Affiliation:
1. KAIST, Daejeon, Republic of Korea
2. Georgia Institute of Technology, Atlanta, GA
3. Inria Saclay, France
4. National University of Singapore, Singapore, Republic of Singapore
5. Carnegie Mellon University, Pittsburgh, PA
Abstract
Given a stream of edge additions and deletions, how can we estimate the count of triangles in it? If we can store only a subset of the edges, how can we obtain unbiased estimates with small variances?
Counting triangles (i.e., cliques of size three) in a graph is a classical problem with applications in a wide range of research areas, including social network analysis, data mining, and databases. Recently, streaming algorithms for triangle counting have been extensively studied since they can naturally be used for large dynamic graphs. However, existing algorithms cannot handle edge deletions or suffer from low accuracy.
Can we handle edge deletions while achieving high accuracy? We propose T
hink
D, which accurately estimates the counts of global triangles (i.e., all triangles) and local triangles associated with each node in a fully dynamic graph stream with additions and deletions of edges. Compared to its best competitors, T
hink
D is
(a) Accurate:
up to
4.3
×
more accurate
within the same memory budget,
(b) Fast:
up to
2.2
×
faster
for the same accuracy requirements, and
(c) Theoretically sound:
always maintaining estimates with zero bias (i.e., the difference between the true triangle count and the expected value of its estimate) and small variance. As an application, we use T
hink
D to detect suddenly emerging dense subgraphs, and we show its advantages over state-of-the-art methods.
Funder
Artificial Intelligence Graduate School Program
National Science Foundation
Korea government MSIT
Institute of Information 8 Communications Technology Planning 8 Evaluation
Army Research Laboratory
National Research Foundation of Korea
Publisher
Association for Computing Machinery (ACM)
Cited by
21 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献