Frequent subgraph pattern mining on uncertain graph data

Zou, Zhaonian; Li, Jianzhong; Gao, Hong; Zhang, Shuo

doi:10.1145/1645953.1646028

Cited by 51 publications

(59 citation statements)

References 32 publications

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“…FSM on uncertain graph transactions under expected semantics considers a subgraph frequent if its expected support is greater than the threshold. Representative algorithms include Mining Uncertain Subgraph patterns (MUSE) [34], Weighted MUSE (WMUSE) [35], Uncertain Graph Index(UGRAP) [36] and Mining Uncertain Subgraph patterns under Probabilistic semantics (MUSE-P) [37]. They are proposed under expected semantics or the probabilistic semantics.…”

Section: Discussionmentioning

confidence: 99%

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

Qiao

Zhang

et al. 2018

Applied Sciences

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Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSIGRAM (Spark based Single Graph Mining), a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, we conduct the subgraph extension and support evaluation parallel across all the distributed cluster worker nodes. In addition, we also employ a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support evaluation process, which significantly improve the performance. Extensive experiments with four different real-world datasets demonstrate that the proposed algorithm outperforms the existing GRAMI (Graph Mining) algorithm by an order of magnitude for all datasets and can work with a lower support threshold.

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Section: Discussionmentioning

confidence: 99%

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

Qiao

Zhang

et al. 2018

Applied Sciences

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“…After that, frequent pattern mining was proposed to find useful, hidden pattern information from such data and various mining techniques and applications have been developed [7][8][9][10][11][12]. Frequent graph mining approaches [13][14][15][16][17] have been proposed to satisfy the needs of users wanting to obtain mining results from large and complex graph data in the real world. It is hard to express recent data as simple structures, such as itemsets because of their complicated and multidimensional features.…”

Section: Introductionmentioning

confidence: 99%

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

2016

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Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs) from databases composed of graph transaction data, which can effectively express complex and large data in the real world. In addition, various applications for graph mining have been suggested. Traditional graph pattern mining methods use a single minimum support threshold factor in order to check whether or not mined patterns are interesting. However, it is not a sufficient factor that can consider valuable characteristics of graphs such as graph sizes and features of graph elements. That is, previous methods cannot consider such important characteristics in their mining operations since they only use a fixed minimum support threshold in the mining process. For this reason, in this paper, we propose a novel graph mining algorithm that can consider various multiple, minimum support constraints according to the types of graph elements and changeable minimum support conditions, depending on lengths of graph patterns. In addition, the proposed algorithm performs in mining operations more efficiently because it can minimize duplicated operations and computational overheads by considering symmetry features of graphs. Experimental results provided in this paper demonstrate that the proposed algorithm outperforms previous mining approaches in terms of pattern generation, runtime and memory usage.

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“…Specific representation of a large variety of data, the most important and most widely used form of a diagram showing the structure, such as social networks, RFID (Radio Frequency Identification, RFID), biological genes, e-commerce, the Internet and so can be used data graph. However, with the passage of time and changes in the external environment, internal structure may also be changed, such a diagram called a dynamic graph or uncertain graph [3]. In addition, e-commerce transactions in the user data will also change with circumstances, occur when consumers and businesses to return, or for some reason when consumers replace frequented merchandising business, user behavior data structure on trading It will change occur.…”

Section: Introductionmentioning

confidence: 99%

Research of Graph Mining Base on Cloud

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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Abstract. With respect to the problems of idle consumption and luxury consumption existing in the present cloud computing platform, and the current situation that the traditional graph mining method could not satisfy the massive data mining, A dynamic graph mining method for minimum energy consumption optimization cloud was proposed, resources integrated utilizing and massive data mining has been solved. First, the cloud computing energy measurement formula was proposed, and analyzes the rationality of two types of task scheduling policy theoretically. Second, one time the problems of system energy optimization and system operation efficiency was considered. Under the condition that the system operates well, it converts the problem of system energy optimization into system cost control, and the total cost of the objective function was proposed, a model for computing adaptive allocation algorithm and the minimum energy consumption optimization cloud was designed.

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Frequent subgraph pattern mining on uncertain graph data

Cited by 51 publications

References 32 publications

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

Research of Graph Mining Base on Cloud

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