Fast katz and commuters : efficient estimation of social relatedness in large networks.
- University of British Columbia, Vancouver BC, Canada
- Yahoo! Research, Barcelona, Spain
Motivated by social network data mining problems such as link prediction and collaborative filtering, significant research effort has been devoted to computing topological measures including the Katz score and the commute time. Existing approaches typically approximate all pairwise relationships simultaneously. In this paper, we are interested in computing: the score for a single pair of nodes, and the top-k nodes with the best scores from a given source node. For the pairwise problem, we apply an iterative algorithm that computes upper and lower bounds for the measures we seek. This algorithm exploits a relationship between the Lanczos process and a quadrature rule. For the top-k problem, we propose an algorithm that only accesses a small portion of the graph and is related to techniques used in personalized PageRank computing. To test the scalability and accuracy of our algorithms we experiment with three real-world networks and find that these algorithms run in milliseconds to seconds without any preprocessing.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1030228
- Report Number(s):
- SAND2010-6453C; TRN: US201124%%27
- Resource Relation:
- Conference: Proposed for presentation at the WAW2010 Conference held December 13-14, 2010 in Stanford, CA.
- Country of Publication:
- United States
- Language:
- English
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