Top-K aggregation queries over large networks

Xifeng Yan, Bin He, Feida Zhu, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Searching and mining large graphs today is critical to a variety of application domains, ranging from personalized recommendation in social networks, to searches for functional associations in biological pathways. In these domains, there is a need to perform aggregation operations on large-scale networks. Unfortunately the existing implementation of aggregation operations on relational databases does not guarantee superior performance in network space, especially when it involves edge traversals and joins of gigantic tables. In this paper, we investigate the neighborhood aggregation queries: Find nodes that have top-k highest aggregate values over their h-hop neighbors. While these basic queries are common in a wide range of search and recommendation tasks, surprisingly they have not been studied systematically. We developed a Local Neighborhood Aggregation framework, called LONA, to answer them efficiently. LONA exploits two properties unique in network space: First, the aggregate value for the neighboring nodes should be similar in most cases; Second, given the distribution of attribute values, it is possible to estimate the upper-bound value of aggregates. These two properties inspire the development of novel pruning techniques, forward pruning using differential index and backward pruning using partial distribution. Empirical results show that LONA could outperform the baseline algorithm up to 10 times in real-life large networks.

Original languageEnglish (US)
Title of host publication26th IEEE International Conference on Data Engineering, ICDE 2010 - Conference Proceedings
Pages377-380
Number of pages4
DOIs
StatePublished - 2010
Event26th IEEE International Conference on Data Engineering, ICDE 2010 - Long Beach, CA, United States
Duration: Mar 1 2010Mar 6 2010

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other26th IEEE International Conference on Data Engineering, ICDE 2010
CountryUnited States
CityLong Beach, CA
Period3/1/103/6/10

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

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