1. Cascading Behavior in Large Blog Graphs: Patterns and a model offence

2. Overall Yet another power-law paper Visually tedious? Come on, give me some new distributions! Joking

3. Goal Build a model that generates realistic cascades, so that it can help us with link prediction and outlier detection. Is the model realistic? No dataset is partial, proposed modeling method is weakly validated, the results are not very consistent. How to predict and detect? What? Where and how?

4. Goal (cont.) Surprising findings, (many Sur. in the paper), 4 surprising findings, but why you think they are surprising Popularity drops with “ power-law ” Exponent = 1.5 Cascade sizes “ power-law ” distributed Cascade shape “ stars ” A 12-page paper with 10 questions in the first page. (To my surprise) Can a solid paper achieve this? Well-solved? No, e.g., how do links evolve? how can we build models that generate realistic cascades? Are these questions important? I doubt, but, at least, not well explained in the paper

5. Dataset 2007 paper using 2005 data, Social networks are rapidly evolved Starting with several most-cited blog posts Biases Not representative Limited depth=100 and breadth=500 Why? Do these two limitations bring biases to your characterization on this derived graph?

6. Observations Yet, another power-law paper. (Not visually interesting) Observation 1: The popularity of posts drops with a power law, instead of exponentially, that one may have expected. Not exactly power-law, see the tail sections Why people assume it should be exponential?

7. Observations (cont.) Where are G 13, G 19 - G 28, etc, how do they look like, why don ’ t you list them here? Only 1.2% of cascades are topological complex > G 2, How well the properties this small portion apply to the properties of the whole population? Is this statistically significant? Since it is a very small portion, any importance to study it again?

8. Observations (cont.) The cascades are mostly tree-like? Based on the below graphs, how could you predict the tree-like characteristics?

9. Modeling Give a process (steps i - iv) to generate a single cascade. No justification why these 4 steps are correct or reasonable, not others Validation of the model is weak Capture the simplest ones, no results are show on the more complex ones.

10. Modeling The results are not very consistent.

11. Conclusion with 2 questions What the implications of this study in reality? Why they are important?