| 1. |
|
|
| 2. |
- Lu, Zhengdong, et al.
(författare)
-
Supervised Link Prediction Using Multiple Sources
- 2010
-
Ingår i: Proceedings of the IEEE International Conference on Data Mining (ICDM). ; , s. 923-928
-
Konferensbidrag (refereegranskat)abstract
- Link prediction is a fundamental problem in social network analysis and modern-day commercial applications such as Facebook and Myspace. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary social networks and/or derived proximity networks available. The contribution of the paper is twofold: (1) a supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks; (2) a feature design scheme for constructing a rich variety of path-based features using multiple sources, and an effective feature selection strategy based on structured sparsity. Extensive experiments on three real-world collaboration networks show that our model can effectively learn to predict new links using multiple sources, yielding higher prediction accuracy than unsupervised and singlesource supervised models.
|
|
| 3. |
- Song, Han Hee, et al.
(författare)
-
Clustered Embedding of Massive Social Networks
- 2012
-
Ingår i: Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE joint international conference on Measurement and Modeling of Computer Systems. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450310970 ; , s. 331-342
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The explosive growth of social networks has created numerous exciting research opportunities. A central concept in the analysis of social networks is a proximity measure, which captures the closeness or similarity between nodes in a social network. Despite much research on proximity measures, there is a lack of techniques to eciently and accurately compute proximity measures for large-scale social networks. In this paper, we develop a novel dimensionality reduction technique, called clustered spectral graph embedding, to embed the graphs adjacency matrix into a much smaller matrix. The embedded matrix together with the embedding subspaces capture the essential clustering and spectral structure of the original graph and allows a wide range of analysis tasks to be performed in an ecient and accurate fashion. To evaluate our technique, we use three large real-world social network datasets: Flickr, LiveJournal and MySpace, with up to 2 million nodes and 90 million links. Our results clearly demonstrate the accuracy, scalability and exibility of our approach in the context of three importantsocial network analysis tasks: proximity estimation, missing link inference, and link prediction.
|
|
| 4. |
- Vasuki, Vishvas, et al.
(författare)
-
Scalable Affiliation Recommendation using Auxiliary Networks
- 2011
-
Ingår i: ACM Transactions on Intelligent Systems and Technology. - : Association for Computing Machinery (ACM). - 2157-6904 .- 2157-6912. ; 3:1, s. 3:1-3:20
-
Tidskriftsartikel (refereegranskat)abstract
- Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations—they can be a user?s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective. We also introduce scalable versions of these algorithms, and demonstrate their effectiveness. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.
|
|
