EE 6605
Learning Project Assignment

Objective:
Work through one learning project
1) Search literature
2) Conceive a new topic
3) Establish a new model or propose a new method (describe the algorithm
or procedure, perhaps derive an analytic formula, e.g., distribution)
4) Carry out some simulations and plot simulation figures / tables
5) Analyze the observations from simulation results with comparisons
6) Conclude the investigation
Requirement:
 Must be related to complex networks
 Apply some learned network knowledge
 Not anything from degree work (thesis/publications)
Object Report:
Write one learning project report
1) Title
2) Abstract
3) Introduction (background information)
4) Problem formulation (motivation)
5) Model or Method description
6) Simulation (set-up, parameters, results, plots, tables) using any
program software or language (MatLab, C++, Python,…)
7) Analysis and/or comparisons
8) Conclusions
9) References
10) Appendix (optional, e.g., program codes or method proof)
Requirement:
10±2 one-sided pages, typed (in Word or latex)  pdf
Example:
Facebook network modeling
 The Facebook Network
o The largest online social network in the world (2.9 billion active users in
2022)
o Two data sets in 2014, with fully connected components of 63731 and
72261577 users respectively
o 99.91% of users are located in a single large connected component
 Review of Existing Models:
o Scale-Free (BA) model
o Distance Social Network (DSN) model
o Asymmetric Weights Dynamic (AWD) model
o Exponential Random Graph models
o Markov Random (MR) model
o Random Triangle (RT) model
Background
Henneberg Model
Original Model
L. Henneberg (1911)
Modified Henneberg Model
Node Addition Edge Rewiring
Modified Model
with Edge Rewiring
Idea: Something new (or different)
Simulations + Plots
Small size
Large size > 500
Reasonable size:
50 < N < 500
o BA (Scale-Free) model
o DSN: Distance Social Network model
o AWD: Asymmetric Weights Dynamic model
o Exponential Random Graph models
o MR: Markov Random model
o RT: Random Triangle model
Simulation + Plots
Power-Law (before taking log-log)
Simulation + Plots
Small size
Larger size
(After taking log-log)
AD: Average Distance
APL: Average Path Length
ACC: Average Clustering Coefficient
Simulation Results and Comparison
Real Data ?
Not required
But if so, a plus
A real Facebook dataset: 72,261,577 users
(too big for this project to do)
Observations and Analysis
Observations:
1. Power-law degree distribution
2. Small-world features
3. Community structure
4. Hierarchical structure
Analysis:
1. Why power-law? Because …
2. Why small-world? Because …
3. Why community structure? Because …
4. Why hierarchical structure? Because …
Conclusions and Discussions
Conclusions:
1. …
2. …
3. …
Discussions:
1. Limitation …
2. Future work …
References and Appendix
References
[1] G. Chen, Lecture Nodes, EE6605, CityU, 2022
[2] L. Henneberg, Die graphische Statik der starren Systeme, Leipzig, 1911
[3] ...
Appendix (Optional)
Program Codes
Final Report:
1) Title [and Name, ID#, 1/2 p]
2) Abstract [1/2 p]
3) Introduction [1~2 pp]
4) Problem formulation [1 p]
5) Model description [1~2 p]
6) Simulation [3~5 pp, including figures/tables]
7) Analysis [2~3 pp]
8) Conclusions [1/2 p]
9) References [1/2 p]
10) Appendix [< 2 pp]
10±2 one-sided pages, typed (in Word or latex)
1.5-line spacing, 12-point font size
 pdf file to upload to Canvas
You may do something else
in similar quality and quantity
Project Report is due to Canvas
Anytime before midnight Wednesday 30 November 2022
Late Reports will not be accepted
Be serious and Enjoy