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.
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Notes
- 1.
Available at http://snap.stanford.edu/data/index.html, which is used in [12].
- 2.
Other variants of k-hop window for directed graphs are possible; e.g., a vertex u is in \(W_{kh}(v)\) iff there is a \(\alpha \)-hop directed path from u to v where \(\alpha \leqslant k\).
- 3.
Note that a simpler variation of our optimization problem has been proven to be NP-hard [16].
- 4.
Note that although we could have avoided deriving W(v) a second time if we had materialized all the derived windows the first time, our approach is designed to avoid the space complexity of materializing all the windows in memory at the cost of computing each W(v) twice. We present an optimization later in this section to avoid the recomputation cost on k-hop window query.
- 5.
- 6.
- 7.
As in [12], for each dataset, EAGR is run for 10 iterations in the index construction.
- 8.
Degree means average degree of the graph. The generated graph is of Erdos-Renyi model .
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Acknowledgment
Qi Fan is supported by NGS Scholarship. This work is supported by the MOE/NUS grant R-252-000-500-112 and AWS in Education Grant award.
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Fan, Q., Wang, Z., Chan, CY., Tan, KL. (2016). Towards Neighborhood Window Analytics over Large-Scale Graphs. In: Navathe, S., Wu, W., Shekhar, S., Du, X., Wang, S., Xiong, H. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9643. Springer, Cham. https://doi.org/10.1007/978-3-319-32049-6_13
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