05 20275 computational solution...

International Journal of Informatics and Communication Technology (IJ-ICT)
Vol.9, No.3, December 2020, pp. 185~194
ISSN: 2252-8776, DOI: 10.11591/ijict.v9i3.pp185-194  185
Journal homepage: http://ijict.iaescore.com
Computational solution of networks versus cluster grouping for
social network contact recommender system
Arnold Adimabua Ojugo, Debby Oghenevwede Otakore
Department of Mathematics / Computer Science, Federal University of Petroleum Resources Effurun, Nigeria
Article Info ABSTRACT
Article history:
Received Jan 26, 2020
Revised Feb 20, 2020
Accepted Mar 13, 2020
Graphs have become the dominant life-form of many tasks as they advance a
structure to represent many tasks and the corresponding relations. A powerful
role of networks/graphs is to bridge local feats that exist in vertices as they
blossom into patterns that help explain how nodal relations and their edges
impacts a complex effect that ripple via a graph. User cluster are formed as a
result of interactions between entities. Many users can hardly categorize their
contact into groups today such as “family”, “friends”, “colleagues” etc. Thus,
the need to analyze such user social graph via implicit clusters, enables the
dynamism in contact management. Study seeks to implement this dynamism
via a comparative study of deep neural network and friend suggest algorithm.
We analyze a user’s implicit social graph and seek to automatically create
custom contact groups using metrics that classify such contacts based on a
user’s affinity to contacts. Experimental results demonstrate the importance
of both the implicit group relationships and the interaction-based affinity in
suggesting friends.
Keywords:
Cluster
Ego-centric networks
Implicit contact
Recommender
Social graphs
Tie-strenght
This is an open access article under the CC BY-SA license.
Corresponding Author:
Arnold Adimabua Ojugo,
Department of Mathematics / Computer Science,
Federal University of Petroleum Resources Effurun,
Delta State, Nigeria.
Email: ojugo.arnold@fupre.edu.ng
1. INTRODUCTION
Group communication has brought about more effective interaction that is usually validated
amongst a cluster of people. It seeks to broadcast data over a communication channel and/or medium to a
cluster of people – restricting peer-to-peer communications. Email, text and social platforms allow the
support for group conversations and consequently, sharing of data in formats like photo, links and document
[1]. Despite the prevalence of such group communication, users spend less time creating and maintaining
custom contact group. Social platforms provide users with exclusive relationship and links with their
corresponding contacts. Thus, it is common practice that some users can identify a person as a friend – even
when they do not know him/her. Treating all contacts in same manner has been the basis for fraud and other
social engineering vices. There is the need for users to differentiate and classify into groups, their personal
contacts as this is quite a safe and unrestrictive practice. Allowing users to curb and minimize the fears
associated with contacts data sharing and interaction on a social networks. Many users have had to quit
groups when close relations (family, friends and colleagues) are added or removed from the platform [2].
Many cluster relationships are easily modeled as social graph that are implemented via social
networks. A graph is a symbolic representation of a network and of its connectivity as a structure of linked
nodes and their relationship. Mathematically, a graph is a set of vertex (node) V, connected by edges E and
denoted as G = (V, E). A social graph interconnects users showing their relationships on a social network

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Int J Inf & Commun Technol, Vol. 9, No. 3, December 2020: 185 – 194
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especially in relation to a user’s egocentric personal social graph/network [3]. It denotes a discrete graph
containing vertices, linked together by edges that describes nodal relationship between these entities [4, 5].
Thus, it describes a socially weighted network that analyses relationship or ties between users, entities or
objects by means of their interaction as they share data.
Literature Review: Types of Social Graphs: There are two kinds of social graph namely in the light
of social discuss namely: explicit and implicit graph relationship. The implicit social graph describes an
interaction between users, their contacts and group of contacts. It defines a graph whose vertices are not
represented as explicit data objects in memory; But rather, are determined algorithmically from some more
concise inputs. It is also a graph whose edges are weighed by feats such as frequency, recency, and direction
of interaction between users and their contacts and their group of contacts. They are used to identify clusters
of contacts who form groups that are meaningful and useful to each user [1, 6].
Conversely, explicit graphs are such that two-individuals deliberately and mutually describe their
connection with one another. Thus, such graphs can be mined more easily, since they begin with hard data,
and not algorithms that will be hard for competitors to replicate in the future. It is best understood as truly
personal and social [7]. Thus, explicit graphs are rare and examples include Facebook and LinkedIn. Thus,
groups change dynamically as new users are added to multi-party communication threads; while, others are
also removed. Thus, a person’s individual relationship dynamically evolve and changes over time as a friend
becomes one’s family, a colleague becomes a friend, a friend becomes a colleague etc. The need to
consistently update all relationships users have with their contacts, require constant maintenance, which is
tedious and time consuming feats [2].
Navigating Many Clustering Models: A graph attempts to bridge local features, as they blossoms
into global patterns, to explain how nodal relationships impacts a complex effect that ripple through a
population system. Each node shapes a graph’s evolution as need arises. Social graphs seeks two goals: (a)
better understand how networks evolve, and (b) study the dependent social processes like innovation
diffusion and data retrieval via models to specify how local interaction of nodal feats are explored to a global
pattern [8]. Graph binds together nodes via a predefined model so that we can effectively analyze its entities
along theories that sought to explain the inherent observed patterns [9-10]. Thus, they propagate local feats
present in the nodes that eventually emerge as global patterns. It examine dynamics in relationship between
nodes as well as helps to locate all the influential entities within such a network – as it theoretically, allows
connection convergence of nodes [5].
2. MATERIALS AND METHODS
2.1. Statement of problem
The following problems are to be addressed:
a. Manual creation of groups from user contacts is quite time consuming as the user must deliberately
identify clusters from his/her contact list so as to create the required groups.
b. The dynamic nature of social groups especially with the addition, deletion and amending of
relationships etc, users often manually handle such updates of custom groups.
c. Study seeks to tackle the above problem using an implicit social graph through a friend suggest
algorithm.
The study seeks to implement a predicted suggestive algorithm using the implicit social graph. This
will help automatically help users to create custom contact groups in their phonebook. It will further,
intelligently help eliminate time penalty and cost associated and spent to manually create custom contact
graph. The study specific goals include: (a) to describe an interaction-based metric for estimating user’s
affinity to his contact group, (b) implement the deep neural network and friend suggestion algorithm via a
user’s implicit social graph, (c) compare results of generated groups using predefined labels (seed-set dataset)
of contacts as already categorized as friends by the user, (d) to suggest contacts that can be used to expand
seed-set of group, (e) to demonstrate the importance of implicit group relationship and the interaction-based
affinity in suggesting contacts to add/remove from groups, and (f) to compare both models and their
effectiveness in these classifications.
2.2. Data gathering
Study employs the Enron Email Corpus Dataset that consist of a large collection of employee’s
email messages of the Enron Corporation collected during the legal investigation of Enron accounting fraud
in December 2001. It contains over 600,000 messages from 150-users; But, for the study, we consider just a
single user (i.e. an employee) email – because we seek a user’ egocentric network. Each employee folder in
the dataset, has other folders of emails such as incoming message and outgoing message folder. The
employee email address to be considered is Shackleton Sara (chosen based on a balance in the number of

Int J Inf & Commun Technol ISSN: 2252-8776 
Computational solution of networks versus cluster grouping for social network … (Arnold Adimabua Ojugo)
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incoming and outgoing messages through a thorough check on all employees’ email folders). Folders
considered in Shackleton Sara folders are inbox folder, notes_inbox folder, sent folder and sent item folder.
The rationale for the adoption of the dataset used, is based on (a) standard email for social networks,
(b) the dataset explores benefits and tie-strength of users, and (c) dataset incorporates the various components
metrics to be measured for the graph algorithm. Dataset is obtained from [web]: http://www.cs.cmu.edu.
3. EXPERIMENTAL FRAMEWORK
Machine learning seeks to develop models, algorithms and systems that mimics intelligence as it
allows such systems to evolve its behavior based on empirical data from sensors data and databases.
The system explores data mining methodology, tools and techniques to capture characteristics of interest as
we seek its underlying unknown probability distribution. Thus, illustrating the relations between observed
and historic data. Thus, system seeks to automatically learn to recognize complex patterns and make
intelligent decisions from it [11-14].
3.1. Friend suggest algorithm (FSA)
Following the work of Roth et al (2010), the researcher further explores the friend suggest algorithm
which probes the presence of implicit clustering in a user’s egocentric network by observing groups of
contacts who are frequently present as co – recipients in the same email threads. FSA functions within the
egocentric network in order to show suggestions based only on a user’s local data so as to protect user
privacy and avoid exposing connections between the user’s contacts that may not otherwise have been
identified to him. The inputs to friends suggest is a seed, which is a small set of one or more contacts that
belong to a group. This seed may be characterized by the user picking a few contacts. e.g. As an initial list in
the “To” field of an email. Given this seed, FSA finds the contacts in the user’s egocentric network that are
related to the seed (i.e. they are present in the same implicit clusters). FSA further returns a score for each
suggested contact, indicating the goodness of its fit to the existing seed-set. The FSA is applicable to
problems of group clustering any interaction based social graph [1, 6]. Figure 1 describes the various
components that makes up the architecture of the friend suggest algorithm (FSA).
Figure 1. General architecture of the friend suggest algorithm
FSA consist of these components explained below as in [6]:
a. The Interaction Rank – Here, the implicit social graph has that its weighed edges represent tie-strength
relationship between a user and his implicit group. This is computed via the following criteria: (i)
Frequency – These are groups with which a user interacts occasionally are more important to the user
than groups with which he interacts infrequently, (ii) Recency is ability for group to change and be
dynamic over time, (iii) Direction: Interactions a user initiates is of more significance, and (iv) Contact
importance: Groups that includes important contact are also more important to a user than other groups.
Thus, we adjust the edge weights based on a contact-importance metric, a group that includes one or more
important contacts are assigned a greater edge weight that group that do not include such important
contact. The contact-importance metric for a respective contact of a user may be determined by other data
about user’s relationship or global information about the contact’s position within the socio-centric graph.
Furthermore, receiving an email from a contact (passive interaction) is a weaker signal of closeness than
an active interaction of sending an email to that contact.

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b. The Core Routine Function expands (adds/removes contact) via the seedset in the ego-centric network.
c. The Scoring Function – implements the various versions of the Update_Score algorithm, and consists of:
(i) intersecting group score, (ii) intersecting weighted score, (iii) intersect group count, (iv) top_contact
score, and (v) suggesting contact to remove algorithms.
Finally, extending [1], study seeks to explore the FSA taking advantage of its strength on a custom dataset.
3.2. Spectral deep learning network
Deep neural network has successfully been implemented in systems that seek to learn useful features
and construct multi-layer networks from a vast amount of training data. Forecast accuracy is improved using
DNNs, allowing more data about a raw dataset to be obtained. DNN deep architectures including multiple
hidden layers—and each hidden layer alone conducts a non-linear transformation from the previous layer to
the next [15, 16]. With deep learning proposed by [17], DNN is trained according to two sections: (a) pre-
trained, and (b) fine-tuned procedures [18, 19].
Auto-Encoder: [20] Auto-Encoder is a type of unsupervised three-layered neural network whose
output target is an input data shown in Figure 2. It includes both encoder and decoder networks. The encoder
network transforms input data from a high-dimensional space into a low-dimensional space; While, the
decoder network remodels the input from the previous step. The encoder network is defined as encoding
function fencoder – with encoding process as in (1) – where xm
is a data point and hm
is the encoding vector
obtained from xm
:
hm
= fencoder(Xm) (1)
Figure 2. Auto-encoder and decoder in deep neural network
The Decoder is a reconstruction function denoted as fdecoder as in (2), where xm
is the decoding vector
obtained from hm
. Other algorithms for encoding (reconstruction function) includes (3-5) respectively.
Xm
= fdecoder(hm) (2)
logsig: fencoder(Xm) =
1
1+ e−Xm (3)
satline: fencoder(Xm) = {
0
z
1
|
if Xm
≤ 0
if Xm
< 1
if Xm
≥ 1
} (4)
pureline: fencoder(Xm) = Xm
(5)
Pre-Training: N auto-encoders can be stacked to pre-train N-hidden-layer DNN. When given an
input dataset, the input layer and the first hidden layer of the DNN are treated as the encoder network of the
first auto-encoder. Next, the first auto-encoder is trained by minimizing its reconstruction error. The trained
parameter set of the encoder network is used to initialize the first hidden layer of the DNN. The first and
second hidden layers of the DNN are regarded as the encoder network of the second auto-encoder.
Accordingly, the second hidden layer of the DNN is initialized by the second trained auto-encoder. This
continues until the Nth auto-encoder is trained to initialize the final hidden layer of the DNN [20]. Thus, all
hidden layers of the DNN are stacked in an auto-encoder in each training N times, and are regarded as pre-
trained. This pre-training process is proven to be significantly better than random initialization of DNN and
quite useful in achieving generalization in many of the classification tasks [21, 17].

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Fine-Tuning is a supervised process that improves performance of a DNN. The network is retrained,
training data are labelled, and errors calculated by difference between real and predicted values are back-
propagated using stochastic gradient descent (SGD) for all multi-layer networks. SGD randomly selects data
samples, and iteratively updates gradient direction with weight parameters. Best gradient direction is
obtained with a minimum loss function. The merit of SGD is that it converges faster and also does not
consider the entire dataset. Making it far suitable for complex neural networks [20] as in (6) below:
E =
1
2
∑ M(yi − ti)2
(6)
E is loss function, y is real label and t is net output. Gradient of weight w is obtained as derivative of error
equation – so that with the gradient of the weight wij, the updated SGD equation is defined by (7) [20]:
Wij
new
= Wij
old
− h(yj − tj) ∗ yj(1 − yi)ω (7)
h is the step-size and it is greater than 0; while, ω is number of hidden layers in DNN [20]. The process is
optimized and tuned using the weights and threshold of the correctly labelled data. This enables the DNN to
learn important knowledge for its final output and direct the parameters of entire network to perform correct
classifications [14].
3.3. K-nearest neighbourhood (KNN)
Is a well-known supervised learning model for pattern recognition, introduced by Fix and Hodges in
1951. It remains one of the most popular nonparametric models for classification problems [22]. KNN
assumes that observations, that are close together, are likely to have the same classification. The probability
that a point x belongs to a class is estimated by proportion of training points in a specified neighbourhood of
x that belong to that class. This point(s) is then either classified by majority vote or by a similarity degree
sum of the specified number (k) of nearest points. In majority voting, number of points in neighbourhood
belonging to each class is counted, and the class to which the highest proportion belongs to is most likely
classification of x [13]. Similarity degree sum calculates a similarity score for each class based on the K‐
nearest points and classifies x into the class with the highest similarity score. Its lower sensitivity to outliers
allows majority voting to be used other than similarity degree sum [23]. We use majority voting for data to
determine which points belongs to neighbourhood so that distances from x to all points in the training set
must be calculated. Any distance function that specifies which of two points is closer to the sample point
could be used. The most common distance metric used in K-nearest neighbour is Euclidean distance [24]
given by Eq. 8 as distance between each test point ft and training set point fs, each with n attributes as thus:
d = [(ft1 − fs1)2
+ (ft2 − fs2)2
… + (ftn − fsn)2
]
1
2 (8)
In general, KNN performs the following steps: (a) chosen of k value, (b) distance calculation, (c)
distance sort in ascending order, (d) finding k class values, and (e) finding the dominant class [25]. A
challenge with KNN is to determine the optimal size of k that acts as smoothing parameter. A small k is not
sufficient to accurately estimate population proportions around the test point. A larger k will result in less
variance in probability estimates (but for risk of introducing more bias). K should be large enough to
minimize probability of a non‐Bayes decision, and small enough that all points included, gives an accurate
estimate of the true class. [26] in [13] found optimal value of k to depend on sample size and covariance
structures in each population, and on proportions for each population in the total sample. In cases where the
differences in covariance matrices and the difference between sample proportions are either both small (or
both large), then optimal k is N3/8
(N is number of samples in training set). If there is a large difference
between covariance matrices, and a small difference between sample proportions (or vice-versa), optimal k is
determined by N2/8
.
This model presents some merits [27]: (a) mathematical simplicity allows for achieve classification
results as good or even better than other more complex pattern recognition techniques, (b) it is free of
statistical assumptions, (c) its effectiveness does not depend on the space distribution of classes, and (d) when
the boundaries between classes are not hyper‐linear or hyper‐conic, K-nearest neighbour performs better
than LDA [13].
Its major demerits is that it does not work well for large differences in samples in each class. KNN
yields poor data about the structure of its classes, and relative importance of variables in classification. Also, it does
not allow graphical representation of the results, and in case of large number of samples, computation become
excessively slow. In addition, KNN requires more processing and memory needs than other methods. All

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prototypes in training set must be stored in memory and used to calculate Euclidean distance from every test
sample. The computational complexity grows exponentially as the number of prototypes increases [28, 13].
3.4. Linear discriminant analysis
LDA is an effective supervised classification method with wide range of applications. Its theory is to
classify compounds (rules) dividing n‐dimensional descriptor space into two regions separated by a hyper‐
plane that is defined by linear discriminant function. Discriminant analysis transforms classification tasks
into functions with data partitioned into classes. It reduces the problem to an identification of a function [13].
The focus of discriminant analysis is to determine this functional form (assumed to be linear) and estimate its
coefficients. Introduced in 1936 by Ronald Fisher, LDA seeks the mean of a set of attributes for each class,
and using the mean of these means as boundary. It thus projects attribute points onto the vector that
maximally separates their class means, and minimizes their within-class variance as in (9) [29]:
LDA = X′(X2 − X1) −
1
2
(X2 + X1)S−1(X2 − X1) > c (9)
X is vector of the observed values, Xi (i = 1, 2…) is the mean of values for each group, S is sample
covariance matrix of all variables, and c is cost function. If the misclassification cost of each group is
considered equal, then c = 0. A member is classified into one group if the result of the equation is greater
than c (or = 0), and into the other if it less than c (or = 0). A result that equals c (set to 0) indicates such a
sample cannot be classified into either class, based on the features used by the analysis. LDA function
distinguishes between two classes – if a data set has more than two classes, the process must be broken down
into multiple two‐class problems. The LDA function is found for each class versus all samples that were not
of that class (one‐versus‐all). Final class membership for each sample is determined by LDA function that
produced the highest value and is optimal when variables are normally distributed with equal covariance
matrices. In this case, the LDA function is in same direction as Bayes optimal classifier [30], and it performs
well on moderate dataset in comparison to other more complex method [13]. Its mathematical function is
simple and requires nothing more complicated than matrix arithmetic. It assumes linearity in the class
boundary, however, limits the scope of application for linear discriminant analysis. When boundaries are
nonlinear, the performance of the linear discriminant may be inferior to other classification methods.
Thus, to curb this – we adopt a decimal encoding of the data to give us a semblance of linear, continuous
boundaries [31].
3.5. Rationale for choice of algorithms
Stochastic models are inspired by biological populations and laws of evolution. They search for
optimal solution via hill-climbing method that is flexible, adaptive to changing states and suited for real-time
tasks. They guarantee high convergence in multimodal task(s) via an initialized random population and
allocates increasing trials to regions found with high fitness for optimality in time. Its demerit is in their
inefficiency with linear systems in that if the optimal is in small region surrounded by regions of low fitness
– function becomes difficult to optimize. But, the adoption of such stochastic graph-based models entails that
for iterated hill-climbing – once a peak is located, it restarts with another, randomly chosen, starting point.
Its merit is simplicity and with each random trial performed in isolation, no overall picture of the domain is
obtained. As the evolution progresses, it continues to allocate its trials evenly over a search space.
This means that the algorithm continues to evaluate as many points in the regions found to be of low fitness
as in regions found to be of high fitness [11-12].
4. RESULT, DISCUSSION AND FINDINGS
4.1. Model performance
Performance is evaluated using the mean square error (MSE), mean regression error (MRE), mean
absolute error (MAE) and coefficient of efficiency (COE) as in Table 1 [13]. It shows that the adopted
models for DNN has a mean square error of .73, mean regression error of 0.79, mean absolute error of 0.75
and coefficient of efficiency of 0.581 respectively. Also, we have that the FSA has a mean square error of
.41, mean regression error of 0.51, mean absolute error of 0.45 and coefficient of efficiency of 0.781
respectively. Then LDA has a mean square error of .18, mean regression error of 0.21, mean absolute error of
0.43 and coefficient of efficiency of 0.492 respectively. Lastly, KNN has a mean square error of .36, mean
regression error of 0.21, mean absolute error of 0.23 and coefficient of efficiency of 0.853 respectively. It is
worthy of note that the more the values of MSE, MRE and MAE tends to 1, the more accurate the adopted
model’s prediction; While, the value of COE conversely should tend more to zero (0).

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Table 1. Model convergence performance evaluation
Model MSE MRE MAE COE
DNN .73 .79 .75 .581
FSA .41 .51 .45 .781
LDA .18 .21 .43 .492
KNN .36 .31 .23 .853
To ensure model’s efficiency and accuracy, we compute misclassification rate and improvement
percentages for both training and testing dataset, summarized in Table 2 and Table 3 respectively via (10, 11)
𝑀𝑅 (𝐴) =
𝑁𝑜 𝑜𝑓 𝐼𝑛𝑐𝑜𝑟𝑟𝑒𝑐𝑡 𝐶𝑙𝑎𝑠𝑠𝑖𝑓𝑦
𝑁𝑜 𝑜𝑓 𝑆𝑎𝑚𝑝𝑙𝑒 𝑆𝑒𝑡
(10)
Table 2 shows model’s error for misclassification of the emails. KNN, LDA and FSA yields error of
30.3%, 39.7% and 18.3%; while, DNN yields 12.8% respectively.
𝐼𝑚𝑝𝑟𝑜𝑣𝑒𝑚𝑒𝑛𝑡 =
𝑀𝑅(𝐴)−𝑀𝑅(𝐵)
𝑀𝑅(𝐴)
∗ 100 (11)
Table 3 shows LDA, KNN and FSA yields an improvement of 41.1%, 43% and 69.9% respectively;
while, DNN yields 76%. It was also observed that though KNN is quite sensitive to the relative magnitude of
different attributes, all attributes are thus scaled by their z‐scores before using KNN [32].
Table 2. Misclassification rate of each model
Model
Classification Errors
Training Data Testing Data
KNN 31.2% 30.3%
LDA 43.4% 39.7%
DNN 13.5% 12.8%
FSA 19.3% 18.3%
Table 3. Improvement percentage rate of each model
Model
Improvement Rate %
Training Data Testing Data
LDA 45.83% 41.16%
KNN 41.79% 43.09%
DNN 78.78% 76.33%
FSA 69.30% 69.91%
4.2. Classification accuracy
The Figure 3 shows the prediction accuracy of the various models with FSA showing an accuracy of
92-percent, DNN showing an accuracy of 89-percent, KNN showing an accuracy of 74-percent and LDA
showing an accuracy of 70-percent respectively. It is also worthy of note that in order forms of classification,
DNN (deep neural networks) have been found to outperform these models.

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Figure 3. Prediction Accuracy of Algorithms in percentage
4.3. Processing speed
The Figure 4 shows the mean prediction processing time for the various models with FSA showing an
men processing time of 1.22, DNN has mean processing time of 0.98, KNN shows a mean processing time of
2.98 and LDA shows a mean processing time of 3.36 respectively. It becomes clearer thus, that DNN reach
an accuracy of 89-percent at 0.98 seconds, FSA reached an accuracy of 92-percent at 1.22 seconds.
KNN reached an accuracy of 74-percent at 2.98seconds; while, LDA reached its 70-percent at 3.36 seconds
respectively.
Figure 4. Processing time in seconds
4.4. Convergence time
The rationale for model choice(s) is to compare and measure their convergence behavior amongst
other statistics as seen in Figure 3 – 5 respectively. Also, LDA and KNN converged after 405 and 387-
iterations respectively; While, FSA and DNN converged after 253- and 193-iterations respectively. DNN
outperforms FSA for the task being considered. We note, model’s speed is traded-off for greater accuracy of
classification, a greater number of rule set generated to update the knowledge database for optimality and
greater functionality.
Figure 5. Convergence time of matches
5. CONCLUSION
There has to be effective communication between two entities for an effective relationship. For this
to happen, both persons have to be in constant communication. A means through which this happens, is via
social platforms such as link editing, photo sharing, and email communication and so on. All models used
shows that two or more users can connect using an implicit social graph. They all generate a friend’s group,
given a small seed-set of contacts already categorized by a user, as friends, colleagues groups. They equally
suggest contacts to expand the seed-set as contained in the user’s egocentric network. For the study, we used
the Enron Corpus Email dataset to determine the interaction rank (edge weight) between a user and his group

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of contacts. We detailed the comparative statistics of the various models based on the efficiency, speed,
convergence and others.
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 ISSN: 2252-8776
194
BIOGRAPHIES OF AUTHORS
Arnold Adimabua Ojugo received BSc in Computer Science from the Imo State University
Owerri in 2000, MSc in Computer Science from the Nnamdi Azikiwe University Awka in 2005,
and PhD in Computer Science from Ebonyi State University Abakiliki in 2013. He currently
lectures with the Department of Mathematics/Computer Science of the Federal University of
Petroleum Resources Effurun, Delta State, Nigeria. His research interests: Intelligent Systems
and Control, High Performance and Dynamic Computing, Machine-Learning, Computer Vision,
Ubiquitous Computing, Data Security and Forensics. He is an Editor with the Progress for
Intelligent Computation and Application, Advancement for Scientific and Engineering Research
and SciencePG (ACIS, AJNC, NET and WCMC Journals). He is a member of: The Nigerian
Computer Society, Computer Professionals of Nigeria and International Association of
Engineers (IAENG).
Debby Oghenevwede Otakore received BSc in Computer Science from Benson Idahosa
University, Edo state in 2008, MSc in Computer Science from the University of Port-Harcourt
2015, She is currently pursing a Ph.D in computer science at the University of Port-Harcourt,
Nigeria. She currently lectures with the Department of Mathematics/Computer Science of the
Federal University of Petroleum Resources Effurun, Delta State, Nigeria. Her research interests:
Recommender and Intelligent Systems. She is a member of: The Nigerian Computer Society.

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