Web Science/Part2: Emerging Web Properties/Modeling the Web as a graph/The standard web graph model

31

Web Science/Part2: Emerging Web Properties

Web Science/Part2: Emerging Web Properties/MoocIndex

Web Science/Part2: Emerging Web Properties/Modeling the Web as a graph/The standard web graph model

• edit MOOC index

The standard web graph model

Learning goals

Video

Script

Quiz

Further reading

Discussion

Learning goals

1. Be able to model web pages as a graph
2. Know that the authorship graph is bipartite
3. Know what kind of graph the graph of web pages is
4. (as always) be aware of the fact that modeling is done by making choices

Video

Script

The slides can be found at File:The standard web graph model.pdf

Quiz

1 In the standard Web Graph Model vertices correspond to...

	web sites
	web pages
	urls
	anchor texts
	authors

2 In the standard Web Graph Model edges correspond to...

	web sites
	web pages
	urls
	anchor texts
	authors

3 Which of the following properties are used in the standard web graph model?

	bipartite
	edge labeled
	vertex labled
	directed
	undirected
	weighted

Further reading

1. tba

Discussion

Navigation

1. How big is the World Wide Web
   1. Problems with the question about the size of the Web
   2. 3 ways to study the Web
   3. A simplistic descriptive model
   4. An unrealistic, simplistic generative model
   5. Summary, further reading, homework
2. Simple statistical descriptive Models for the Web
   1. Counting Words And Documents
   2. Typical length of a document
   3. How to formulate a research hypothesis
   4. Number of words needed to understand most of Wikipedia
   5. Linguists way of checking simplicity of text
3. Advanced statistical descriptive models for the Web
   1. The Zipf law for text
   2. Visually straight lines on log log plots
   3. Fitting a curve on a log log plot
   4. Zipf law powerlaw or pareto law.webm
4. Modelling Similarity of Text
   1. Similarity Measures and their Applications
   2. Jaccard Similarity for Sets
   3. Cosine Similarity For Vectorspaces
   4. Probabilistic Similarity Measures Kullback Leibler Divergence
   5. Comparing Results of Similarity Merasures
5. Generative Models for the Web
   1. Introduction to generative modelling.webm
   2. Sampling from a probability distribution
   3. Evaluating a generative model
   4. Pittfalls when increasing the number of model parameters
6. Modeling the Web as a graph
   1. Reviewing terms from graph theory
   2. The standard web graph model
   3. Descriptive statistics of the web graph
   4. Topology of the web graph
   5. Modelling-graphs-with-linear-algebra

Reviewing terms from graph theory

Descriptive statistics of the web graph