Difference between revisions of "Compare latentfriend familiarstranger"
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== Algorithm == | == Algorithm == | ||
| − | Because the two papers focused on totally different challenges, the methods used in those two papers are not comparable. In the ICDM paper, the authors proposed three methods to measure the similarity between two users without concerning about how to find them; In the ICWSM paper, the major task | + | Because the two papers focused on totally different challenges, the methods used in those two papers are not comparable. In the ICDM paper, the authors proposed three methods to measure the similarity between two users without concerning about how to find them; In the ICWSM paper, the major task was how to find those similar users in a large graph given the definition of similarity. |
| − | In sum, the two papers focused on different perspective of the problem and the methods are not comparable. | + | In sum, the two papers focused on totally different perspective of the problem and the methods are not comparable. |
== Dataset == | == Dataset == | ||
Revision as of 16:05, 3 November 2012
Two Papers
1 A social identity approach to identify familiar strangers in a social network
2 Latent Friend Mining from Blog Data
Problem
In the ICDM 2006 paper, the authors defined a problem of finding the "latent friend", which is "...".
In the ICWSM 2009 paper, the authors defined a problem of finding the "familiar stranger", which is "...".
From the definition of the two problems, it's very similar. However, they focused on totally different challenges. In the ICDM paper, the major challenge that the authors were dealing with is how to measure similarity between two users, while the ICWSM paper was mainly dealing with how to narrow down the search space. As a result, even both of the papers were trying to solve similar problem, but they focused on totally different perspective.
Algorithm
Because the two papers focused on totally different challenges, the methods used in those two papers are not comparable. In the ICDM paper, the authors proposed three methods to measure the similarity between two users without concerning about how to find them; In the ICWSM paper, the major task was how to find those similar users in a large graph given the definition of similarity.
In sum, the two papers focused on totally different perspective of the problem and the methods are not comparable.
Dataset
Again, because of the different perspective of the two papers, they used different dataset to evaluation their methods. In the ICDM paper, the authors used MSN Space blog data and randomly selected 10k users so that the major concern is how to measure the similarity between two users without worrying about the scalability too much. The ICWSM paper used BlogCatalog and DBLP dataset. For both of the dataset, the authors used user metadata to define similarity and evaluated the proposed methods on a large scale.
Big Picture
The two papers were solving similar problem from different perspectives, However, those two perspective are not likely to be combined easily as both of the papers make some simplifying assumption about other perspectives so that they can just focus on one.
