Frequent Subgraph Mining Algorithm Research Articles

FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP

As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address the challenge of mining frequent subgraphs within a single, large graph. This study advocates for the incorporation of a message sending and receiving mechanism to facilitate data sharing across various stages of the frequent subgraph mining algorithm. Additionally, it suggests employing a standard coded subgraph and sending it to the same node for global support calculation on the large graph. The adoption of the rightmost path expansion strategy in generating candidate subgraphs helps to mitigate the occurrence of redundant subgraphs. The use of standard coding ensures the unique identification of subgraphs, thus eliminating the need for isomorphism calculations. Support calculation is executed using the Minimum Image (MNI) measurement method, aligning with the downward closure attribute. The experimental results demonstrate the robust performance of the FSM-BC-BSP algorithm across diverse input datasets and parameter configurations. Notably, the algorithm exhibits exceptional efficacy, particularly in scenarios with low support requirements, showcasing its superior performance under such conditions.

Isomorphic Graph Embedding for Progressive Maximal Frequent Subgraph Mining

Maximal frequent subgraph mining (MFSM) is the task of mining only maximal frequent subgraphs, i.e., subgraphs that are not a part of other frequent subgraphs. Although many intelligent systems require MFSM, MFSM is challenging compared to frequent subgraph mining (FSM), as maximal frequent subgraphs lie in the middle of graph lattice, and FSM algorithms must explore an exponential space and an NP-hard subroutine of frequency counting. Different from prior research, which primarily focused on optimal solutions, we introduce pmMine, a progressive graph neural framework designed for MFSM in a single large graph to attain an approximate solution. The framework combines isomorphic graph embedding, non-parametric partitioning, and an efficiently top-down pattern searching strategy. The critical insight that makes pmMine work is to define the concepts of rooted subgraph and isomorphic graph embedding, in which the costly isomorphism subroutine can be efficiently performed using similarity estimation in embedding space. In addition, pmMine returns the patterns identified during the mining process in a progressive manner. We validate the efficiency and effectiveness of our technique through extensive experiments on a variety of datasets spanning various domains.

Research on online and offline double-loop teaching of college students’ mental health education based on cluster analysis algorithm

Abstract In this paper, a knowledge difficulty clustering algorithm (MIBKPC) based on multidimensional time-series data and learning path networks is proposed. The algorithm integrates the cyclic learning sequence, forgetting behavior and system interaction degree formed by learners learning some of the more difficult knowledge points, and constructs a computational model for the difficulty similarity of knowledge points based on system interaction behavior so as to better portray the difficulty similarity of knowledge points based on system interaction behavior. Then a knowledge point difficulty clustering algorithm (MFSKPC) based on multidimensional time-series data and maximum frequent subgraphs is proposed. The algorithm extracts the maximum frequent subgraphs of the atlas based on the learner’s directed learning path atlas using the gSpan-based maximum frequent subgraph mining algorithm and portrays the difficulty similarity of knowledge points based on system interaction behaviors by combining the system interaction degree and the maximum frequent subgraphs. In the accuracy comparison, the clustering accuracy of the MFSKPC algorithm for advanced learners was higher than that of the MIBKPC algorithm for both K=3 and K =5 conditions by 7.89% and 8.29%, respectively. In the comparative analysis of double-loop psychological instruction, the experimental class improved its pre and post-test scores by an average of 2.38 points, while the control group improved by an average of only 1.88 points. The experiment showed that the double-loop teaching based on the MFSKPC algorithm was more effective.

CFGM: An algorithm for closed frequent graph patterns mining

The extraction of frequent subgraphs is a basic and well studied operation on graphs. Thus, mining frequent graph patterns and problems associated with it is very important. However, the number of frequent subgraphs is potentially exponential while mining large graph patterns. This issue can be partly overcome using closed frequent graphs mining. Instead of mining all frequent subgraphs, it is more efficient to enumerate only the closed frequent graphs. Thus, in this paper, we propose a novel closed frequent subgraph mining algorithm: CFGM. In this algorithm, a stack-based architecture is used to enumerate the frequent graph represented by the depth-first search. Moreover, this algorithm defines a strict partial order among frequent graphs. We demonstrate that, with respect to this strict partial order, only maximal elements (frequent graphs) need to be discovered. A pruning strategy is developed based on this strict partial order that dramatically reduces unnecessary frequent subgraphs to be enumerated. Computational results show our algorithm displays excellent performance, especially for some large asymmetric frequent graph patterns.

Common Structure Mining of 3D Model Assembly Model Based on Frequent Subgraphs

As an important data source of complex product design and manufacturing, 3D assembly model has accumulated a large number of 3D assembly models in various manufacturing industries under the background of widely used digital design technology. In order to make better use of reusable common structure information about 3D assembly model, reduce repetitive labor, and shorten product development cycles, a common structure mining method of 3D assembly models was proposed. Firstly, based on the attribute neighbourhood diagram of individual parts of the 3D assembly model, the information of the assembly feature attributes is maintained and the nonassembly feature attributes are simplified to form an attributed assembly feature neighbourhood diagram; then, based on the assembly relationship between the parts of the 3D assembly model, the attributed assembly feature neighbourhood diagrams of the parts are combined to form a 3D assembly model attribute neighbourhood diagram. Secondly, the common structure of the 3D assembly model is extracted by the frequent subgraph mining algorithm. Finally, a set of fixture models is used to verify the results, which show that this 3D assembly model common structure mining method can accurately and effectively explore the common structure information of the product and has good results.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network.

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Novel Approach to Minimize the Memory Requirements of Frequent Subgraph Mining Techniques

Frequent subgraph mining (FSM) is a subset of the graph mining domain that is extensively used for graph classification and clustering. Over the past decade, many efficient FSM algorithms have been developed with improvements generally focused on reducing the time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms also require high memory consumption, which is another problem that should be solved. In this paper, we propose a new approach called Predictive dynamic sized structure packing (PDSSP) to minimize the memory needs of FSM algorithms. Our approach redesigns the internal data structures of FSM algorithms without making algorithmic modifications. PDSSP offers two contributions. The first is the Dynamic Sized Integer Type, a newly designed unsigned integer data type, and the second is a data structure packing technique to change the behavior of the compiler. We examined the effectiveness and efficiency of the PDSSP approach by experimentally embedding it into two state-of-the-art algorithms, gSpan and Gaston. We compared our implementations to the performance of the originals. Nearly all results show that our proposed implementation consumes less memory at each support level, suggesting that PDSSP extensions could save memory, with peak memory usage decreasing up to 38% depending on the dataset.

A New Approach to Minimize Memory Requirements of Frequent Subgraph Mining Algorithms

Frequent subgraph mining (FSM) is a subsection of graph mining domain which is extensively used for graph classification and graph clustering purposes. Over the past decade, many efficient FSM algorithms have been developed. The improvements generally focus on reducing time complexity by changing the algorithm structure or using parallel programming techniques. FSM algorithms have another problem to solve, which is the high memory consumption. In this study, a new approach called Predictive Dynamic Sized Structure Packing (PDSSP) have been proposed to minimize the memory requirement of FSM algorithms. Proposed approach redesigns the internal data structures of FSM algorithms without any algorithmic modifications. PDSSP has two contributions. The first one is the Dynamic Sized Integer Type (ds_Int) which is a newly designed unsigned integer data type. The second contribution is “Data Structure packaging” component that uses a data structure packing technique which changes the behaviour of the compiler. A number of experiments have been conducted to examine the effectiveness and efficiency of the PDSSP approach by embedding it into two state-of-art algorithms called gSpan and Gaston. Proposed implementation have been compared to the official one. Almost all results show that the proposed implementation consumes less memory on each support level. As a result, PDSSP extensions can save memory and the peak memory usage may decrease up to 38% depending on the dataset.

Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey

Frequent Subgraph Mining Algorithms in Static and Temporal Graph-Transaction Settings: A Survey

Discovering the Graph-Based Flow Patterns of Car Tourists Using License Plate Data: A Case Study in Shenzhen, China

Identifying flow patterns from massive trajectories of car tourists is considered a promising way to improve the management of tourism traffic. Previous researches have mainly focused on tourist movements at the macro-scale, such as inbound, domestic, and urban tourism using flow maps. Compared with modeling the flow patterns of tourists at the macro-scale, modeling tourist flow at the microscale is more complicated. This paper takes Dapeng Island located in Shenzhen as the study area and uses the car recognition devices to collect traffic flow. Firstly, car tourists are separated from the mixed traffic flow after analyzing the spatial-temporal characteristics of tourists and residents. Next, daily graphs of tourist movements between road segments and tourist attractions are constructed. Finally, a frequent subgraph mining algorithm is used to extract the flow patterns of car tourists. The experimental results show that (1) car tourists have obvious preferences in the selection of trip time and tourist attractions; (2) the intercity tourists tend to take multidestination trips rather than a single destination trip in the same type of attractions; (3) car tourists are inclined to park their cars in an easy-to-access place, even if the attractions visited are changed. The main contribution of this paper is to present a new method for discovering the flow patterns of car tourists hidden in massive amounts of license plate data.

Frequent Subgraph Mining by Giraph Distributed System

To overcome the challenges for managing the rapid growth of social graphs, massive Distributed Graph Mining Systems are developed, such as Pregel, GiraphHama, GraphLab, PowerLab, etc. The common approach to all systems is to divide the entire Graph Dataset into smaller divisions and use it as “think like a vertex”, the programing model is to hold up a continual graph calculation. In this paper, we use the Optimized Frequent Subgraph Mining algorithm in the Giraph framework model and make a comparative study with existing different Distributed Systems. To enhance the flexibility and performance of the novel method, we carry out different optimization techniques associating it with updating different run time limits. We also investigate how the performance could be improved by Giraph Distribution System, which plays a vital role in social graphs such as LinkedIn, Twitter, Facebook, etc. The graph input, output, cluster set up and hardware configuration play vital roles in optimizing the performance of our proposed algorithm.

Map Reduce Based Optimized Frequent Subgraph Mining Algorithm for Large Graph Database

Distributed System, plays a vital role in Frequent Subgraph Mining (FSM) to extract frequent subgraph from Large Graph database. It help to reduce in memory requirements, computational costs as well as increase in data security by distributing resources across distributed sites, which may be homogeneous or heterogeneous. In this paper, we focus on the problem related complexity of data arises in centralized system by using MapReduce framework. We proposed a MapReduced based Optimized Frequent Subgrph Mining (MOFSM) algorithm in MapReduced framework for large graph database. We also compare our algorithm with existing methods using four real-world standard datasets to verify that better solution with respect to performance and scalability of algorithm. These algorithms are used to extract subgraphs in distributed system which is important in real-world applications, such as computer vision, social network analysis, bio-informatics, financial and transportation network.

An updated dashboard of complete search FSM implementations in centralized graph transaction databases

Frequent subgraph mining algorithms are widely used in various areas for information analysis. As yet, a handful of algorithms have been proposed and defined in the literature. While several experimental studies were reported, these experiments lack critical information which are important for selecting an implementation of an algorithm for a specific case of use. In this paper, we report on experiments that we carried out on available implementations of complete search Frequent Subgraph Mining (FSM) algorithms. These experiments are conducted in order to choose a suitable FSM solution (i.e., implementation). We identified 32 algorithms in the literature, six of them were selected for our experiments, through a filtering process relying on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experiments in terms of performance metrics and input variation effect. We propose a preliminary selection of the most efficient FSM solutions for end users, based on the most tested centralized graph-transaction datasets of the literature.

Frequent Subgraph Mining for Graph based Tamil Bibliographic Big Data Analytics

Data analysis can be done in more effectively, when they are represented in the form of graphs. Especially, Frequent Subgraph Mining (FSM) is an important technique for extracting similar patterns in the graphs. Normally, things have been assumed as the graph data we are taking will fit in main memory for their processing. But, as the data grow higher and higher, they will not fit in main memory, rather they need a special framework called MapReduce to put them in a distributed fashion and to process. Many Frequent Subgraph Mining (FSM) algorithms are changing their faces to adopt the MapReduce programming paradigm. Using FSM-H, analysis had been performed on various graph based data like molecular structures, viral patterns etc., Even DBLP i.e., the computer science bibliography data have also been analyzed using this pattern extraction technique. Whereas the details of Tamil Journals and Publications are kept hidden and not available widely to do research on them, for getting their insights to improve the number and quality of the journals; and to give some input for the authors interested to work on Tamil research. In this work, we collected the Tamil journals details from the available data sources and extracted essential patterns using Frequent Subgraph Mining Technique. Also, we presented a detailed statistical analytics on certain frequently happening Tamil Journals and Conferences.

Basic and personalized pattern-based workflow fragments discovery

With an increasing number of scientific workflows accessible on public repositories, the mechanism for discovering and recommending workflow fragments is important to facilitate the reuse and repurposing of legacy workflows when novel workflows are to be constructed, where dependencies among workflow activities, which implies functional patterns with different types and characteristics, are specified in the specification of workflows. Traditional approaches ignore or seldom consider this aspect, which may have certain influence on the quality of personalized recommendation. To address this challenge, this paper proposes a novel workflow fragment discovery mechanism for personalized requirements, where discovery strategies of basic and personalized patterns are presented independently. Specifically, frequent basic subfunctions are discovered from scientific workflows by applying the frequent subgraph mining algorithm. Similar subfunctions are clustered considering their semantic relevance of topics, and clusters with high functional frequency are assumed as basic patterns. Thereafter, the multi-dimensional representation of scientific workflows is constructed to explore workflow relevance. Workflow clustering is conducted, and frequent personalized functions are discovered from clusters with similar workflows and assumed as personalized patterns under respective contents. For a personalized requirement given in terms of a workflow template, target basic patterns and candidate subfunctions are discovered, and they compose the backbone structure of solution in a novel coverage strategy. Candidate personalized patterns are applied to cover remaining functionalities of requirement. An optimal solution is obtained through atomic service optimization. Evaluation results show that this technique is accurate on discovering fragment solutions for personalized requirements in comparison with the state-of-the-art techniques.

Frequent Subgraph Mining Based Collaboration Pattern Analysis for Wikipedia

Online knowledge collaborations, where distributed members without hierarchies self-organize themselvesto create valuable contents, are prevalent in many open production systems such as Wikipedia, GitHub andsocial networks. While many existing studies from network science have been brought to analyse the general interactivebehavioural patterns embedded in these systems, how the collaborations influence the achievement outcomes hasnot been thoroughly investigated. In this paper, we mine the collaboration patterns from a micro perspective to deeplyunderstand the relationships between the collaboration among participants and the qualities of theWikipedia articles.In particular, the subgraphs contained in the collaboration networks derived from theWikipedia revision histories aretaken as the fundamental units to analyse the collaboration diversities from the subgraph properties such as size andtopology. In contrast to the predefined static motifs adopted by the previous works, the collaboration subgraphs aredirectly found from Wikipedia dataset by a frequent subgraph mining algorithm GRAMI, which is able to capturethe real dynamic collaboration patterns. Moreover, the relationships between the co-authors in the subgraphs are alsodiscriminated to further explore the collaboration patterns. The experiments exhibit the statistical properties of thecollaboration subgraphs and the efficiency of them as the metrics for the article quality assessments. We concludethat a small group of editors with relative frequent fixed collaboration patterns contribute more to the excellent articlequality than the professional extents of arbitrary individuals in the collaboration group. This discovery confirms thecommonly insight about collaboration that many heads are always better than one and concretely suggests a potentialexplanation for the increasing prevalence and success of the online knowledge collaborations

Malware homology identification based on a gene perspective

Malware homology identification is important in attacking event tracing, emergency response scheme generation, and event trend prediction. Current malware homology identification methods still rely on manual analysis, which is inefficient and cannot respond quickly to the outbreak of attack events. In response to these problems, we propose a new malware homology identification method from a gene perspective. A malware gene is represented by the subgraph, which can describe the homology of malware families. We extract the key subgraph from the function dependency graph as the malware gene by selecting the key application programming interface (API) and using the community partition algorithm. Then, we encode the gene and design a frequent subgraph mining algorithm to find the common genes between malware families. Finally, we use the family genes to guide the identification of malware based on homology. We evaluate our method with a public dataset, and the experiment results show that the accuracy of malware classification reaches 97% with high efficiency.

Supergraph Search in Graph Databases via Hierarchical Feature-Tree

Supergraph search is a fundamental problem in graph databases that is widely applied in many application scenarios. Given a graph database and a query-graph, supergraph search retrieves all data-graphs contained in the query-graph from the graph database. Most existing solutions for supergraph search follow the pruning-and-verification framework, which prune false answers based on features in the pruning phase and perform subgraph isomorphism testings on the remaining graphs in the verification phase. However, they are not scalable to handle large-sized data-graphs and query-graphs due to three drawbacks. First, they rely on a frequent subgraph mining algorithm to select features which is expensive and cannot generate large features. Second, they require a costly verification phase. Third, they process features in a fixed order without considering their relationships to the query-graph. In this paper, we address the three drawbacks and propose new indexing and query processing algorithms. In indexing, we select features directly from the data-graphs without expensive frequent subgraph mining. The features form a feature-tree that contains all-sized features and both the cost sharing and pruning power of the features are considered. In query processing, we propose a new algorithm, where the order to process features is query-dependent by considering both the cost sharing and the pruning power. We explore two optimization strategies to further improve the algorithm efficiency. The first strategy applies a lightweight graph compression technique and the second strategy optimizes the inclusion of answers. We further introduce how to efficiently maintain the index incrementally when the graph database is updated dynamically. Moreover, we propose an approximation approach to significantly reduce the computational cost for large data-graphs and/or query-graphs while preserving a high result quality. Finally, we conduct extensive performance studies on two real large datasets to demonstrate the efficiency and effectiveness of our algorithms.

A Survey of Frequent Subgraph Mining Algorithms

Graphs are broadly used data structure. Graphs are very useful in representing/analyzing and processing real world data. Evolving graphs are graphs which are frequently changing in nature. There is either increase or decrease in their size i.e. change in number of edges or/and vertices. Mining is the process done for knowledge discovery in graphs. Detecting specific patterns with their number of repetition more than a predefined threshold in graph is known as frequent subgraph mining or FSM. Real Timed data representing graphs are high volumetric or of very large in size, handling such graphs require processing them with special mechanisms and algorithms. Our review paper discovers present FSM techniques and tries to give their comparative study.

CmFSM: a scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining

BackgroundFrequent subgraphs mining is a significant problem in many practical domains. The solution of this kind of problem can particularly used in some large-scale drug molecular or biological libraries to help us find drugs or core biological structures rapidly and predict toxicity of some unknown compounds. The main challenge is its efficiency, as (i) it is computationally intensive to test for graph isomorphisms, and (ii) the graph collection to be mined and mining results can be very large. Existing solutions often require days to derive mining results from biological networks even with relative low support threshold. Also, the whole mining results always cannot be stored in single node memory.ResultsIn this paper, we implement a parallel acceleration tool for classical frequent subgraph mining algorithm called cmFSM. The core idea is to employ parallel techniques to parallelize extension tasks, so as to reduce computation time. On the other hand, we employ multi-node strategy to solve the problem of memory constraints. The parallel optimization of cmFSM is carried out on three different levels, including the fine-grained OpenMP parallelization on single node, multi-node multi-process parallel acceleration and CPU-MIC collaborated parallel optimization.ConclusionsEvaluation results show that cmFSM clearly outperforms the existing state-of-the-art miners even if we only hold a few parallel computing resources. It means that cmFSM provides a practical solution to frequent subgraph mining problem with huge number of mining results. Specifically, our solution is up to one order of magnitude faster than the best CPU-based approach on single node and presents a promising scalability of massive mining tasks in multi-node scenario. More source code are available at:Source Code: https://github.com/ysycloud/cmFSM.