The background concepts used in this course are quite general. You will find many resources online to look up. You are free to use your favourite books. Wikipedia is a good source for specific definitions. **The course assumes that you know the following conecpts. Make sure that you understand them.**.

Background Concepts:

- Graphs
- Bipartite graphs
- Walks and Paths
- Breadth first search and Depth First search
- Connected components
- Shortest paths and Dijkstra's algorithm
- Spanning trees and minimum spanning trees. Kruskal's algorithm.
- Planar graphs
- Graph coloring. Four color theorem (not the proof, only the theorem statement.)
- Directed graphs and strongly connected components

There is of course wikipedia. There are many other sources, for example:

- Make sure you are comfortable with material of "Graph algorithms" in this algorithms book.
- Alternatives are: These lecture notes
- Or This graph theory book

Concepts

- Big-O
- Big-Omega
- Theta

Sources: Wikipedia. Also, Page 43-48 in this book.

- Basic probability
- Expectations
- Union bound

Sources: Wikipedia.

- See background problems Exercise 0

- Slides
Required Reading

- Kleinberg-Easley. Chapter 2.
- Background material: Topic 0.

Additional (optional) reading:

- None for this lecture

- Slides on cascades
- Reading:
- Kleinberg & Easley 2010 Chapter 19: cascades.

- Slides
Reading

- Kleinberg & Easley 2010 Chapter 19.
- Kempe 2018 Chapter 8.6
- Maximizing spread of influence

Additional (optional) reading:

- Rest of Kempe 2018 Chapter 8.

- Slides
Reading

- Kleinberg & Easley 2010 Chapter 3 (including advanced material), Chapter 4, upto Sec 4.4.
- Tie strength in mobile nets

Additional (optional) reading.

- Rest of Kleinberg & Easley 2010 Chapter 4
- Granovetter, Strength of Weak ties

Reading:

- Slides -- web graphs and ranking
- slides -- spectral analysis
- ipython notebook
- Reading
- Kleinberg & Easley 2010 – Chapter 13 & 14.

- Additional (optional) reading:

- Slides
- Reading
- Kempe 2018 Chapter 3. Upto Sec. 3.5.2.
- C. Aggarwal: Data Mining Textbook [acccessible from University network] Clustering: Sections 6.3, 6.4, 6.6

- Additional (optional) reading

- Slides
- ipython notebook
Reading:

- Kempe 2018 -- Chapter 6, Beginning to Sec 6.1
- Kleinberg & Easley 2010 -- Chapter 18

Additional (optional) readings.

Reading

- Collective Dynamics of Smalls world networks (accessible from university network or vpn)
- Navigation in a small world (accessible from university network or vpn)
- Kempe 2018 Chapter 7.
- Kleinberg & Easley 2010 – Chapter 20.

Additional (optional) readings

- Slides
- Reading
- Kleinberg & Easley 2010 Chapter 21.

- Additional (optional) reading
- Kleinberg & Easley 2010 Chapter 21: Additional materials.