Communications of the ACM
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to Algorithms
On clusterings-good, bad and spectral
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Topic-Sensitive PageRank: A Context-Sensitive Ranking Algorithm for Web Search
IEEE Transactions on Knowledge and Data Engineering
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
Local Graph Partitioning using PageRank Vectors
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
From biological to social networks: Link prediction based on multi-way spectral clustering
Data & Knowledge Engineering
Efficient simrank-based similarity join over large graphs
Proceedings of the VLDB Endowment
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In this paper, we design recommender systems for blogs based on the link structure among them. We propose algorithms based on refined random walks and spectral methods. First, we observe the use of the personalized page rank vector to capture the relevance among nodes in a social network. We apply the local partitioning algorithms based on refined random walks to approximate the personalized page rank vector, and extend these ideas from undirected graphs to directed graphs. Moreover, inspired by ideas from spectral clustering, we design a similarity metric among nodes of a social network using the eigenvalues and eigenvectors of a normalized adjacency matrix of the social network graph. In order to evaluate these algorithms, we crawled a set of blogs and construct a blog graph. We expect that these algorithms based on the link structure perform very well for blogs, since the average degree of nodes in the blog graph is large. Finally, we compare the performance of our algorithms on this data set. In particular, the acceptable performance of our algorithms on this data set justifies the use of a link-based recommender system for social networks with large average degree.