This document presents a critical review report on a course about algorithm design and analysis. It summarizes a paper that studied graph-based forecasting techniques for social networks. The paper generated a social graph from user activity data and used rule-based mining to predict future social behaviors. It found that this method was more efficient than the Apriori algorithm as it did not require multiple scans of the dataset. The real-world simulation achieved accuracy rates of over 80% for predictions made over 10 days. However, the review critiques that the paper's title was too broad and its impact was limited by only being cited 3 times in the last 7 years. Overall, the paper presented a good recommender system for social networks.

1. Prepared by Ashenafi Workie (pgr/18068/11)
Adama Science and Technology University
Department of Computer Science and Engineering
Critical Review Report on a course Design of Algorithm and Analysis
Submitted to Dr. Tilahun Melak (PHD)

2. Presentation Outline
2

3. Paper published in 2012 by
3

4. Abstract
This paper studied mining database of different algorithms.
like F-tree and Aprori
In this paper the authors tried to investigate forecasting
techniques based on directed weighted social graph.
In the methodology author’s research proposed is to
generate a social graph user’s action and predict the future.
Sanam et al. created some mathematical rules applied on
the graph in terms of community memberships
The strength of a relationship between two users without
knowing the content of the discussion.
4

5. Introduction
The article entitled with “Graph based forecasting for
social network site (Sanam Kadge and Gresha Bhatia)”
had been published at IEEE in 2012 Oct. 19-20, Mumbai,
India.
The authors study how to connect the people of similar
interest and builds some model.
They stated the an algorithms require more time for
scanning the datasets multiple times
multiple database scans for performing data mining on
the databases
5

6. Introduction cont...d
The basic idea of this research lies on forecasting based
on the generated social graph.
This novel approach for mining consists of visualization
of datasets into dynamic, directed, weighted graphs .
The graph depicts the user’s status in real time using
links and the weights on the links.
Lastly the authors introduce the limitation and overview
topic that had been included under the article contents.
6

7. Statements of the problem
As a result data is increasing day by day at a rate
which is difficult to control and process.
Even though researches have been carried out in this
area on huge datasets using association rule mining.
 These algorithms require more time for scanning the
datasets .
multiple times for example the algorithms require
multiple database scan for performing data mining.
7

8. Research Objective
Studied the approach of dynamic, directed, weighted
graphs and implemented Facebook like social network.
Specifically
Generate a social graph of user’s actions.
 predict the future social activities using graph mining
The strength of a relationship between two users
without knowing the content of the discussion.
8

9. Methodology(Data finding technique)
Authors stated that data can be collected from two
sources i.e.
1) The data entered by the user at the time of profile
creation and
2) based on users activities as follows like Login of
sender and receiver, searching a user, events like
posting a comment or liking a post, chatting
The methodology proposed is to generate a social graph
of users’ actions and predict the future social activities.
9

10. Methodology(Approaches)
1) Capture the data of user’s activities to generate graph
2) Forecasting using rule based mining.
10
Strength of Relationship
Rule based mining

11. Implementation( social network screenshot)
11

12. Implementation framework analysis
12

13. Result and discussion
Forecasting based on the result
The method is more efficient compared to Apriori
algorithm as there is no need of scanning the dataset
 Finding the accuracy of the predictions
 The real world simulation was done to find the accuracy
of the prediction made by the system.
 The predictions were noted on each day and after
observation for 10 days it was found that maximum
number of predictions turned true.
13

14. Cont.…d
14
Accuracy of predictions day wise
Accuracy of predictions percentage wise

15. Critique(strength of the paper)
1. The Authors(s) have appropriate professional
qualification and experience the main author studied
information technology whereas and the coauthor was
assistant professor(at department of computer
engineering ).
2. The abstract include all basic concepts and summary.
3. The abstract outlined methodology, reports of finding
limitation of the study.
4. The introduction stated overview the graph based
forecasting and the problems in paragraph1
15

16. Critique(strength of the paper)
1. The methodology section clearly state two approaches
their was captured data of user’s activities to generate
graph and forecasting using rule based mining.
2. the result section used graphics materials enhance clarity
of result through pie and other.
3. Generally, the data collection mechanism is the users
activity in the social networks
4. The conclusion state some how the basic results and
judgments to strengthen relationship.
16

17. Critique(Weakness of the paper)
1. The title of the research was too much
2. It is difficult to judge whether the authors of the
research had appropriate academic qualification
3. No recommendation were mentioned in the article.
4. The impact factors of the authors was not a good
because only 3 paper cited this paper for the last 7
years.
5. This paper major biased to research project or project
but it is not pure scientific research.
17

18. Conclusion
Generally, this paper good recommender system for
social networking friendship and community.
The result and methodology based on such forecasting
they could found the most popular community among
others
which would got maximum membership in the future
and in which the users are more active.
18

19. Any Question
19
Thank you