Difference between revisions of "M. E. J. Newman PNAS 2006"
| Line 18: | Line 18: | ||
=== Dataset === | === Dataset === | ||
| − | * [[UsesDataset::Karate network|Zachary's karate network]] 34 nodes | + | * [[UsesDataset::Karate network|Zachary's karate network]] 34 nodes. |
** This dataset is a famous benchmark dataset for community detection. This paper shows the proposed eigen-vector based method can find communities observed in real life. | ** This dataset is a famous benchmark dataset for community detection. This paper shows the proposed eigen-vector based method can find communities observed in real life. | ||
| + | * [[UsesDataset::Jazz musicians network|Pablo's jazz musicians network]] 198 nodes. | ||
| + | ** From [http://www.worldscientific.com/doi/pdf/10.1142/S0219525903001067 Gleiser, P. & Danon, L. Community Structure in Jazz. (2003) Adv. Complex Systems 6, 565–573. ] | ||
| + | * [[UsesDataset::Metabolic network|Jeong's metabolic network]] 198 nodes. | ||
| + | ** From [http://www.nature.com/nature/journal/v407/n6804/full/407651a0.html Jeong, H., Tombor, B., Albert, R., Oltvai, Z. N. & Barab ́asi, A.-L. The large-scale organization of metabolic networks. (2000) Nature 407, 651–654.] | ||
== Strengths and weaknesses == | == Strengths and weaknesses == | ||
Revision as of 18:43, 30 September 2012
Contents
Citation
@article{Newman:2006:Proc-Natl-Acad-Sci-U-S-A:16723398,
author = {Newman, M E},
journal = {Proc Natl Acad Sci U S A},
pages = {8577-8582},
title = {Modularity and community structure in networks},
volume = 103,
year = 2006
Online version
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
Summary
Dataset
- Zachary's karate network 34 nodes.
- This dataset is a famous benchmark dataset for community detection. This paper shows the proposed eigen-vector based method can find communities observed in real life.
- Pablo's jazz musicians network 198 nodes.
- Jeong's metabolic network 198 nodes.
Strengths and weaknesses
- Strength
The problems the authors pointed out regarding existing social CF are genuine to social media, but have not yet been fully considered. The authors propose a method that can solve the problem in a unified way based on MF. In addition, they actually created a Facebook application to collect user behavior data in Facebook. By doing so, they got rich features and conducted detailed analyses on users behaviors, too.
- weakness
They manually set the weight for each objective function, and this might be time-consuming in practical situations. In addition, we are not clear which part of extensions actually contributed the increase of accuracy, because there are no systematic analyses. Thus, we cannot get much insight about users behaviors. We also cannot get much insight to improve the proposed method.
Possible impact
If they were able to publish data, it would have much more impact. (Actually, they cannot publish their data because of the requirement from the funding project.) Also, if they conducted analyses about how much each part of the extension contributed to the performance. If they did so, we could have insight about users' behaviors, or ways to improve existing social CF.
Recommendation for whether or not to assign the paper as required/optional reading in later classes.
No. There are not much insight about phenomena in social media.
Related Papers
- There are many papers on how to combine social network and users' other actions.
- S. H. Yang et al. WWW 2011 : S. H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: Joint friendship and interest propagation in social networks.In WWW-11, 2011.
Study Plan
To understand some matrix calculation, I read some of the paper; K. B. Petersen and M. S. Pedersen. The matrix cookbook, 2008.
