Mining interesting subgraphs by output space samplingThough the output space sampling is novel. There are v arious sampling al- gorithms. In frequent pattern mining that focus on sampling the input space. Of an FPM algorithm. Mining interesting subgraphs by output space sampling. Home SIGs SIGKDD ACM SIGKDD Explorations Newsletter Vol. 1 Mining interesting subgraphs by output space sampling abstract Mining interesting subgraphs by output space sampling. Mining Interesting Subgraphs by Output Space. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

Hasan09mininginteresting. Mohammad Al Hasan. Mining Interesting Subgraphs by Output Space Sampling. Output Space Sampling for Graph Patternsother recent work in the graph domain. A sampling algorithm to uniformly sample maximal frequent subgraphs that uses Markov Chain Monte Carlo. Though proposed as a fre- quent subgraph summarization algorithm. Is one of the ﬁrst algorithms that aims to sample the output space of patterns; however. The sampling is limited to the maximal patterns. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

Output Space Sampling for Graph Patternsgiven problem instance. By sampling output space. We mean to sample one feasible subgraph from a user- speciﬁed dis- crete distribution. The user wants to sample in pro- portion to the interestingness score. The discrete distribution can be constructed from the interestingness value of all the feasible subgraphs in the output space. Output Space Sampling for Graph Patterns. Submitte d for public ation M. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

Uniform Sampling of k Maximal Patterns. SIAM Data Mining. Efficient Mining of Top- K Frequent Subgraphsedges to explore the search space. Keep a queue Q k that contains the current top- k subgraphs found until now. When k patterns are found. Raise minsup to the support of the least frequent subgraph in Q k. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

For each subgraph added to Q k. Raise the minsup threshold. When the algorithm terminates. The top- k subgraphs have been found. Grasping frequent subgraph mining for bioinformatics. FANMOD is a tool that implements the RAND- ESU algorithm for enumerating and sampling subgraphs as well as the full enumeration algorithm. RAND- ESU was designed to address the bias of the sampling method for subgraph counting implemented in Mfinder. As Mfinder’ s sampling method is prone to sampling certain subgraphs more often than others. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

RAND- ESU fixes this bias and is faster. It enumerates all subgraphs of a certain size. Although during the execution it will ignore some of. A reverse search algorithm for mining maximal. An algorithm for mining all maximal frequent subgraphs is to enumerate the frequent subgraphs enumeration tree and to report subgraphs that do not have any frequent valid or invalid extension. This algorithm is a straightforward extension of Algorithm 2. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

To decide locally if a subgraph is a maximal frequent subgraph. We need to switch lines 8 and 9 in Algorithm 2. We also need a flag before line 7 that is set to. Mining Top- k Pairs of Correlated Subgraphs in a Large NetworkDefinition of correlation. Target the graph database scenario where there are multiple graphs. Two subgraphs A and B are called correlated if the containment of A within a data graph increases the likelihood of containing B as well. We have only one large data graph. Subgraphs A and B are defined to be correlated if the instances of A are frequently located in close proximity to the instances of B. **Mining Interesting Subgraphs by Output Space Sampling - Mohammad Al Hasan**

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