Course Support

Network analysis can be challenging, but you don’t have to navigate it alone. This page provides information about all the support resources available to help you succeed in SMM635 - Network Analytics.

Where to get help:

  • R/Python setup issues → Office hours or lab sessions
  • Network analysis questions → Course forum, then office hours
  • Conceptual questions → Lecture Q&A or course forum
  • Project guidance → Schedule appointment with instructor
  • Technical emergencies → Email instructor directly

Support Resources

Course Forum

Access the course forum through module SMM635’s Moodle website for:

  • Asking questions (conceptual or technical)
  • Sharing helpful resources
  • Discussing course material
  • Finding study partners

Forum Guidelines:

  1. Search before posting (your question may be answered)
  2. Use descriptive titles
  3. Include error messages and code snippets
  4. Be respectful and helpful
  5. Don’t share complete assignment solutions

Email Support

Module Leader: Simone.Santoni.1@city.ac.uk

Instructor: Yinji.Zhou.2@city.ac.uk

When to email:

  • Personal matters (illness, accommodations)
  • Scheduling appointments
  • Urgent technical issues
  • Administrative questions

Response time: 24-48 hours on weekdays

Email tips:

  • Use clear subject line: “SMM635: [Topic]”
  • Include your student ID
  • Be specific about your issue
  • Attach relevant files if needed

Common Issues and Solutions

Installation Problems

Common Installation Issues:

Problem: R/RStudio won’t install

  • Quick fix attempt: Check system requirements, disable antivirus temporarily, run as administrator

Problem: Python packages won’t install

  • Quick fix attempt: Update conda/pip, check firewall settings, try different repository

Problem: Network analysis packages won’t import

  • Quick fix attempt: Verify installation with library() in R or import in Python, reinstall package, check dependencies (igraph, networkx, etc.)

Network Analysis Challenges

“I don’t know where to start with network analysis”:

  1. Start with simple network visualizations
  2. Understand your data structure (nodes and edges)
  3. Begin with basic centrality measures
  4. Use sample networks before your own data
  5. Look at similar network examples in the literature

“My network code doesn’t work”:

  1. Check your data format (edge list, adjacency matrix)
  2. Verify node and edge attributes
  3. Ensure proper package loading (igraph, networkx)
  4. Test with small sample networks first
  5. Check for isolated nodes or missing edges

“I don’t understand network concepts”:

  1. Review the network terminology glossary
  2. Practice with interactive network examples
  3. Visualize concepts with simple networks
  4. Ask specific questions on the forum
  5. Attend office hours with concrete examples

Study Resources

Peer Support

Study Groups:

  • Form groups of 3-4 students
  • Meet regularly to review material
  • Work through problems together
  • Share different approaches

Peer Tutoring:

  • Advanced students available for tutoring
  • Sign up through course website
  • Free service for enrolled students

Mental Health and Wellbeing

Programming can be frustrating at times. Remember:

It’s normal to:

  • Feel stuck sometimes
  • Need multiple attempts
  • Make lots of errors
  • Need to ask for help
  • Take breaks when frustrated

Campus Resources: See “Your health and wellbeing” page, hosted in the University’s website.

Personal Emergencies

Illness or emergency: - Email instructor ASAP - Provide documentation if extended - Discuss makeup options - Don’t fall behind

FAQ

Q: How much time should I spend on this course? A: Plan for 2-3 hours outside class for every hour in class (6-9 hours/week total).

Q: Do I need prior programming experience? A: Basic programming experience in R or Python is helpful but not required. We’ll cover the essentials for network analysis.

Q: Can I use AI tools like ChatGPT? A: You may use them for learning and understanding concepts, but submitted work must be your own. Always cite any AI assistance received.

Q: What if I’m falling behind? A: Seek help immediately. Network analysis concepts build on each other, so early gaps become bigger problems later.

Q: Do I need a powerful computer? A: No, basic network analysis doesn’t require much computing power. Any computer from the last 5 years should work fine.

Q: Should I use R or Python for network analysis? A: Both are excellent. We’ll primarily use R with igraph, but Python with NetworkX is also supported. Choose based on your comfort level.

Success Tips

Top tips from successful network analysis students:

  1. Start with simple networks - Build complexity gradually
  2. Visualize first - Always plot your networks to understand structure
  3. Think about the domain - Network interpretation depends on context
  4. Practice with real data - Work with networks from your field of interest
  5. Learn the terminology - Master key concepts like centrality, clustering, paths
  6. Ask “why” questions - Don’t just calculate metrics, understand their meaning
  7. Document your analysis - Network studies require clear interpretation
  8. Compare measures - Different centrality metrics tell different stories

Remember: We want you to succeed! Don’t hesitate to reach out when you need help.