency of processes such as information
dissemination, epidemic spread, and virus control [2, 3, 4, 5, 1].
In the Social Network Analysis area, assortativity can be seen as a special case of the concept
of homophily. This concept was introduced by Lazarsfeld and Merton [6, 7] and indicates the
tendency of participants in a community to interact primarily with other participants who have
the same characteristics. In sociology, there are two types of homophily, status homophily
and value homophily. Status homophily is the tendency of individuals to interact with others
who have the same status. Value homophily indicates the tendency of individuals to interact
with others with whom they share the same values [8]. When we apply this concept to Social
SEBD 2024: 32nd Symposium on Advanced Database Systems, June 23-26, 2024, Villasimius, Sardinia, Italy
*
Corresponding author.
†
These authors contributed equally.
$ g.bonifazi@univpm.it (G. Bonifazi); e.corradini@univpm.it (E. Corradini); d.ursino@univpm.it (D. Ursino)
0000-0002-1947-8667 (G. Bonifazi); 0000-0002-1140-4209 (E. Corradini); 0000-0003-1360-8499 (D. Ursino)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Network Analysis, status homophily mainly refers to the structure of the network where, for
example, high-degree nodes tend to interact with high-degree nodes. Value homophily, on the
other hand, refers to the content that users associated with nodes exchange and publish; indeed,
published content reveals the values of the corresponding authors.
Since assortativity is closely related to homophily in Social Network Analysis, we believe that,
at least in this context, and possibly in others that we will analyze in the future, it is possible to
define two categories of assortativity by distinguishing between status assortativity and value
assortativity. Status assortativity occurs when the similarity between nodes is evaluated based
on their structural characteristics (e.g., their degree or betweenness or eigenvector centrality). In
contrast, value assortativity occurs when the similarity between nodes is evaluated based on the
content published and exchanged by the corresponding users. Accordingly, status assortativity
indicates the preference, of the node in a network, to relate to other nodes that are structurally
similar. Value assortativity, on the other hand, indicates the preference for the node in a network
to relate to other nodes in such a way that the corresponding users publish similar content and
thus show similar interests.
Having introduced the distinction between status assortativity and value assortativity, we
decided to compute these two measures on Threads1 . Launched by Meta on July 6, 2023,
Threads is a new content-based social platform seen by many experts as a direct competitor to
X. Designed to share text updates and foster public conversations, Threads has quickly taken its
place in the social media landscape, recently reaching more than 130 million monthly active
users2 .Because of its newness, Threads has not yet been extensively studied by social network
researchers. The idea of applying our new concepts of status and value assortativity directly to
Threads allows us to contribute to better understanding this little explored digital platform. In
addition, we extend the scientific community’s knowledge of assortativity to this new social
platform. In order to study assortativity on Threads, it was crucial to have a dataset derived
from it that contained all structural and content information capable of supporting this type of
analysis. Unfortunately, we were unable to find a dataset in the literature that would support
such an analysis. Therefore, we had to construct one. Once we completed this task, we decided
to make such a dataset open by making it available to all researchers who want to perform
analyses and studies on Threads.
After building the Threads dataset, we ran our status and value assortativity analyses on it
and compared the results with those already known about other social platforms, highlighting
similarities and differences.
The structure of this paper is as follows: In Section 2, we introduce the concepts of status
and value assortativity and propose some specific forms of them. In Section 3, we describe the
main features of our Threads dataset, illustrate our experiments to compute different forms of
assortativity on Threads and compare our results with those of other social platforms. Finally,
in Section 4 we draw our conclusions and highlight some possible developments of our research
efforts.
1
www.threads.net
2
https://techcrunch.com/2024/02/01/threads-now-reaches-more-130-million-monthly-users-says-meta-up-30m-from-q3
2. Defining status and value assortativity
In the Introduction, we stated that the first goal of this paper is to deepen the concept of
assortativity by introducing a distinction between status and value assortativity. In particular:
(i) Status assortativity indicates the preference, for the node in a network, to relate to other
nodes that are structurally similar; (ii) Value assortativity denotes the preference, for the node
in a network, to relate to other nodes such that the corresponding users publish similar content,
thus showing similar interests.
Starting from these two definitions, in this section we propose some forms of status and value
assortativity that are applicable to any content-based social platform.
To do this, we must first introduce a model for representing a content-based network. In
particular, the latter can be represented as a directed graph:
𝒩 = ⟨𝑁, 𝐴⟩ (2.1)
𝑁 is the set of nodes in 𝒩 . A node 𝑛𝑖 ∈ 𝒩 is associated with a user 𝑢𝑖 in the content-based
network. Since there is a biunivocal correspondence between a node and a user, we will use
these two terms interchangeably in the following. Each node 𝑛𝑖 is associated with a label 𝑓𝑖
indicating the number of followers of 𝑢𝑖 . 𝐴 is the set of arcs of 𝒩 . An arc 𝑎𝑖𝑗 = (𝑛𝑖 , 𝑛𝑗 ) ∈ 𝐴
represents the set of interactions from a user 𝑢𝑖 to a user 𝑢𝑗 . An interaction from 𝑢𝑖 to 𝑢𝑗
indicates that 𝑢𝑖 commented on a post by 𝑢𝑗 . Each arc 𝑎𝑖𝑗 is associated with a label 𝑇 𝑆𝑖𝑗
indicating the main topics discussed in the interactions from 𝑛𝑖 to 𝑛𝑗 . 𝑇 𝑆𝑖𝑗 depends on the
content of posts and comments sent from 𝑛𝑖 to 𝑛𝑗 . Examples of possible topics are “Technology”,
“Health”, “Entertainment”, “Politics”, etc.
Starting from this model, we are able to define some possible versions of status and value
assortativity.
The first status assortativity we consider is the most classical one, i.e., degree assortativity.
Based on our model, it can be defined as:
∑︀
𝑎𝑖𝑗 ∈𝐴 (𝑘𝑖 − 𝑘)(𝑘𝑗 − 𝑘)
𝑟𝐷 = ∑︀ 2
(2.2)
𝑎𝑖𝑗 ∈𝐴 (𝑘𝑖 − 𝑘)
In this case, 𝐴 is the set of arcs of 𝒩 , 𝑘𝑖 (resp., 𝑘𝑗 ) is the degree (intended as the sum of
indegree and outdegree) of 𝑛𝑖 (resp., 𝑛𝑗 ) and 𝑘 is the average degree of the nodes of 𝒩 . In this
formula, the numerator is the result of the product of the degree deviations from the mean
for each pair of connected nodes. It represents the covariance of degrees between all pairs of
connected nodes. A positive numerator indicates a tendency for nodes with high degree to
connect with other nodes with high degree (assortativity or assortative mixing), while a negative
numerator denotes a tendency for nodes with high degree to connect with other nodes with
low degree (disassortativity or disassortative mixing). The denominator represents the variance
in the degree of all nodes. It acts as a normalization factor and assures us that the values of 𝑟𝐷
are within the real range [−1, 1], where 1 denotes perfect assortativity, -1 represents perfect
disassortativity and 0 indicates the lack of any assortativity relationships.
Since our model associates each node 𝑛𝑖 ∈ 𝑁 with the number 𝑓𝑖 of its followers, it is possible
to think of a second version of status assortativity that we call weighted degree assortativity. It
can be defined as follows:
∑︀
𝑎 ∈𝐴 𝛼(𝑛𝑖 , 𝑛𝑗 ) · (𝑘𝑖 − 𝑘𝑤 )(𝑘𝑗 − 𝑘𝑤 )
𝑟𝑊 𝐷 = ∑︀ 𝑖𝑗 (2.3)
(𝑘𝑖 −𝑘𝑤 )2 +(𝑘𝑗 −𝑘𝑤 )2
𝑎𝑖𝑗 ∈𝐴 𝛼(𝑛𝑖 , 𝑛𝑗 ) · 2
Here, 𝑁 is the set of nodes of 𝒩 . 𝛼(𝑛𝑖 , 𝑛𝑗 ) is an aggregation function of the number of
followers of 𝑛𝑖 and 𝑛𝑗 . It is possible to think of various aggregation functions, for example
𝑓 +𝑓
𝛼(𝑛𝑖 , 𝑛𝑗 ) = 𝑚𝑎𝑥(𝑓𝑖 , 𝑓𝑗 ), 𝛼(𝑛𝑖 , 𝑛𝑗 ) = 𝑚𝑖𝑛(𝑛𝑖 , 𝑛𝑗 ), 𝛼(𝑛𝑖 , 𝑛𝑗 ) = 𝑓𝑖 + 𝑓𝑗 , 𝛼(𝑛𝑖 , 𝑛𝑗 ) = 𝑖 2 𝑗 .
We used the latter function in our experiments. Unlike degree assortativity, weighted degree
assortativity takes into account not only the degree of the nodes involved in the interactions
but also their influence, as measured by their number of followers.
In this formula, the numerator computes a weighted degree covariance between pairs of
connected nodes, where 𝛼(𝑛𝑖 , 𝑛𝑗 ) is an aggregation function of the number of followers of 𝑛𝑖
and 𝑛𝑗 , and acts as a weight. Therefore, the numerator captures the tendency of influential
nodes to connect with other influential nodes and takes into account both the number of
connections and the level of influence of connected nodes. The denominator computes the
variance of the nodes’ degrees, weighted against the nodes’ level of influence, and provides a
basis for comparison with the numerator that takes into account both the degree and the level
of influence of connected nodes. It acts as a normalization factor by ensuring that the values
of 𝑟𝑊 𝐷 are within the real range [−1, 1], where 1 denotes complete assortativity, -1 indicates
complete disassortativity, and 0 represents the lack of any assortativity relationship.
Weighted degree assortativity 𝑟𝑊 𝐷 is useful in networks where the number of connections
(degree) alone does not fully capture the importance or influence of a node. For example, in
the case of content dissemination, a user with few connections but a large number of followers
can have a significant impact. In a case like this, 𝑟𝑊 𝐷 provides a more nuanced version of
assortativity, reflecting not only how nodes are connected but also how their influence or
popularity contribute to the network’s potential. In particular, if we consider high-influence
networks, where influence plays a critical role in shaping interactions, this assortativity can
better capture the dynamics of interactions between nodes than degree assortativity. Moreover,
in heterogeneous networks, with a high variance of influence of nodes, 𝑟𝑊 𝐷 can show connec-
tivity patterns that 𝑟𝐷 could overlook. This is especially relevant when we want to analyze how
content dissemination or engagement patterns are correlated with user influence.
Having seen two examples of status assortativity, we now introduce a definition of value
assortativity. As in the case of status assortativity, the following is not the only possible
definition of value assortativity, but in the future other definitions could be introduced based
on research needs. Since value assortativity concerns content, the topic set 𝑇 𝑆𝑖𝑗 will play a key
role in its definition. The formula we propose for value assortativity is the following:
∑︀
𝑎 ∈𝐴 |𝑇 𝑆𝑖𝑗 | · (𝑇𝑖 − 𝑇 )(𝑇𝑗 − 𝑇 )
𝑟𝑉 = ∑︀ 𝑖𝑗 (2.4)
(𝑇𝑖 −𝑇 )2 +(𝑇𝑗 −𝑇 )2
𝑎𝑖𝑗 ∈𝐴 |𝑇 𝑆𝑖𝑗 | · 2
As can be seen, the structure of this formula is similar to that of the formula of 𝑟𝑊 𝐷 except
that, in this case, instead of the function 𝛼(𝑛𝑖 , 𝑛𝑗 ) and node degrees (which are all structural
measures), we have the cardinality of the set 𝑇 𝑆𝑖𝑗 and the variables 𝑇𝑖 , 𝑇𝑗 and 𝑇 . In particular,
recall that 𝑇 𝑆𝑖𝑗 denotes the set of topics related to the comments from 𝑛𝑖 to 𝑛𝑗 . Instead, 𝑇𝑖
(resp., 𝑇𝑗 ) is a variable indicating the number of topics characterizing all messages posted by 𝑛𝑖
(resp., 𝑛𝑗 ), while 𝑇 is the average number of topics of the messages posted by all users of 𝒩 .
In this formula, the numerator is given by the product of the differences between the average
number 𝑇 of topics handled by the connected nodes of 𝒩 and the number of topics handled
by each pair of connected nodes in the network. Each of these differences is weighted by the
cardinality of the set 𝑇 𝑆𝑖𝑗 of topics in common between each pair of connected nodes in the
network. |𝑇 𝑆𝑖𝑗 | acts as a weight indicating how extensive the shared interests are between
the users 𝑢𝑖 and 𝑢𝑗 associated with the nodes 𝑛𝑖 and 𝑛𝑗 . Each component of the sum at the
numerator measures how similarly or dissimilarly the nodes engage in topics compared with
the average engagement level of the nodes in the network. A positive, high numerator indicates
that nodes with similar levels of engagements in topics (both in terms of number of topics and
number of common topics) tend to interact, implying value assortativity. A negative numerator
with a high absolute value denotes that nodes with different levels of engagement on topics tend
to interact, implying value disassortativity. The denominator is used to normalize the numerator
by taking into account the variance of the number of topics related to each node, again weighted
by the number of topics in common between each pair of nodes. This ensures that the value
of 𝑟𝑉 varies in the real range [−1, 1], where 1 indicates total value assortativity, -1 denotes
complete value disassortativity, and 0 represents the lack of any assortativity relationship.
3. Experiments
3.1. Dataset description
As we mentioned in the Introduction, in order to test our definitions of status and value
assortativity on Threads, we had to build an appropriate dataset, since we did not find any
available open dataset that could support our work. Once we built such a dataset, we decided
to make it available for all researchers who want to perform analyses on Threads. It can be
found at the following address: https://github.com/ecorradini/ThreadsDataset. The dataset is
anonymized to preserve user privacy. In this section, we describe in detail how we obtained it
and its main features.
To collect data from Threads we used a server with a 16 core CPU, 96 GB of RAM and Ubuntu
22.04 operating system. Threads allows access to its feed in the European Union without the
need for an account. We organized all collected data into two main files, namely: (i) posts.csv,
which records all data related to posts, and (ii) users.csv, which contains all data related to
users. In addition, we created a special folder to store all images and videos linked by posts.
In more detail, the file posts.csv has the following fields: (i) url, which indicates the web
address of the post; (ii) parent_post, which denotes the web address of the parent post, if the
original post is a comment; it is empty otherwise; (iii) user, which indicates the username of
the user who created the post; (iv) caption, which denotes any text or caption associated with
the post; (v) image_video, which indicates the name of the visual content file in the associated
folder, if the post includes images or videos; it is empty otherwise; (vi) time, which denotes the
timestamp when the post was made; (vii) likes, which denotes the number of likes received
by the post; it is set to 0 if the post received no like.
Instead, the file users.csv has the following fields: (i) url, which indicates the web address
of the user’s profile; (ii) username, which denotes the username of the user; (iii) display_name,
which indicates the user’s display name; (iv) bio, which indicates biographical information, if
it is present in the user’s profile; it is empty otherwise; (v) bio_url, which denotes web links if
they are present in the user’s profile; it is empty otherwise; (vi) followers, which indicates
the number of followers of the user; it is set to 0 if the user has no follower.
Table 1 shows some basic statistics of the network 𝒩 associated with our Threads dataset.
Statistic Value
Number of nodes 26,248
Number of arcs 39,771
Number of isolated nodes 770
Density 5.773 · 10−5
Average Clustering Coefficient 0.003197
Average indegree of nodes (excluding isolated ones) 1.56
Average outdegree of nodes (excluding isolated ones) 1.56
Table 1
Some basic statistics of the network 𝒩
3.2. Investigating status and value assortativity on Threads
The first analysis on status assortativity involved the computation of degree assortativity (see
Equation 2.2). By performing such a computation we obtained that 𝑟𝐷 = −0.0303. This
result shows that, as far as this form of assortativity is concerned, users in Threads are neither
assortative nor disassortative. This, in turn, implies that high-degree users tend to establish
connections with high-degree, medium-degree and low-degree ones, and vice versa. This
behavior differs from what happens to other social networks where researchers generally find
the existence of degree assortativity among users. It may be motivated by the fact that Threads
is still a new network and, therefore, there has not been time for backbones of influencers, or
other strong typologies of interactions among them, to form. At the moment, therefore, the
influencers’ interest in spreading their content directly to everyone, without superstructures
such as backbones with other influencers, prevails. Of course, we cannot exclude that as time
goes on, influencers’ backbone or other superstructures involving this type of users will also
form in Threads.
The second analysis on status assortativity involved the computation of weighted degree
assortativity (see Equation 2.3). By performing that computation, we obtained that 𝑟𝑊 𝐷 =
0.0157. This result differs from the previous one only in the sign; indeed, in both cases the
value of the assortativity coefficient is very close to 0. This implies that, again, no significant
assortativity or disassortativity relationships are evident among the network users. Recall
that weighted degree assortativity differs from degree assortativity in that it also takes into
account the weight of nodes, which, in our model, is given by the number of followers of the
corresponding users. Consequently, this form of assortativity focuses even more on influencers
than the previous form; here, influencers are evaluated based on not only the number of
connections they have but also the number of people who follow them. The fact that we again
obtain a substantially null assortativity value is a further confirmation that in Threads there still
does not seem to exist any superstructure (e.g., backbones) through which influencers support
each other.
After evaluating status assortativity on the Threads dataset, we moved on to consider value
assortativity on it. The first task to do was the extraction of the set of topics related to user
interactions. For this purpose, we used OpenAI’s GPT-3.5 model3 . More specifically, for each
post or comment of the dataset, we used the gpt-3.5-turbo to extract the topic that most
represented it. Proceeding in this way, we identified 846 different topics. Figure 1 shows the
distribution of the 100 most frequent ones. Note that there are 3,727 arcs whose posts and
comments feature an “Uncategorized” topic. This is mainly due to the fact that these comments
or posts consist only of emojis or single words like “Yes” or “No”. We did not consider these
comments or posts in the computation of value assortativity.
Distribution of topics
4000
3500
3000
2500
Frequency
2000
1500
1000
500
0
appreciation
religion
nature
greeting
career
uncategorized
general
language
creativity
approval
Transportation
recreation
support
behavior
appearance
entertainment
politics
technology
emotion
sport
health
food
sociality medium
communication
music
celebration
travel
humor
opinion
identity
finance
fashion
art
Education
ethic
agreement
gaming
literature
productivity
law
privacy
business
science
medium
motivation
socialization
innovation
family
photography
aesthetic
society
beauty
shopping
encouragement
compliment
self improvement
time
employment
location
weather
history
mentality health
Justice
work
philosophy
positivity
conflict
culture
expression
criticism
economy
achievement
safety
environment
crime
inspiration
psychology
diversity
praise
celebrity
confusion
legality
geography
economics
news
security
gratitude
applause
gender
memory
nostalgia
feedback
recognition
fitness
ambiguity
none
mathematics
information
identity development
excitement
Topic
Figure 1: Distribution of the 100 most frequent topics in our Threads dataset
At this point, we computed the value assortativity coefficient 𝑟𝑉 by applying Equation 2.4. At
the end of this task, we obtained a value of 𝑟𝑉 equal to 0.103. This value reveals a slight tendency
of Threads users to communicate with other users who posted similar content, and thus showed
common interests. This result suggests the existence within Threads of communities or clusters
of users who are interesed in the same topics and like to interact with each other. The positive,
but not extremely high, assortativity value can be explained by considering that a user is
generally interested in multiple topics and therefore tends to interact with other users within
multiple communities each of which could be only minimally intersecting with another.
3.3. Comparison with other social platforms
Having computed status and value assortativity in Threads, in Table 2 we compare our results
with those obtained by other researchers in the past for other social platforms.
3
www.openai.com
Source Investigated network Assorativity Type Assortativity based on Obtained result
This paper Threads Status Degree of nodes Unassortative (i.e., neutral),
𝑟 = −0.0303
This paper Threads Status Number of followers Unassortative (i.e., neutral),
𝑟 = 0.0157
This paper Threads Value Posts and Comments Assortative, 𝑟 = 0.103
[1] Several complex networks Status Degree of nodes It depends on networks
[9] Several complex networks Status Node degrees, links and weights It depends on networks
[10] Several complex networks Status Network configuration It depends on networks
[11] Arxiv:condmat Status Degree of nodes Assortative, 𝑟 ∈ [0.14, 0.35]
depending on parameters
[12] Several online social networks Status Node degrees It depends on networks
[13] Several social networks Status Social status Assortative, depending on parameters
[14] X Value Happiness Assortative, 𝑟 constant (about 0.4)
[15] X Value Subjective Well-Being Assortative, 𝑟 = [0.1, 0.4]
[16] X Value Suicide Assortative
[4] Reddit Status Degree centrality Assortative for some centrality intervals
[4] Reddit Status Eigenvector centrality Assortative for some centrality intervals
[4] Reddit Value Co-posting activity Assortative
[17] Facebook Status Age Assortative, 𝑟 = 0.226
[18] Facebook Status Membership of common platforms Assortative, 𝑟 = 0.074
[19] Facebook Status Based on bridge users Assortative, 𝑟 = 0.894
[19] X Status Based on bridge users Assortative, 𝑟 = 0.675
[20] X Status Network structure Assortative, 𝑟 = 0.996
[21] X Status Degree assortativity Assortative, 𝑟 = 0.414
[21] X Value Shared interests and language Assortative, 𝑟 positive for
all attributes considered.
[22] X Value Music Category Assortative, 𝑟 = 0.737
[22] X Value Cinema Category Assortative, 𝑟 = 0.811
[22] X Value Sport Category Assortative, 𝑟 = 0.776
Table 2
An overview of investigations on assortativity
4. Conclusions
In this paper, we proposed an in-depth study of the concept of assortativity proposed by
Newman [1]. First, we introduced the concept of status assortativity, which takes into account
the structure of the network, and that of content assortativity, which takes into account the
content exchanged between nodes, if it exists. We then applied our definitions of assortativity to
Threads. In order to perform our analysis, we needed a dataset of Threads storing the network
users, their interactions, and the content they exchanged. Unfortunately, we could not find an
existing dataset that was suitable for our purposes. Therefore, we had to build a new dataset
from scratch.
This paper should not be seen as an end point but as a starting point for further researches in
this context. In fact, in the future, we can think of designing a framework that makes it easy to
define new forms of status and value assortativity on Threads and other social networks as the
need or opportunity arises. This framework could include a machine learning component to
predict changes in assortativity based on trends that should gradually emerge in interactions
of users and the content they exchange. At a later stage, we could extend this framework to
consider other forms of network dynamics beyond assortativity. Another possible extension of
our approach would be to consider not only textual posts, but also images and videos in the
study of value assortativity. Finally, we would like to make an in-depth comparison between
X and Threads with respect to different forms of value and status assortativity, since these
two networks are direct competitors. This comparison may allow us to gain insight into the
differences in user connections and behavior in the two networks.
References
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