Top-K aggregation queries over large networks

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 val...

• Type: Conference
• Year: 2010
• Where: ICDE
• Authors: Xifeng Yan, Bin He, Feida Zhu, Jiawei Han
• Keywords: Aggregate Value, Aggregation Operations, Database, ICDE 2010, Neighborhood Aggregation