Difference between revisions of "Compare Yang et al Modeling Information Diffusion in Implicit Networks and Inferring the Diffusion and Evolution of Topics in Social Communities"
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= Paper links = | = Paper links = | ||
| − | Yang et al. [[Yang_et_al_Modeling_Information_Diffusion_in_Implicit_Networks]] (Simply name it YANG) | + | * Yang et al. [[Yang_et_al_Modeling_Information_Diffusion_in_Implicit_Networks]] (Simply name it YANG) |
| − | Cindy et al. [[Inferring_the_Diffusion_and_Evolution_of_Topics_in_Social_Communities]] (Simply name it CINDY) | + | * Cindy et al. [[Inferring_the_Diffusion_and_Evolution_of_Topics_in_Social_Communities]] (Simply name it CINDY) |
= Comparison = | = Comparison = | ||
== method == | == method == | ||
| − | YANG proposed Linear | + | * YANG proposed Linear Influence Model, which means the diffusion is proceeded with linear factor. |
| + | * CINDY treat this problem in a top-down view. They consider text-based features together with network graph topology. | ||
| + | And to simulate the diffusion, they proposed a Gaussian Markov Random Field model. | ||
| + | |||
| + | == dataset == | ||
| + | * YANG : [http://malt.ml.cmu.edu/mw/index.php/Volume_Time_Series_of_Twitter_HashTags_and_Memtracker_phrases Volume Time Series of Twitter Hashtags and Memetracker Phrases] | ||
| + | * CINDY use DBLP and Twitter. | ||
| + | |||
| + | == problem == | ||
| + | * YANG: model the global influence of a node on the rate of diffusion of information. | ||
| + | * CINDY: model the information diffusion and evolution for topic-based information. | ||
| + | |||
| + | == big idea == | ||
| + | * YANG: their work is highlighted to analyze the global effect rather than point-wise or local influences. | ||
| + | * CINDY: they do not analyze the diffusion only: evolution of topics and diffusion is also important in real-world networks. | ||
| + | |||
| + | == others == | ||
| + | I observed that YANG focus more on the global structure of networks themselves, | ||
| + | while CINDY combine network structure and text information (for similarity and topic trend analysis). | ||
| + | |||
| + | |||
| + | = Answers to the 6 questions = | ||
| + | * [1] How much time did you spend reading the (new, non-wikified) paper you summarized? '''(3 hours)''' | ||
| + | * [2] How much time did you spend reading the old wikified paper? '''(30 minutes with the help of existing wiki summary.)''' | ||
| + | * [3] How much time did you spend reading the summary of the old paper? '''(20 minutes)''' | ||
| + | * [4] How much time did you spend reading background materiel? '''(5 minutes , just browsing. They are not very helpful in order to understand the paper itself)''' | ||
| + | * [5] Was there a study plan for the old paper? '''(No. oops.)''' | ||
| + | * [6] Give us any additional feedback you might have about this assignment. | ||
| + | This is a nice assignment. I feel that I learn a lot of different methods, viewpoints and research topics. | ||
| + | |||
| + | One suggestion is to make the "Related Paper" chosen by us ourselves, not by the author of initial wiki summary. | ||
| + | Because we might have more background readings or different interesting points in the paper. | ||
| + | |||
| + | Thanks! | ||
Latest revision as of 00:31, 6 November 2012
Contents
Paper links
- Yang et al. Yang_et_al_Modeling_Information_Diffusion_in_Implicit_Networks (Simply name it YANG)
- Cindy et al. Inferring_the_Diffusion_and_Evolution_of_Topics_in_Social_Communities (Simply name it CINDY)
Comparison
method
- YANG proposed Linear Influence Model, which means the diffusion is proceeded with linear factor.
- CINDY treat this problem in a top-down view. They consider text-based features together with network graph topology.
And to simulate the diffusion, they proposed a Gaussian Markov Random Field model.
dataset
- YANG : Volume Time Series of Twitter Hashtags and Memetracker Phrases
- CINDY use DBLP and Twitter.
problem
- YANG: model the global influence of a node on the rate of diffusion of information.
- CINDY: model the information diffusion and evolution for topic-based information.
big idea
- YANG: their work is highlighted to analyze the global effect rather than point-wise or local influences.
- CINDY: they do not analyze the diffusion only: evolution of topics and diffusion is also important in real-world networks.
others
I observed that YANG focus more on the global structure of networks themselves, while CINDY combine network structure and text information (for similarity and topic trend analysis).
Answers to the 6 questions
- [1] How much time did you spend reading the (new, non-wikified) paper you summarized? (3 hours)
- [2] How much time did you spend reading the old wikified paper? (30 minutes with the help of existing wiki summary.)
- [3] How much time did you spend reading the summary of the old paper? (20 minutes)
- [4] How much time did you spend reading background materiel? (5 minutes , just browsing. They are not very helpful in order to understand the paper itself)
- [5] Was there a study plan for the old paper? (No. oops.)
- [6] Give us any additional feedback you might have about this assignment.
This is a nice assignment. I feel that I learn a lot of different methods, viewpoints and research topics.
One suggestion is to make the "Related Paper" chosen by us ourselves, not by the author of initial wiki summary. Because we might have more background readings or different interesting points in the paper.
Thanks!
