Network Science (ITEC-520) - Course Outline

BS IT 5th Semester

The University of Modern Sciences, Tando Muhammad Khan, Sindh

Instructor: Prof. Dr. Mudasar Hussain
Department: Information Technology
Credit Hours: 3 (2 Theory + 1 Practical)
Prerequisites: Data Structures, Discrete Mathematics
Duration: 16 Weeks (48 contact hours)

Course Description

Network Science is an interdisciplinary field that studies complex networks in nature, technology, and society. This course provides comprehensive coverage of network theory, algorithms, and applications. Students will learn to analyze, model, and visualize real-world networks including social networks, biological networks, information networks, and technological networks. The course combines theoretical foundations with hands-on practical experience using Python and network analysis tools.

Course Objectives

Upon successful completion of this course, students will be able to:

1. Understand fundamental concepts and principles of network science

2. Analyze network structures using mathematical and computational approaches

3. Apply network algorithms for centrality, community detection, and link prediction

4. Model and simulate network dynamics and spreading phenomena

5. Visualize and interpret complex networks using appropriate tools

6. Design and implement network analysis solutions for real-world problems

7. Understand applications of network science in social media, biology, technology, and infrastructure

Learning Outcomes

Knowledge and Understanding:

• Comprehend the mathematical foundations of graph theory and network science

• Understand different types of networks and their properties

• Recognize network patterns and structures in real-world systems

Skills:

• Apply network analysis algorithms and techniques

• Use Python libraries (NetworkX, igraph) for network analysis

• Visualize networks effectively using appropriate tools

• Conduct empirical analysis of real-world networks

Competencies:

• Design network-based solutions for practical problems

• Evaluate network robustness and vulnerability

• Predict network evolution and dynamics

Course Content Outline

Week 1-2: Introduction to Network Science 

Lecture 1: Course Introduction & Fundamentals (Slides)

• What is Network Science?

• Historical perspective and applications

• Networks in nature, technology, and society

• Basic terminology: nodes, edges, directed/undirected networks

Lecture 2: Graph Theory Basics

• Mathematical foundations

• Graph representation (adjacency matrix, adjacency list)

• Types of graphs: simple, weighted, directed, bipartite

• Graph properties: degree, paths, walks, cycles

Practical 1-2:

• Python environment setup

• Introduction to NetworkX library

• Creating and visualizing basic networks

Week 3-4: Network Characteristics

Lecture 3: Degree Distribution and Network Density

• Degree distribution

• Average degree

• Network density and sparsity

• Degree sequences

Lecture 4: Random Networks

• Erdős-Rényi model

• Properties of random graphs

• Phase transitions

• Limitations of random networks

Practical 3-4:

• Calculating network statistics

• Generating random networks

• Visualizing degree distributions

• Analyzing real network datasets

Week 5-6: Small-World Networks

Lecture 5: Small-World Phenomenon

• Six degrees of separation

• Clustering coefficient

• Average path length

• Small-world property

Lecture 6: Watts-Strogatz Model

• Regular lattice to random rewiring

• Interpolating between order and randomness

• Applications in social and biological networks

• Limitations and extensions

Practical 5-6:

• Measuring clustering and path length

• Implementing Watts-Strogatz model

• Analyzing small-world properties in real networks

• Facebook network analysis

Week 7-8: Scale-Free Networks

Lecture 7: Power Laws and Scale-Free Networks

• Power law distributions

• Heavy-tailed distributions

• Hub nodes and robustness

• Real-world scale-free networks

Lecture 8: Preferential Attachment

• Barabási-Albert model

• Growth and preferential attachment mechanism

• Rich-get-richer phenomenon

• Mathematical analysis of degree distribution

Practical 7-8:

• Identifying power laws in data

• Generating scale-free networks

• Analyzing Internet and citation networks

• Comparative analysis of network models

Week 9-10: Network Centrality

Lecture 9: Centrality Measures I

• Degree centrality

• Closeness centrality

• Betweenness centrality

• Applications in identifying important nodes

Lecture 10: Centrality Measures II

• Eigenvector centrality

• PageRank algorithm

• Katz centrality

• Comparing centrality measures

Practical 9-10:

• Computing various centrality measures

• PageRank implementation

• Identifying influential nodes

• Social network influencer analysis

Week 11-12: Community Detection

Lecture 11: Community Structure

• Definition of communities

• Modularity

• Hierarchical clustering

• Clique-based methods

Lecture 12: Community Detection Algorithms

• Girvan-Newman algorithm

• Louvain method

• Label propagation

• Spectral clustering

Practical 11-12:

• Implementing community detection algorithms

• Modularity optimization

• Visualizing community structure

• Analyzing social media communities

Week 13-14: Network Dynamics

Lecture 13: Network Resilience and Robustness

• Attack tolerance

• Random failures vs targeted attacks

• Percolation theory

• Network robustness measures

Lecture 14: Spreading Phenomena

• Epidemic models (SI, SIS, SIR)

• Information diffusion

• Cascade models

• Viral marketing

Practical 13-14:

• Simulating network attacks

• Epidemic modeling on networks

• Information cascade simulation

• COVID-19 spread analysis on contact networks

Week 15-16: Advanced Topics & Applications

Lecture 15: Link Prediction

• Common neighbors

• Jaccard coefficient

• Preferential attachment score

• Machine learning approaches

Lecture 16: Network Applications & Future Directions

• Biological networks (protein interaction, neural networks)

• Transportation networks

• Financial networks

• Emerging trends in network science

Practical 15-16:

• Link prediction implementation

• Real-world network analysis projects

• Final project presentations

• Course review

Practical Component Details

Laboratory Work (1 Credit Hour)

Required Software:

• Python 3.8+

• NetworkX library

• Matplotlib, Seaborn for visualization

• Pandas for data handling

• Jupyter Notebook

• Gephi (optional for advanced visualization)

Lab Structure: Each lab session includes:

1. Introduction to concepts (15 mins)

2. Hands-on coding exercises (60 mins)

3. Discussion and problem-solving (15 mins)

Lab Datasets:

• Karate Club network

• Facebook social circles

• Collaboration networks

• Citation networks

• Biological networks (protein interaction)

• Transportation networks

Lab Assessment:

• Weekly lab exercises: 20%

• Mid-lab practical exam: 15%

• Final lab project: 15%

Assessment Strategy

Component

Weightage

Details

Quizzes

10%

4 quizzes (best 3 counted)

Assignments

15%

3 assignments

Lab Work

20%

Weekly lab exercises

Mid-term Exam

15%

Theory + Practical

Final Project

15%

Network analysis project

Final Exam

25%

Theory + Practical

Grading Scale:

• A: 85-100%

• B: 70-84%

• C: 60-69%

• D: 50-59%

• F: Below 50%

Textbooks and References

Primary Textbook:

1. "Network Science" by Albert-László Barabási (2016)

   • Free online: http://networksciencebook.com/

Additional References:

2. "Networks: An Introduction" by Mark Newman (2018)

3. "Social Network Analysis: Methods and Applications" by Stanley Wasserman & Katherine Faust (1994)

4. "Complex Networks: Principles, Methods and Applications" by Vito Latora, Vincenzo Nicosia, & Giovanni Russo (2017)

Online Resources:

• NetworkX Documentation: https://networkx.org/

• Stanford Network Analysis Platform (SNAP): http://snap.stanford.edu/

• Network Repository: http://networkrepository.com/

• Gephi Tutorials: https://gephi.org/

Course Policies

Attendance:

• Minimum 75% attendance required

• Medical certificates required for prolonged absence

Academic Integrity:

• Plagiarism will result in zero marks

• Collaboration on labs encouraged, but submissions must be individual

• Proper citation of sources required

Late Submission:

• 10% penalty per day (up to 3 days)

• No submissions accepted after 3 days without valid reason

Communication:

• Office hours: Monday & Wednesday, 2-4 PM

• Email response within 24-48 hours

• Course announcements via LMS

Weekly Schedule Template

Week

Theory Topics

Practical Topics

Assessments

1

Introduction to Networks

Python & NetworkX Setup

-

2

Graph Theory Basics

Basic Network Creation

Quiz 1

3

Degree Distribution

Network Statistics

-

4

Random Networks

Random Network Generation

Assignment 1

5

Small-World Phenomenon

Clustering Analysis

-

6

Watts-Strogatz Model

Small-World Networks

Quiz 2

7

Scale-Free Networks

Power Law Analysis

-

8

Preferential Attachment

Scale-Free Generation

Mid-term

9

Centrality Measures I

Computing Centrality

-

10

Centrality Measures II

PageRank Implementation

Assignment 2

11

Community Structure

Community Detection

Quiz 3

12

Detection Algorithms

Modularity Optimization

-

13

Network Robustness

Attack Simulation

-

14

Spreading Phenomena

Epidemic Modeling

Assignment 3

15

Link Prediction

Link Prediction Methods

Quiz 4

16

Applications & Future

Final Projects

Final Project Due

Final Project Guidelines

Objective: Apply network science concepts to analyze a real-world network

Requirements:

• Choose a real-world network dataset

• Conduct comprehensive network analysis

• Apply at least 3 different analytical techniques

• Visualize findings effectively

• Present results in report and presentation

Deliverables:

1. Project proposal (Week 10)

2. Progress report (Week 14)

3. Final report (15-20 pages)

4. Presentation (15 minutes)

5. Code submission

Suggested Topics:

• Social media network analysis

• Citation network analysis

• Transportation network optimization

• Disease spread modeling

• Recommendation system using networks

• Criminal network analysis

• Academic collaboration networks

Learning Resources

Python Libraries:

python

import networkx as nx

import matplotlib.pyplot as plt

import numpy as np

import pandas as pd

from scipy import stats

import seaborn as sns

Recommended Tools:

• Jupyter Notebook for interactive analysis

• Gephi for advanced visualization

• Cytoscape for biological networks

• Pajek for large network analysis

Online Courses:

• Stanford CS224W: Machine Learning with Graphs

• MIT 6.207: Networks (OpenCourseWare)

• Coursera: Applied Social Network Analysis in Python

Contact Information

Instructor: Prof. Dr. Mudasar Latif  Memon
Office: Department of Information Technology
Email:  mudasar.latif@ums.edu.pk
Office Hours: Monday & Wednesday, 2:00 PM - 4:00 PM
Lab Supervisor: [Lab Assistant Name]

Important Dates

• Course Start: [Week 1]

• Mid-term Exam: Week 8

• Project Proposal Due: Week 10

• Last Day to Drop: Week 12

• Final Project Due: Week 16

• Final Exam: [As per university schedule]

Additional Notes

For Students from Rural Areas:

• Extra support sessions available for students with limited prior programming experience

• Online resources and tutorials provided

• Peer mentoring program available

Industry Relevance:

• Guest lectures from industry professionals

• Case studies from Pakistani tech industry

• Applications in local context (e.g., social networks in Pakistan, transportation networks in Sindh)

Course Prepared By: Prof. Dr. Mudasar  Latif Memon
Department: Information Technology
Institution: The University of Modern Sciences, Tando Muhammad Khan
Date: January 2026

Appendix: Sample Lab Exercise

Lab 1: Introduction to NetworkX

Objective: Create and visualize basic networks

Tasks:

1. Install NetworkX and dependencies

2. Create a simple undirected graph

3. Add nodes and edges

4. Calculate basic properties (number of nodes, edges, degree)

5. Visualize the network

6. Save the network data