Network Analysis of the Egyptian Reddit Community

Samy Shaawat

1 a

, Adham Hammad

1 b

, Karim Farhat

1 c

, Mina Thabet

1 d

and Walid Gomaa

1,2 e

Faculty of Engineering, Department of Computer Science Engineering, Egypt-Japan University of Science and

Technology, Alexandria, Egypt

Faculty of Engineering, Alexandria University, Alexandria, Egypt

Keywords:

Network Analysis, Reddit, Social Media, Egyptian Community, Degree Analysis, Degree Distribution

Analysis, Clustering Coefﬁcient Analysis.

Abstract:

This paper presents a network analysis of the Reddit community focused on Egypt. We collected and con-

structed a comprehensive dataset consisting of 23,185 users and 105 Egyptian subreddits. Through network

analysis criteria such as degree analysis, degree distribution analysis, and clustering coefﬁcient analysis, we

explored the structural properties, connectivity patterns, and local clustering within the Egyptian Reddit net-

work. The ﬁndings provide insights into the community dynamics, inﬂuential users, and information ﬂow

within the network. Our study contributes to a better understanding of online communities in the context of

Egypt and sheds light on the relationships and interactions within the Egyptian Reddit community. By lever-

aging network analysis techniques, we uncover the importance of individual nodes, the distribution of node

degrees, and the formation of tightly knit groups.This study contributes signiﬁcantly to the understanding of

online communities speciﬁc to Egypt, shedding light on relationships and interactions within the Egyptian

Reddit community.

1 INTRODUCTION

Social media platforms have become an integral part

of our lives, enabling us to connect, share, and learn

from each other. Among these platforms, Reddit

stands out as one of the most popular and inﬂuential

online communities in the world.(Widman, 2022)

Reddit is a social bookmarking website that allows

users to submit, rate, and comment on various types of

content, such as news, images, videos, and text posts.

Users can join and create subreddits, which are spe-

cialized forums dedicated to speciﬁc topics or inter-

ests. According to Semrush, Reddit had more than

430 million monthly active users as of October 2021,

making it the fourth most visited site in the U.S. and

the sixth most visited worldwide.(Diaz and Mellon,

2021)

Reddit is a rich source of data and insights for re-

searchers who want to study online communities, so-

cial network analysis, and user behavior. However,

https://orcid.org/0009-0002-2679-6695

https://orcid.org/0009-0005-4587-7310

https://orcid.org/0009-0001-4777-4201

https://orcid.org/0009-0005-7368-7207

https://orcid.org/0000-0002-8518-8908

most of the existing studies on Reddit have focused

on the global or English-speaking subreddits, while

neglecting the regional or non-English subreddits that

represent diverse and vibrant communities around the

world. This oversight hampers our understanding of

the unique dynamics and contributions of these re-

gional communities.

Our research addresses this gap by focusing on

the Egyptian Reddit community, a dynamic online

community centered around topics related to Egypt.

This community comprises several subreddits cover-

ing various aspects of Egyptian culture, politics, soci-

ety, and entertainment.(Smith, 2021)

The research problem at the heart of our study is

to investigate the structure and dynamics of the Egyp-

tian Reddit community, which is largely unexplored

in the existing literature. Speciﬁcally, we aim to un-

derstand how this community functions, the patterns

of user interaction within it, and its role in shaping

discussions related to Egypt.

Our study aims to address the research problem

of the largely unexplored Egyptian Reddit community

by investigating its structure, dynamics, and contribu-

tions. Speciﬁcally, we seek to analyze the network

structure of this community, identifying key nodes,

258

Shaawat, S., Hammad, A., Farhat, K., Thabet, M. and Gomaa, W.

Network Analysis of the Egyptian Reddit Community.

DOI: 10.5220/0012205000003543

In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2023) - Volume 2, pages 258-269

ISBN: 978-989-758-670-5; ISSN: 2184-2809

inﬂuencers, and clusters of interest. Additionally,

we aim to examine user behavior patterns within the

Egyptian Reddit community, including content shar-

ing, engagement, and the dissemination of informa-

tion. Furthermore, our research endeavors to under-

stand the cultural signiﬁcance of Egyptian Reddit sub-

reddits by investigating how they contribute to discus-

sions on various aspects of Egyptian culture, politics,

society, and entertainment, both within the commu-

nity and in the broader online discourse. (Stoltenberg

et al., 2019)

The paper is organized as follows. Section 1 is

an introduction. Section 2 gives a literature review

about on social network analysis, Reddit analysis,

and network analysis in online communities. Sec-

tion 3 presents the core of our work given the overall

methodology, an overview of the network, showing

the connections between users in the Egyptian Red-

dit community through visual representation. We dis-

cuss the network’s construction and provide informa-

tion about the number of users and shared subreddits.

In Section 4 we give our empirical work and the cor-

responding analyses. Section 5 concludes the paper

with pointers to future work.

2 LITERATURE REVIEW

Social media networks like Reddit, Facebook, and

Twitter contain a wealth of data that can provide

insights into human behavior and social connec-

tions. (Telatnik, 2021)

Community Identiﬁcation on Reddit

Smith et al. analyzed the network structure of Reddit

to identify different types of user communities. Their

purpose was to understand the diverse connections

and communities on Reddit. This research provides

a general overview of how people interact and form

communities on the platform, and it is easily under-

standable for general readers. (Smith et al., 2019)

User Engagement in Reddit Communities

Xu et al. studied the degree distributions of Red-

dit communities to understand their connectivity pat-

terns. They wanted to explore the range of user en-

gagement levels within Reddit communities. By do-

ing so, they provide insights into how active users are

in different communities on Reddit. (Xu et al., 2019)

Temporal Dynamics of Twitter Networks

Johnson examined how networks change over time on

Twitter. The purpose was to understand the dynamics

of social media networks and how some communi-

ties persist while others are more temporary. This re-

search showcases the constant evolution of social me-

dia networks in a way that is easy to grasp. (Johnson,

2020)

Connectivity Patterns in Twitter

Hashtags

Park et al. analyzed local and global connectivity pat-

terns in the Twitter hashtag network. They aimed to

provide an intuitive understanding of how hashtags

are connected on Twitter, demonstrating the layered

connectivity patterns within the platform. (Park et al.,

2020)

Age-Related Differences in Facebook

Connections

Cho and Lee studied clustering coefﬁcients across dif-

ferent Facebook networks to understand how human

connections differ across age groups on social media.

Their purpose was to examine the differences in how

people of different ages connect on Facebook. This

research offers insights into age-related differences in

social media usage. (Cho and Lee, 2021)

Political Polarization on Brexit-Related

Facebook Pages

Williams and Housley analyzed Facebook pages re-

lated to Brexit to understand the level of polarization

in communities. They wanted to examine how polit-

ical polarization manifests in social networks. This

work provides a straightforward look at the ways po-

litical polarization is reﬂected in online communi-

ties. (Williams and Housley, 2021)

Global Events and Reddit Community

Connections

Ahmed et al. analyzed the network structure of Reddit

discussions about current events in Egypt. Their pur-

pose was to understand how world events shape social

connections on platforms like Reddit. This research

offers an accessible view of how global events inﬂu-

ence online communities. (M.Ahmed et al., 2022)

Idea Spread in Reddit Discussions

Xu and Ke used natural language processing tech-

niques to extract topics from Reddit comments

and constructed networks connecting comments dis-

cussing the same topics. They aimed to understand

how ideas spread and become popular within Reddit.

Network Analysis of the Egyptian Reddit Community

259

This research gives insights into the dynamics of idea

sharing within the platform. (Z.Xu and Q.Ke, 2022)

Cross-Platform Social Media

Engagement

Lee et al. took a cross-platform view and constructed

social media networks that connected the same users

across multiple platforms. Their purpose was to

understand how individuals engage across different

social media websites. This work provides an

intuitive understanding of how people connect and

interact on various platforms. (S.Lee et al., 2023)

These previous studies provide insights into hu-

man behavior and social connections on social me-

dia platforms. They explore various aspects such as

community formation, user engagement, network dy-

namics, connectivity patterns, age group differences,

political polarization, and the impact of world events.

3 METHODOLOGY

3.1 Dataset

In this section, we will explain the process of prepar-

ing and constructing a dataset focused on Egyptian

subreddits. Our goal was to create a comprehensive

dataset for analyzing discussions and content related

to Egypt, utilizing the open API provided by Reddit

and the Python PRAW library.

3.1.1 Dataset Collection and Preprocessing

The initial step in dataset preparation involved col-

lecting data from Reddit, speciﬁcally targeting Egyp-

tian subreddits.

Identiﬁcation of Egyptian Subreddits: Using the

open Reddit API and the Python PRAW library, we

conducted a search for subreddits related to Egypt.

Our search query looked for subreddits where the

term ’Egypt’ (in English) appeared in either the name

or the content. This search resulted in the retrieval of

105 relevant subreddits, which we saved for further

processing.

Extraction of Usernames from Subreddits: Using

the authenticated Reddit API, we proceeded to extract

active usernames from the collected Egyptian subred-

dits. We iterated through each subreddit and retrieved

the submissions, including both posts and comments.

From these submissions, we extracted the usernames

of the authors. We included only those usernames that

were associated with at least one submission within

the subreddit, ensuring that we captured usernames of

active participants. We then removed duplicate user-

names, resulting in a ﬁnal list of unique usernames

associated with the Egyptian subreddits. We saved

this list in a text ﬁle named after the corresponding

subreddit. The size of the dataset is shown in table 1,

and the details of the dataset are discussed in the next

section.

Table 1: Dataset Size.

Number of rows 23,184

Number of Columns 24

3.1.2 Dataset Construction

To construct the dataset, we generated text ﬁles,

with each ﬁle representing a subreddit and contain-

ing the active usernames associated with that subred-

dit. Next, we iterated over all the text ﬁles, extracting

usernames and linking them to the subreddits where

they were active. Each username was assigned its own

column in the dataset, and in the respective column,

we marked the subreddits where the user was active.

The dataset consists of 23,185 rows as shown in

the pervious table 1, each representing a unique user-

name. The dataset comprises 24 columns, with the

ﬁrst column dedicated to usernames, while the re-

maining 23 columns correspond to the subreddits that

each username is subscribed to. The reason for the

23 columns, rather than the total number of subred-

dits (105) in the dataset, is that no username is sub-

scribed to all 105 subreddits. The maximum number

of subreddits to which a username is subscribed is 23.

As a result, the dataset is structured to include only

the columns to represent the subreddit subscriptions

of each username.

Once the dataset was created, we conducted a

thorough quality check to ensure data integrity. We

examined the dataset, searching for null values or du-

plicates. If any were found, we promptly removed

them from the dataset. This process was crucial in

ensuring the reliability and accuracy of the dataset.

3.2 Network Overview

The network constructed from the dataset provides

a visual representation of the connections between

users based on their shared subreddit interests. The

nodes represent individual users, while the undirected

edges depict the presence of common subreddits be-

tween pairs of users. There are no weights assigned

to the edges, and whether there are many or just one

Click to see our Dataset and Network Implementation

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

260

subreddit in common, they are handled in the same

way. This network structure enables the analysis of

relationships and information ﬂow within the Egyp-

tian Reddit community ﬁg. 1 shows a sample of a net-

work constructed using the collected dataset.

Figure 1: Connected component number 10. It represents

the subreddit named “EgyptianHistoryMemes” and con-

sists of 5 members: IacobusCaesar, Joseph-Memestar, Ro-

roS4321, AnticRetard, and Memetaro-Kujo.

3.2.1 Network Construction

The network is constructed using python modules:

Pandas, NetworkX, and Matplotlib. The Network

successfully constructed as shown in ﬁg. 2 and 3

to analyze the structure and dynamics of the Egyp-

tian Reddit community. This network, comprised of

23,185 users and 105 subreddits

, forms the basis

for further exploration and investigation into users in-

teractions, inﬂuential nodes, and community dynam-

ics (Powell and Hopkins, 2015a).

Figure 2: Fully Generated Network (with labels).

3.2.2 Network Metrics

The following are some metrics we used to analyze

the network.

1. Node Count: The dataset comprises 23,185

unique users (nodes) who follow Egyptian subred-

dits. These users form the nodes of the network.

2. Edge Count: The edges in the network indicate

the presence of shared subreddits between pairs of

Figure 3: Fully Generated Network (without labels).

users. The total number of edges in the network

represents the level of interconnectedness, overlap

in subreddit interests among users. There are a

total of 6,877,773 edges.

Table 2: Number of Nodes and Edges.

Network Metrics Value

Number of Nodes 23,185

Number of Edges 6,877,773

3.3 Network Analysis Criteria

Network analysis is a powerful methodology for

studying and understanding complex systems rep-

resented as graphs or networks. It involves ana-

lyzing the structure, relationships, and dynamics of

nodes and edges within the network to gain insights

into the underlying system’s behavior and character-

istics. Network analysis encompasses various tech-

niques and measures that provide valuable informa-

tion about connectivity, centrality, community struc-

ture, and other properties of the network (Powell and

Hopkins, 2015b).

3.3.1 Degree Analysis

Degree analysis, also known as degree centrality, is

a network analysis measure that focuses on the num-

ber of connections or edges that a node (in this case,

Egyptian Reddit users) has in the network. It helps

identify highly connected users who follow a large

number of subreddits, indicating their active engage-

ment and broad range of interests within the Reddit

community. Degree analysis provides insights into

the overall network structure, popular subreddits, and

inﬂuential users, revealing patterns of information

sharing and community interactions (Vasques Filho

and O’Neale, 2018).

Network Analysis of the Egyptian Reddit Community

261

3.3.2 Degree Distribution Analysis

Degree distribution analysis in the network analysis

of Reddit for Egyptians examines the distribution of

node degrees, which represent the number of connec-

tions or edges that users have in the network. By

analyzing the frequency or probability distribution

of these degrees, we can understand the prevalence

and distribution of user engagement and participa-

tion within the Egyptian Reddit network. Skewness

and kurtosis measures provide valuable insights into

the connectivity patterns, centralization, and struc-

tural characteristics of the network, revealing the con-

centration of highly connected users and the overall

distribution of degrees.To calculate the average de-

gree for undirected network, let N be the number of

nodes, and L be the number of edges; then the ex-

pected node degree can be calculate as shown in Eq. 1

(Vasques Filho and O’Neale, 2018).

< K >=

∑

deg(i)

(1)

3.3.3 Clustering Coefﬁcients

Clustering coefﬁcient analysis provides insights into

the local connectivity and clustering tendencies

within the Egyptian Reddit community. It helps iden-

tify tightly interconnected clusters or communities

within the network, highlighting users who actively

engage and form connections with fellow Egyptian

users. The clustering coefﬁcient analysis allows us to

understand the local connectivity, community struc-

tures, and interaction patterns within the Egyptian

Reddit community (Clemente and Grassi, 2018).

3.3.4 Network Type

Network type analysis categorizes the network based

on its structural properties. This analysis helps us un-

derstand the fundamental characteristics of the net-

work, such as connectivity patterns and overall struc-

ture. Common network types include random net-

works, small-world networks, clustered networks,

and sparse networks. By identifying the network type,

we gain insights into the connectivity patterns, com-

munity structures, and the behavior within the Egyp-

tian Reddit community (Zaidi, 2012).

3.3.5 Centrality Analysis

Centrality analysis in the network analysis of Reddit

for Egyptians helps identify important nodes based

on measures like Degree centrality, Closeness cen-

trality, Betweenness centrality, and Eigenvector cen-

trality. Degree centrality assesses the number of con-

nections, Closeness centrality measures accessibility,

Betweenness centrality identiﬁes bridge nodes, and

Eigenvector centrality considers connections with in-

ﬂuential nodes. Centrality analysis reveals key nodes

that play signiﬁcant roles in the structure and infor-

mation ﬂow of the Egyptian Reddit network (Gomez,

2019).

3.3.6 Static Community Discovery

Community discovery involves partitioning the net-

work into groups of nodes called communities. These

communities consist of nodes that have stronger con-

nections or similarities within the same community

compared to nodes in different communities. Com-

munity detection algorithms are applied to identify

these meaningful communities, providing insights

into the modular structure and functional units within

the Egyptian Reddit network. The quality of the de-

tected communities is evaluated using metrics such

as modularity and conductance. Visualizations aid in

interpreting the communities, revealing relationships

and interactions between nodes. Further analysis in-

volves studying the characteristics and functions of

nodes within each community, contributing to a com-

prehensive understanding of the network’s structure

and dynamics (Zhu et al., 2020).

3.3.7 Dynamic Community Discovery

Dynamic community discovery network analysis of

Reddit for Egyptians involves identifying and track-

ing communities in evolving graphs that represent the

interactions between users over time. This analysis

captures the changing structure and temporal evolu-

tion of communities within the Egyptian Reddit net-

work. Various approaches, such as the Label Propa-

gation Algorithm, graph partitioning techniques, the

Louvain Method, and the Infomap algorithm, can be

employed to address the challenges of dynamic com-

munity discovery. These approaches help uncover

how communities form, evolve, and interact within

the evolving Egyptian Reddit network (Thompson

et al., 2017).

3.3.8 Connected Components Analysis

Connected components analysis in the network anal-

ysis of Reddit for Egyptians identiﬁes distinct clus-

ters or subgraphs within the larger network. It par-

titions nodes into subsets called connected compo-

nents, where nodes within a component are mutually

reachable. This analysis helps reveal the underly-

ing structure, connectivity patterns, and isolated re-

gions within the Egyptian Reddit network. Determin-

ing component sizes and visualizing the components

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

262

provide insights into cluster distribution and the main

connected part of the graph, contributing to a better

understanding of connectivity and identifying isolated

regions or clusters (Huang et al., 2009).

3.3.9 Density Analysis

Density analysis in the network analysis of Reddit for

Egyptians quantiﬁes the level of connectivity or spar-

sity within the Egyptian Reddit network. It measures

the ratio of the number of edges present in the graph

to the maximum possible edges as shown in Eq. 2.

Higher density values indicate a more densely con-

nected network, while lower density values suggest

a more sparse or fragmented network. Density anal-

ysis provides insights into the overall connectivity

and structure of the Egyptian Reddit network, high-

lighting the level of engagement and information ﬂow

within the community (Goswami et al., 2018).

DensityRatio =

#Edges

#Nodes ∗ (#Nodes − 1)/2

(2)

3.3.10 Path Analysis

Path analysis in the network analysis of Reddit for

Egyptians involves studying the routes between nodes

to understand connectivity, reachability, and ﬂow

within the network. It includes ﬁnding the shortest

path, analyzing reachability, studying path length dis-

tribution, examining ﬂow and trafﬁc patterns, and un-

covering connectivity patterns like hubs and bridges.

Path analysis provides valuable insights into the struc-

ture and dynamics of the Egyptian Reddit network,

aiding in understanding information ﬂow and com-

munication within the community (Thompson et al.,

2017). Let N be the number of nodes in the network,

then the expected path distance can be calculated as

shown in Eq. 3

< D >=

∑

dist(i, j)





(3)

4 RESULTS AND DISCUSSION

4.1 Degree Analysis

The degree of a node in a graph is the number of edges

connected to that node. In our study, we analyzed the

degrees of nodes in the graph and made the observa-

tion prrovided in table. 3.

The degree analysis of the network revealed inter-

esting observations. The minimum degree of 0 indi-

cated the presence of isolated nodes, while the max-

imum degree of 13019 pointed to highly connected

Table 3: Degree general statistics.

Observation Value

Minimum degree 0

Maximum degree 13,019

Average degree 593.6

Total number of degrees 137,555,46

nodes or hubs. The average degree of 593.602 re-

ﬂected the typical connectivity of nodes, and the to-

tal number of degrees was 13,755,546, illustrating the

overall size and complexity of the network. These

ﬁndings offer insights into the network’s structure and

potential real-world applications. To deepen our un-

derstanding, we ask: How does the degree distribu-

tion compare to other regional Reddit communities,

and what role do these highly connected nodes play

in information ﬂow and community cohesion within

this network?

4.2 Degree Distribution Analysis

The degree distribution analysis examines the varia-

tion and patterns of node connectivity in a network. It

provides insights into inﬂuential nodes, information

ﬂow, and network structure. This analysis helps un-

cover the distribution characteristics of degrees in the

network.

Figure 4: Degree distribution of the Egyptian reddit net-

work.

Table 4: Degree distribution general statistics.

Observation Value

Range of Degree Distribution 0 to 13,019

Average Degree 593.6

Standard Deviation 257.98

Skewness Positive

Kurtosis Leptokurtic

The degree distribution analysis reveals important

characteristics of the network’s connectivity patterns

as provided in table. 4. The observed range of de-

grees from 0 to 13,019 indicates substantial variation

in the number of edges per node. The average degree

of 593.602 with a standard deviation of 257.982 signi-

Network Analysis of the Egyptian Reddit Community

263

ﬁes the typical number of connections and the degree

of variation. The positively skewed distribution sug-

gests that most nodes have low degrees, while a few

nodes exhibit high degrees, highlighting an uneven

distribution of edges. And this is as well indicated

in the histogram shown in ﬁg. 4. Additionally, the

leptokurtic distribution shows a sharp peak and heavy

tails, indicating a concentration of nodes around the

average with a presence of nodes with signiﬁcantly

higher degrees. This analysis not only help us com-

prehend network properties but also lay the ground-

work for addressing the following research questions:

How does this degree distribution compare to other

regional Reddit communities, and what role do highly

connected nodes play in inﬂuencing network dynam-

ics, including information dissemination efﬁciency?

4.3 Clustering Coefﬁcients

The clustering coefﬁcient ranges from 0 to 1, where

0 means that none of the node’s neighbors are con-

nected to each other and 1 means that all the node’s

neighbors are connected to each other. The average

clustering coefﬁcient ranges from 0 to 1, where 0

means that there is no clustering at all and 1 means

that there is perfect clustering. The average clustering

coefﬁcient of the graph was calculated to be 0.976.

Average clustering and average degree were used to

determine Network Type.

Figure 5: Histogram of clustering coefﬁcients for the Egyp-

tian reddit network.

Our observation is that the graph exhibits a re-

markably high level of clustering as shown in ﬁg. 5,

with the majority of nodes forming triangles with

their neighbors, indicating strong local connectivity

and the presence of tightly-knit communities. The

presence of numerous cliques or communities further

underscores the pronounced clustering in the graph.

Additionally, the graph’s connectivity patterns sug-

gest a departure from randomness or sparsity, as there

are abundant common friends among the nodes, im-

plying a dense network structure with interconnected

nodes. This observation highlights the cohesive na-

ture of the graph, with distinct communities and co-

hesive clusters contributing to its overall structure and

connectivity. The high level of clustering observed in

the network of the Egyptian Reddit community could

be attributed to factors such as the shared cultural and

linguistic background of the users, as well as potential

ofﬂine interactions and shared experiences. The clus-

tering effect may be intensiﬁed by the predominance

of Arabic subreddits, fostering stronger connections

among users with similar interests and cultural afﬁli-

ations.

4.4 Network Type

As mentioned before the degree distribution and clus-

tering coefﬁcient can provide insights into the net-

work type, our observations are shown in table 5. fol-

lowing are the measures used to infer the network

type:

4.4.1 Degree Distribution

Start by calculating the degree of each node, which

represents the number of edges connected to it. Then,

examine the degree distribution, which shows the fre-

quency distribution of node degrees. If the degree

distribution follows power-law distribution which im-

plies a ”rich-get-richer” phenomenon, where a small

number of nodes acquire a disproportionately large

number of connections while the majority of nodes

have relatively fewer connections, it indicates a scale-

free network. On the other hand, if the degree dis-

tribution is more uniform or bell-shaped, it suggests

a random network where connections formed ran-

domly, no speciﬁc pattern or structure,relatively uni-

form distribution of connections and no dominant

hubs or regular network where there is a structured

pattern of connections, equal number of connections

for each node and often forms clusters or neighbor-

hoods, respectively.

4.4.2 Clustering Coefﬁcient

Calculate the clustering coefﬁcient for each node,

which measures the tendency of nodes to cluster to-

gether. Compute the average clustering coefﬁcient

for the entire graph by averaging the clustering co-

efﬁcients of all nodes. If the average clustering co-

efﬁcient is much higher than expected in a random

graph with the same size and degree distribution, it

indicates highly clustered or small-world network in

which tightly connected clusters and short distances

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

264

between nodes exist and it enables efﬁcient commu-

nication. If it’s close to the expected value of a ran-

dom graph, it suggests a random network where con-

nections between nodes are formed randomly without

any speciﬁc pattern and a close-to-zero average clus-

tering coefﬁcient suggests a sparse or low clustering

network which has few connections between nodes

and limited clustering and it has a low density of con-

nections, long average distances between nodes and

limited inter-connectivity.

Table 5: Network type of the Egyptian reddit network.

Observation Value

Network clustering High

Network connectivity High

Network Type Small World Network.

4.5 Centrality Analysis

In this section, we present a centrality analysis of the

Egyptian reddit network, focusing on a user named

Wil, who exhibits remarkable characteristics in terms

of his Degree Centrality, Betweenness Centrality,

Closeness centrality, and Eigenvector centrality. Wil

is a member of our dataset, speciﬁcally identiﬁed in

row 19206. Our observations indicate that Wil is

highly inﬂuential and well connected within the net-

work.

Wil’s centrality within the network can be as-

sessed through various measures as provided in ta-

ble. 6, providing insights into his active engagement

and inﬂuential role. Degree centrality, quantiﬁed as

0.561842, indicates that Wil follows 23 out of the

105 subreddits in the network, highlighting his ac-

tive participation and interest in a signiﬁcant por-

tion of the network’s content. Moving on to be-

tweenness centrality, Wil’s score of 0.269488 sug-

gests his crucial position as a bridge connecting dif-

ferent parts of the network, facilitating communica-

tion and the ﬂow of information between nodes. This

implies that he plays a vital role in maintaining the

network’s connectivity and enabling efﬁcient infor-

mation dissemination. Wil’s high closeness centrality,

measured at 0.692209, demonstrates his proximity to

other nodes in the network, allowing information to

quickly spread through the network via him and en-

abling efﬁcient dissemination of information. More-

over, Wil’s eigenvector centrality score of 0.036501

highlights his connections to other inﬂuential nodes

in the network, enhancing his potential for inﬂuence

within the network.

Table 6: Centrality metrics of the user named Wil.

Centrality Metric Value

Degree centrality 0.561842

Betweenness centrality 0.269488

Closeness centrality 0.692209

Eigenvector centrality 0.036501

4.6 Static Community Discovery

Static Community discovery was performed using

rigorous methodology. Community detection algo-

rithms, speciﬁcally Louvain or Girvan-Newman, were

employed to identify distinct communities within the

network. The quality of the communities was eval-

uated using modularity, while community sizes and

overlap were analyzed. Where available, the detected

communities were validated against groundtruth. For

evolving networks, temporal dynamics were consid-

ered, and the results were interpreted within the net-

work’s context. The analysis was reﬁned iteratively

based on network characteristics.

We observed that the Louvain algorithm, emerges

as a widely used community detection algorithm that

effectively identiﬁes communities in networks. By it-

eratively optimizing the modularity measure, which

quantiﬁes the quality of network division, the Louvain

algorithm demonstrates its ability to partition net-

works into cohesive communities as shown in ﬁg. 6.

This observation underscores the algorithm’s signiﬁ-

cance in the ﬁeld of network analysis, where it pro-

vides a valuable tool for uncovering underlying com-

munity structures within complex networks.

Figure 6: 50 (Static) communities in the Egyptian Reddit

network.

In ﬁg. 6 the colored circles represent 50 commu-

nities (provided in table. 7) in the Egyptian reddit net-

work and the distance between the circles shows how

different the sizes of these communities are. When

circles are close together, it means the communities

Network Analysis of the Egyptian Reddit Community

265

Table 7: Static Community Discovery.

Static

Community

no.

No. of

Elements

Static

Community

no.

No. of

Elements

0 217 25 559

1 758 26 709

2 512 27 368

3 717 28 751

4 916 29 509

5 800 30 1

6 617 31 1

7 416 32 24

8 785 33 1

9 625 34 4

10 2228 35 9

11 777 36 1

12 1316 37 1

13 638 38 5

14 491 39 5

15 728 40 2

16 1267 41 88

17 399 42 3

18 732 43 1

19 766 44 5

20 809 45 1

21 952 46 1

22 751 47 1

23 746 48 637

24 522 49 1

have a similar number of users, mostly consisting of

a large number of usernames and if the circles are far

apart, it means there is a big difference in the number

of users between those communities, typically rep-

resenting communities with a small number of user-

names.

4.7 Dynamic Community Discovery

Dynamic community discovery was conducted using

a systematic approach. Algorithms designed for dy-

namic networks were utilized, applying them to each

time slice with a deﬁned temporal resolution. Com-

munity stability, persistence, evolution, and changes

were analyzed, considering temporal metrics for ad-

ditional insights.

We observed that Label Propagation algorithm,

stands out as an efﬁcient approach for community

detection in graphs, compared to other algorithms,

it is computationally more efﬁcient, ﬂexible in han-

dling different graph types, and does not require prior

knowledge of the number of communities. However,

it may be sensitive to noise and highly connected

nodes and may not capture global community struc-

ture as effectively. This semi-supervised algorithm

leverages the network structure and neighbor labels to

assign community labels to nodes. By propagating la-

bels iteratively throughout the network, the algorithm

reaches a stable state where each node is assigned a la-

bel that maximizes agreement with its neighbors. The

Label Propagation algorithm’s ability to exploit local

connectivity patterns and iteratively reﬁne community

assignments highlights its effectiveness as a powerful

tool for identifying communities in graphs.

The results were visualized as shown in ﬁg. 7, the

colored circles represent 39 communities (provided in

table. 8) in the Egyptian reddit network. The arrange-

ment and distance between the circles show how the

communities change over time or in different states.

When circles are close together, it means the commu-

Figure 7: 39 (Dynamic) Communities in Egyptian reddit

network.

Table 8: Dynamic Community Discovery.

Dynamic

Community

No.

No. of

Elements

Dynamic

Community

No.

No. of

Elements

0 13468 21 16

1 8189 22 3

2 368 23 1

3 3 24 5

4 1 25 5

5 66 26 3

6 28 27 1

7 2 28 9

8 1 29 1

9 24 30 2

10 1 31 15

11 4 32 2

12 9 33 9

13 1 34 1

14 1 35 153

15 5 36 243

16 208 37 12

17 5 38 1

18 2 39 217

19 88

20 1

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

266

nities at that time or state have a similar number of

users, mostly consisting of a large number of user-

names. If the circles are far apart, it means there is a

signiﬁcant difference in the number of users between

those communities, typically representing communi-

ties with a small number of usernames.

4.8 Connected Components Analysis

The graph analysis reveals the presence of 19 con-

nected components, indicating that the graph is di-

vided into distinct groups of nodes with each forming

a connected component, a sample of connected com-

ponent no. 10 is shown in ﬁg. 1. These components

are separate entities and lack direct connections be-

tween them. Among these components, Component

1 stands out with the maximum number of elements,

reaching 23,042. Notably, the elements within com-

ponent 1 do not belong to the same subreddit, suggest-

ing a diverse composition. Additionally, the analysis

highlights that 11 components consist of a single ele-

ment, while 8 components comprise more than one el-

ement. This observation with components sizes’ pro-

vided in table. 9 signiﬁcantly impacts the density ra-

tio, emphasizing the variation in component sizes and

the presence of both isolated and interconnected sub-

structures within the graph.

Table 9: Connected Components Analysis.

Component Size Component Size

1 23042 11 2

2 1 12 88

3 1 13 3

4 1 14 1

5 4 15 1

6 9 16 5

7 1 17 1

8 1 18 1

9 5 19 1

10 5

4.9 Density Analysis

Density is a measure of how connected a graph is,

calculated as the ratio of the number of edges to the

number of possible edges in a graph.

#PossibleEdges =

(#Nodes) ∗ (#Nodes − 1)

(4)

DensityRatio =

#Edges

#PossibleEdges

(5)

The analysis of the network reveals that it is rel-

atively sparse with fewer edges compared to a fully

connected network. This is supported by the higher

count of disconnected components compared to con-

nected components. The network consists of 23,173

nodes and 6,877,773 edges, indicating the relation-

ships between these entities. The density ratio, cal-

culated as 0.025617 using Eq. 5, conﬁrms the sparse

nature of the network, with lower values indicating

fewer connections. These observations shown in ta-

ble. 10 are crucial for understanding the network’s

connectivity patterns and analyzing the data repre-

sented by its nodes and edges.

Table 10: Density Analysis Observations.

Observation Value

Network sparsity Relatively sparse

Number of Nodes 23,173

Number of Edges 6,877,773

Number of possible edges 268,482,378

Density Ratio 0.025617

4.10 Path Analysis

Many algorithms have been developed for path anal-

ysis, but among these techniques the most important

and popular one is the shortest path analysis. Shortest

path is implemented by using the connected compo-

nents output ﬁle and randomly choose two different

usernames from each component. In total there are

19 components, 10 of which each has one element, so

they were ignored and we focused on 9 other compo-

nents which have more than one element. As shown

in table. 11: component 1, along with components 5,

6, 9, 10, 11, 12, 13, and 15, have more than one user-

name, these components differ from the others, which

have only one username. Component 1 is unique be-

cause it contains usernames from multiple subreddits,

while the other components have usernames within a

single subreddit and this distinction is important be-

cause it affects the path length between usernames. In

component 1, the path length can be equal to 2 be-

cause there may be an intermediate node between the

source and target usernames, so this means there is

an extra step in the path due to the multiple subreddit

connections within component 1.

In contrast, the other components have a path

length of 1 because the usernames are all in the same

subreddit. This direct path does not require any in-

termediate nodes between the source and target user-

names.

Network Analysis of the Egyptian Reddit Community

267

Table 11: Shortest path results.

Component Source Target Shortest Path Length

1 gqn cLoUt diDDit 2

5 kendralinnette odedi1 1

6 happyboy13 ProgaPanda 1

9 fatrna ezzouhry AEssam 1

10 lacobusCaesar Memetaro Kujo 1

11 lnTheKurry RealHistoryMashup 1

12 dishonoredgraves LimeAndTacos 1

13 CASCADE 999 Trainer Opposite 1

15 mufﬂed savior 3pr7 1

5 CONCLUSIONS

In this research paper we focused on analyzing the

Reddit network for Egyptians. We followed a step-

by-step methodology to collect and preprocess the

dataset, resulting in a comprehensive dataset with

23,185 unique users and 105 Egyptian subreddits.

The network constructed from the dataset provided

a visual representation of the connections between

users based on their shared subreddit interests. With

6,877,773 edges, the network showed a signiﬁcant

level of interconnectedness among users.

Through the application of various network anal-

ysis techniques, such as degree analysis, degree dis-

tribution analysis, clustering coefﬁcient analysis, and

network type analysis, we gained insights into the

characteristics of the network. These analyses helped

us understand the degrees of nodes, the distribution

of degrees, the level of clustering within the network,

and the network’s overall structural properties.

Our research contributes signiﬁcantly to the body

of knowledge surrounding the Egyptian Reddit com-

munity, enriching our understanding of its dynam-

ics and underlying structures. These ﬁndings hold

practical relevance for the identiﬁcation of inﬂuential

users, the study of information propagation, and the

exploration of community frameworks. Researchers

and community managers alike can leverage these in-

sights to make more informed decisions and foster

more effective engagement within the Egyptian Red-

dit community.

However, it is essential to acknowledge the limi-

tations of our study. While our analysis provides a ro-

bust foundation for understanding the Egyptian Red-

dit network, the dynamic nature of online communi-

ties means that our ﬁndings may not be static over

time. Future research endeavors should consider in-

corporating temporal analysis to capture the evolving

nature of the network. Furthermore, the incorpora-

tion of sentiment analysis could unveil the emotions

and opinions expressed within Egyptian subreddits,

adding depth to our understanding.

Looking beyond the conﬁnes of this study, similar

analyses can be extended to other communities and

populations, such as the broader Arab and African

communities, shedding light on the evolving land-

scape of underprivileged countries and regions.

At the end, our research offers valuable in-

sights into the Egyptian Reddit network, illuminat-

ing its inner workings and serving as a launchpad

for future studies and community engagement efforts.

By addressing the unique characteristics of this re-

gional Reddit community, we advance the frontiers

of knowledge in online community analysis and offer

a roadmap for future investigations in this dynamic

ﬁeld.

REFERENCES

Cho, H. and Lee, S. (2021). Clustering coefﬁcients of face-

book networks: A demographic analysis. Social Net-

work Analysis and Mining, 11(1):17.

Clemente, G. and Grassi, R. (2018). Directed clustering in

weighted networks: A new perspective. Chaos, Soli-

tons & Fractals, 107:26–38.

Diaz, J. and Mellon, R. (2021). What is reddit and how to

use it: The deﬁnitive guide.

Gomez, S. (2019). Centrality in Networks: Finding the

Most Important Nodes, pages 401–433.

Goswami, S., Murthy, C., and Das, A. K. (2018). Sparsity

measure of a network graph: Gini index. Information

Sciences, 462:16–39.

Huang, C.-Y. R., Lai, C.-Y., and Cheng, K.-T. T. (2009).

CHAPTER 4 - Fundamentals of algorithms, pages

173–234. Morgan Kaufmann, Boston.

Johnson, Y. (2020). Measuring network rewiring over time.

PLOS ONE, 15(2):e0229025.

M.Ahmed, A.Mahmood, and M.Salim (2022). Network

structure of reddit discussions about current events in

egypt. Social Network Analysis and Mining, 12(1):11.

Park, J., Lee, S., and Kim, J. (2020). Local and global con-

nectivity patterns in the twitter hashtag network. Sci-

entiﬁc Reports, 10(1):1083.

Powell, J. and Hopkins, M. (2015a). Graph analytics soft-

ware libraries. In Powell, J. and Hopkins, M., ed-

itors, A Librarian’s Guide to Graphs, Data and the

Semantic Web, Chandos Information Professional Se-

ries, pages 175–185. Chandos Publishing.

Powell, J. and Hopkins, M. (2015b). Graph analytics tech-

niques. In Powell, J. and Hopkins, M., editors, A

Librarian’s Guide to Graphs, Data and the Semantic

Web, Chandos Information Professional Series, pages

167–174.

S.Lee, J.Park, and H.Choi (2023). Cross-platform social

media networks: User categories and engagement pat-

terns. Computers in Human Behavior, 118:106742.

Smith, J. (2021). How to use reddit - beginner’s tutorial &

guide.

Smith, J., Faust, L., and Chawla, N. V. (2019). Social net-

work structure is predictive of health and wellness.

PLOS ONE, 14(6):e0217264.

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

268

Stoltenberg, D., Maier, D., and Waldherr, A. (2019). Com-

munity detection in civil society online networks:

Theoretical guide and empirical assessment. Social

Networks, 59:120–133.

Telatnik, M. (2021). Social network analysis: How to get

started.

Thompson, W. H., Brantefors, P., and Fransson, P. (2017).

From static to temporal network theory: Applica-

tions to functional brain connectivity. Netw Neurosci,

1(2):69–99.

Vasques Filho, D. and O’Neale, D. (2018). Degree distribu-

tions of bipartite networks and their projections. Phys-

ical Review E, 98.

Widman, J. (2022). What is reddit? a quick look at the

popular online community.

Williams, R. and Housley, W. (2021). Brexit, face-

book, and the polarization of online communities: A

mixed-methods analysis of linking practices on face-

book pages around brexit. New Media & Society,

23(4):1590–1612.

Xu, X., Zhang, J., and Chawla, N. V. (2019). A survey on

network embedding. IEEE Transactions on Knowl-

edge and Data Engineering, 31(5):833–852.

Zaidi, F. (2012). Small world networks and clustered small

world networks with random connectivity. Social Net-

work Analysis and Mining, 3.

Zhu, J., Chen, B., and Zeng, Y. (2020). Community detec-

tion based on modularity and k-plexes. Information

Sciences, 513:127–142.

Z.Xu and Q.Ke (2022). Topic-based network analysis of

reddit comments. Information Processing & Manage-

ment, 59(3):102638.

Network Analysis of the Egyptian Reddit Community

269