Name
Abstract | ||
|---|---|---|
Information networks are often modeled as graphs, where the vertices are associated with attributes. In this paper, we study neighborhood window analytics, namely k-hop window query, that aims to capture the properties of a local community involving the k-hop neighbors defined on the graph structures of each vertex. We develop a novel index, Dense Block Index DBIndex, to facilitate efficient processing of k-hop window queries. Extensive experimental studies conducted over both real and synthetic datasets with hundreds of millions of vertices and edges show that our proposed solutions are four orders of magnitude faster in query performance than the non-index algorithm, and are superior over the state-of-the-art solution in terms of both scalability and efficiency. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-32049-6_13 | DASFAA |
Field | DocType | Citations |
Data mining,Graph,Information networks,Vertex (geometry),Computer science,Graph analytics,Theoretical computer science,Analytics,Database,Scalability | Conference | 1 |
PageRank | References | Authors |
0.34 | 15 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Fan | 1 | 41 | 3.02 |
| Zhengkui Wang | 2 | 91 | 10.46 |
| Chee Yong Chan | 3 | 643 | 199.24 |
| Kian-Lee Tan | 4 | 6962 | 776.65 |