Journal Article10.1109/TKDE.2010.80
Mining Frequent Subgraph Patterns from Uncertain Graph Data
TL;DR: This paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data and uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining sub graph patterns.
read more
Abstract: In many real applications, graph data is subject to uncertainties due to incompleteness and imprecision of data. Mining such uncertain graph data is semantically different from and computationally more challenging than mining conventional exact graph data. This paper investigates the problem of mining uncertain graph data and especially focuses on mining frequent subgraph patterns on an uncertain graph database. A novel model of uncertain graphs is presented, and the frequent subgraph pattern mining problem is formalized by introducing a new measure, called expected support. This problem is proved to be NP-hard. An approximate mining algorithm is proposed to find a set of approximately frequent subgraph patterns by allowing an error tolerance on expected supports of discovered subgraph patterns. The algorithm uses efficient methods to determine whether a subgraph pattern can be output or not and a new pruning method to reduce the complexity of examining subgraph patterns. Analytical and experimental results show that the algorithm is very efficient, accurate, and scalable for large uncertain graph databases. To the best of our knowledge, this paper is the first one to investigate the problem of mining frequent subgraph patterns from uncertain graph data.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
CatchSync: catching synchronized behavior in large directed graphs
Meng Jiang,Peng Cui,Alex Beutel,Christos Faloutsos,Shiqiang Yang +4 more
- 24 Aug 2014
TL;DR: This work proposes a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters, and introduces novel measures to quantify both concepts ("synchronicity" and "normality") and proposes a parameter-free algorithm that works on the resulting synchronicities-normality plots.
186
Patent
Frequent Pattern Mining
Shi Han,Yingnong Dang,Dongmei Zhang,Song Ge +3 more
- 27 Apr 2011
TL;DR: This comprehensive reference consists of 18 chapters from prominent researchers in the field of frequent pattern mining, and contains a survey describing key research on the topic, a case study and future directions.
129
Injecting uncertainty in graphs for identity obfuscation
Paolo Boldi,Francesco Bonchi,Aristides Gionis,Tamir Tassa +3 more
- 01 Jul 2012
TL;DR: A new anonymization approach that is based on injecting uncertainty in social graphs and publishing the resulting uncertain graphs is introduced, using a finer-grained perturbation that adds or removes edges partially to achieve the same desired level of obfuscation with smaller changes in the data, thus maintaining higher utility.
Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach
TL;DR: This work proposes a fast and effective method, CatchSync, which exploits two of the tell-tale signs left in graphs by fraudsters, and introduces novel measures to quantify both concepts (“synchronicity” and “normality”) and proposes a parameter-free algorithm that works on the resulting synchronicities-normality plots.
Efficient subgraph search over large uncertain graphs
Ye Yuan,Guoren Wang,Haixun Wang,Lei Chen +3 more
- 01 Aug 2011
TL;DR: This paper considers the problem of answering threshold-based probabilistic queries over a large uncertain graph database with the possible world semantics and adopts a filtering-and-verification strategy to speed up the search.
References
•Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
Michael Randolph Garey,David S. Johnson +1 more
- 01 Jan 1979
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
The complexity of computing the permanent
TL;DR: It is shown that the permanent function of (0, 1)-matrices is a complete problem for the class of counting problems associated with nondeterministic polynomial time computations.
3.3K
•Book
Probability and Computing: Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher,Eli Upfal +1 more
- 01 Jan 2005
TL;DR: Preface 1. Events and probability 2. Discrete random variables and expectation 3. Moments and deviations 4. Chernoff bounds 5. Balls, bins and random graphs 6. Probabilistic method 7. Markov chains and random walks 8. Continuous distributions and the Poisson process
2.7K
gSpan: graph-based substructure pattern mining
Xifeng Yan,Jiawei Han +1 more
- 09 Dec 2002
TL;DR: A novel algorithm called gSpan (graph-based substructure pattern mining), which discovers frequent substructures without candidate generation by building a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label.
Related Papers (5)
Ye Yuan,Guoren Wang,Haixun Wang,Lei Chen +3 more
- 01 Aug 2011
Xifeng Yan,Jiawei Han +1 more
- 09 Dec 2002