Subgraph Mining Research Articles

Mining weighted subgraphs in a single large graph

Weighted single large graphs are often used to simulate complex systems, and thus mining frequent subgraphs in a weighted large graph is an important issue that has attracted the attention of many researchers. Within the studies on the mining of frequent subgraphs, the Graph Mining (GraMi) algorithm is considered as a state-of-the-art algorithm. However, this algorithm can only be implemented on graph datasets which have node labels and edge labels, without weights, and this means that the importance of all objects in a large graph is the same. In this paper, we propose a frequent subgraph algorithm on a weighted large graph, called Weighted Graph Mining (WeGraMi), which is based on two effective strategies to mining weighted subgraphs. Firstly, we apply a new strategy to calculate the weight of all mined subgraphs, which is based on the weights of the nodes in that subgraph. Secondly, we apply a search space pruning strategy based on the existing weights; if a frequent subgraph cannot satisfy the given weighting threshold, that subgraph will be pruned, which can reduce the processing time and storage space needed. With both directed and undirected graph datasets, our experimental results show that the runtime as well as the memory requirements of our algorithm are significantly better than those of GraMi.

Generalizing Design of Support Measures for Counting Frequent Patterns in Graphs.

Frequent subgraph mining (FSM) from graphs is an active subject in computer science research. One major challenge in FSM is the development of support measures, which are basically functions that map a pattern to its frequency count in a database. Current state-of-the-art in this topic features a hypergraph-based framework for modeling pattern occurrences which unifies the two main flavors of support measures: the overlap-graph based maximum independent set measure (MIS) and minimum image/instance based (MNI) measures. For the purpose of exploring the middle ground between these two groups and guiding the development of new support measures, we present general sufficient conditions for designing new support measures in hypergraph framework, which can be applied to MNI and other support measures that are not included in the overlap graph framework. We utilize the sufficient conditions to generalize MNI and minimum-instance measure (MI) for designing user-defined linear-time measures. Furthermore, we show that a maximum independent subedge set (MISS) measure developed from the sufficient conditions can fill the gap between MIS and MI in computation complexity and support count.

SIAS-miner: mining subjectively interesting attributed subgraphs

Data clustering, local pattern mining, and community detection in graphs are three mature areas of data mining and machine learning. In recent years, attributed subgraph mining has emerged as a new powerful data mining task in the intersection of these areas. Given a graph and a set of attributes for each vertex, attributed subgraph mining aims to find cohesive subgraphs for which (some of) the attribute values have exceptional values. The principled integration of graph and attribute data poses two challenges: (1) the definition of a pattern syntax (the abstract form of patterns) that is intuitive and lends itself to efficient search, and (2) the formalization of the interestingness of such patterns. We propose an integrated solution to both of these challenges. The proposed pattern syntax improves upon prior work in being both highly flexible and intuitive. Plus, we define an effective and principled algorithm to enumerate patterns of this syntax. The proposed approach for quantifying interestingness of these patterns is rooted in information theory, and is able to account for background knowledge on the data. While prior work quantified the interestingness for the cohesion of the subgraph and for the exceptionality of its attributes separately, then combining these in a parameterized trade-off, we instead handle this trade-off implicitly in a principled, parameter-free manner. Empirical results confirm we can efficiently find highly interesting subgraphs.

MILES: a Java tool to extract node-specific enriched subgraphs in biomolecular networks

Abstract Summary The growing availability of biomolecular networks has led to a need for analysis methods that are able to extract biologically meaningful information from these complex data structures. Here we present MILES (MIning Labeled Enriched Subgraphs), a Java-based subgraph mining tool for discovering motifs that are associated to a given set of nodes of interest, such as a list of genes or proteins, in biomolecular networks. It provides a unique extension to the widely used enrichment analysis methodologies by integrating network structure and functional annotations in order to discern novel biological subgraphs which are enriched in the targets of interest. The tool can handle various types of input data, including (un)directed, (un)connected and multi-label networks, and is thus compatible with most types of biomolecular networks. Availability and implementation MILES is available as a platform-independent Java application at https://github.com/pmoris/miles-subgraph-miner alongside a user manual, example datasets and the source code. Supplementary information Supplementary data are available at Bioinformatics online.

High performance frequent subgraph mining on transaction datasets: A survey and performance comparison

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years, several research groups have attempted to handle the Frequent Subgraph Mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google's MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and noniterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this survey, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results.

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.

Learning compact graph representations via an encoder-decoder network

Feature representation learning for classification of multiple graphs is a problem with practical applications in many domains. For instance, in chemoinformatics, the learned feature representations of molecular graphs can be used to classify molecules which exhibit anti-cancer properties. In many previous work, including discriminative subgraph mining and graphlet-based approaches, a graph representation is derived by counting the occurrence of various graph sub-structures. However, these representations fail to capture the co-occurrence patterns that are inherently present among different sub-structures. Recently, various methods (e.g., DeepWalk, node2vec) have been proposed to learn representations for nodes in a graph. These methods use node co-occurrence to learn node embeddings. However, these methods fail to capture the co-occurrence relationship between more complex sub-structures in the graph since they were designed primarily for node representation learning. In this work, we study the problem of learning graph representations that can capture the structural and functional similarity of sub-structures (as evidenced by their co-occurrence relationship) in graphs. This is particularly useful when classifying graphs that have very few sub-structures in common. The proposed method uses an encoder-decoder model to predict the random walk sequence along neighboring regions (or sub-structures) in a graph given a random walk along a particular region. The method is unsupervised and can be used to obtain generic feature representations of graphs making it applicable with various types of graphs. We evaluate the learned representations using several real-world datasets on the binary graph classification task. The proposed model is able to achieve superior results against multiple 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

A 3D assembly model retrieval method based on assembly information

PurposeThe purpose of this paper is to propose a three-dimensional (3D) assembly model retrieval method based on assembling semantic information to address semantic mismatches, poor accuracy and low efficiency in existing 3D assembly model retrieval methods.Design/methodology/approachThe paper proposes an assembly model retrieval method. First, assembly information retrieval is performed, and 3D models that conform to the design intention of the assembly are found by retrieving the code. On this basis, because there are conjugate subgraphs between attributed adjacency graphs (AAG) that have an assembly relationship, the assembly model geometric retrieval is translated into a problem of finding AAGs with a conjugate subgraph. Finally, the frequent subgraph mining method is used to retrieve AAGs with conjugate subgraphs.FindingsThe method improved the efficiency and accuracy of assembly model retrieval.Practical implicationsThe examples illustrate the specific retrieval process and verify the feasibility and reasonability of the assembly model retrieval method in practical applications.Originality/valueThe assembly model retrieval method in the paper is an original method. Compared with other methods, good results were obtained.

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.

Frequent subgraph mining in oceanographic multi-level directed graphs

ABSTRACTWe present an adaptation and application of frequent subgraph mining (FSM) in a time series of spatial multi-level directed graphs depicting probabilistic transitions of water masses between neighboring sea areas within a given time interval. The directed graphs are created from the results of the numerical model, the Mediterranean Ocean Forecasting System. We assign unique labels (geographical locations) to vertices of the multi-level directed graphs. Then, we add the edge labels as discretized values of the probabilities of transitions between vertices. This modification allows the use of the established algorithm gSpan to search for frequently directed subgraphs in the sequence of such directed graphs. Thus, we obtain both general and specific subgraphs, such as convergences, divergences, and paths of the ocean currents in the numerical model. The resulting substructures, revealed by directed subgraphs, match oceanographic structures (gyres, convergences/divergences, and paths) deduced from field observations, and can also serve as a tool for the validation of the numerical model of circulation in the sea.

An Approach for Interesting Subgraph Mining from Web Log Data Using W-Gaston Algorithm

Graph-Based Data Mining (GBDM) is an emerging research topic nowadays, for the retrieval of the essential information from the graph database. There exist many algorithms that find frequent patterns in a given graph database. One such algorithm, GASTON uses support based on frequency to discover frequent patterns. The discovery phase in the Gaston algorithm is time-consuming, and the pages captured the interest of the users are ignored by the existing GASTON algorithm. This paper proposes an algorithm, Weighted-Gaston (W-Gaston) algorithm, by modifying the existing Gaston algorithm. Here, four interesting measures are developed based on the frequency, entropy, and the page duration, for the retrieval of the interesting sub-graphs. The proposed interesting measures include four types of support: (1) Support based on the page duration (W-Support), (2) Support based on the entropy (E-Support), (3) Support based on the page duration and the entropy (WE-Support), and (4) Support based on the frequency, page duration, and the entropy (FWE-Support). The simulation of the proposed work is done using the MSNBC and the weblog databases. The experimental results show that the proposed algorithm performed well as compared with the existing algorithms.

Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs

SummaryComplex networks have been used in many scientific disciplines like sociology, microbiology, and telecommunication to represent the interactions among them. Graphs are generally used for representing such complex networks. Mining significant frequent patterns from graph databases has been a challenging area of research. A number of sub graph mining algorithms have been proposed for finding frequent fragments in molecular databases. A very few algorithms have been proposed for mining frequent patterns from large communication networks. All these algorithms perform well on medium size networks and fail on very large graphs. The scalability of these algorithms has been an issue because of the enormous memory requirements and also due to the exponential number of frequent sub graphs possible. In this paper, we propose a compact way of representing graph databases and also use it in a maximal frequent sub graph mining algorithm. The algorithm is found to be efficient and scalable to very large graph databases.

Efficient frequent subgraph mining on large streaming graphs

Efficient frequent subgraph mining on large streaming graphs

Probabilistic and exact frequent subtree mining in graphs beyond forests

Motivated by the impressive predictive power of simple patterns, we consider the problem of mining frequent subtrees in arbitrary graphs. Although the restriction of the pattern language to trees does not resolve the computational complexity of frequent subgraph mining, in a recent work we have shown that it gives rise to an algorithm generating probabilistic frequent subtrees, a random subset of all frequent subtrees, from arbitrary graphs with polynomial delay. It is based on replacing each transaction graph in the input database with a forest formed by a random subset of its spanning trees. This simple technique turned out to be quite powerful on molecule classification tasks. It has, however, the drawback that the number of sampled spanning trees must be bounded by a polynomial of the size of the transaction graphs, resulting in less impressive recall even for slightly more complex structures beyond molecular graphs. To overcome this limitation, in this work we propose an algorithm mining probabilistic frequent subtrees also with polynomial delay, but by replacing each graph with a forest formed by an exponentially large implicit subset of its spanning trees. We demonstrate the superiority of our algorithm over the simple one on threshold graphs used e.g. in spectral clustering. In addition, providing sufficient conditions for the completeness and efficiency of our algorithm, we obtain a positive complexity result on exact frequent subtree mining for a novel, practically and theoretically relevant graph class that is orthogonal to all graph classes defined by some constant bound on monotone graph properties.

Aesthetics-Guided Graph Clustering With Absent Modalities Imputation.

Accurately clustering Internet-scale Internet users into multiple communities according to their aesthetic styles is a useful technique in image modeling and data mining. In this paper, we present a novel partially supervised model which seeks a sparse representation to capture photo aesthetics. It optimally fuzes multi-channel features, i.e., human gaze behavior, quality scores, and semantic tags, each of which could be absent. Afterward, by leveraging the KL-divergence to distinguish the aesthetic distributions between photo sets, a large-scale graph is constructed to describe the aesthetic correlations between users. Finally, a dense subgraph mining algorithm which intrinsically supports outliers (i.e., unique users not belong to any community) is adopted to detect aesthetic communities. The comprehensive experimental results on a million-scale image set grabbed from Flickr have demonstrated the superiority of our method. As a byproduct, the discovered aesthetic communities can enhance photo retargeting and video summarization substantially.

Query Processing Based on Similar Nodes on SimRank Graph

With the exploding of information on Internet, relationship graphs have widely used in research areas, such as subgraph search, subgraph matching and subgraph mining. However, graph in real lives is always large-scale with millions of nodes and edges, leading to the analysis on the graph time-consuming and space-consuming. This paper analyze on the structure feature of graphs and discover the claster in the graph. Based on the claster feature of graphs, the author proposed a new algorithm which focuses on the similar vertexes query on the graph. At first, apply k-mean method to decompose the graph into small part, and then the similar degree on the part can be computed.

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.

Efficient Mining of Frequent Patterns on Uncertain Graphs

Uncertainty is intrinsic to a wide spectrum of real-life applications, which inevitably applies to graph data. Representative uncertain graphs are seen in bio-informatics, social networks, etc. This paper motivates the problem of frequent subgraph mining on single uncertain graphs, and investigates two different - probabilistic and expected - semantics in terms of support definitions. First, we present an enumeration-evaluation algorithm to solve the problem under probabilistic semantics. By showing the support computation under probabilistic semantics is #P-complete, we develop an approximation algorithm with accuracy guarantee for efficient problem-solving. To enhance the solution, we devise computation sharing techniques to achieve better mining performance. Afterwards, the algorithm is extended in a similar flavor to handle the problem under expected semantics, where checkpoint-based pruning and validation techniques are integrated. Experiment results on real-life datasets confirm the practical usability of the mining algorithms.

Couple Approach to Fixture Design Based on Maximum Common Subgraph Mining and Fixturing Performance Analysis

Couple Approach to Fixture Design Based on Maximum Common Subgraph Mining and Fixturing Performance Analysis