A Fast Method to Mine Frequent Subsequences from Graph Sequence Data

Inokuchi, Akira; Washio, Takashi

doi:10.1109/icdm.2008.106

Cited by 39 publications

(48 citation statements)

References 10 publications

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“…We have proposed TRs to represent graph sequences compactly under the assumption that "the change over successive graphs is gradual". In other words, only a small part of the graph changes between two successive graphs g ( j) and g ( j+1) in a graph sequence, while the other parts remain unchanged [13]. In the aforementioned human and gene networks, this assumption certainly holds, because most of the changes in vertices are progressive over successive steps.…”

Section: Representationmentioning

confidence: 99%

“…We have introduced six TRs as defined in Table 2 [13]. In summary, we give the following definition of a transformation sequence.…”

Section: Representationmentioning

confidence: 99%

“…Similarly, a gene network consisting of genes and their interactions produces a graph sequence in the course of its evolutionary history by acquiring new genes, deleting genes, and mutating genes. Recently, much attention has been paid to relevant frequent pattern mining * from graph sequences [13] (dynamic graphs [5] or evolving graphs [4]). Figure 1 (a) shows an example of a graph sequence containing 4 steps and 5 vertex IDs, denoted by the numbers attached to the vertices.…”

Section: Introductionmentioning

confidence: 99%

“…By assuming that the change in each graph is gradual, we can represent a graph sequence compactly even if the graph in the graph sequence has many vertices and edges. Based on this idea, Inokuchi and Washio proposed a method, called GTRACE (graph transformation sequence mining), for efficiently mining all frequent patterns, called relevant FTSs (frequent transformation subsequences), from ordered sequences of TRs [13]. In a similar manner to PrefixSpan [21], GTRACE first recursively mines FTSs, appending a TR to the tail of the mined FTS, and then removes irrelevant FTSs during post-processing.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Graph Sequence Mining Using Reverse Search

Inokuchi

Ikuta

Washio

2012

IEICE Trans. Inf. & Syst.

Self Cite

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SUMMARYThe mining of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences. A method, called GTRACE, has been proposed to mine frequent patterns from graph sequences under the assumption that changes in graphs are gradual. Although GTRACE mines the frequent patterns efficiently, it still needs substantial computation time to mine the patterns from graph sequences containing large graphs and long sequences. In this paper, we propose a new version of GTRACE that permits efficient mining of frequent patterns based on the principle of a reverse search. The underlying concept of the reverse search is a general scheme for designing efficient algorithms for hard enumeration problems. Our performance study shows that the proposed method is efficient and scalable for mining both long and large graph sequence patterns and is several orders of magnitude faster than the original GTRACE.

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Section: Representationmentioning

confidence: 99%

“…We have introduced six TRs as defined in Table 2 [13]. In summary, we give the following definition of a transformation sequence.…”

Section: Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient Graph Sequence Mining Using Reverse Search

Inokuchi

Ikuta

Washio

2012

IEICE Trans. Inf. & Syst.

Self Cite

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show abstract

“…They study changes of properties on each of the three levels under investigation. Inokuchi and Washio [2008] propose a fast method to mine frequent subsequences from graph sequence data defining a formalism to represent changes of subgraphs over time. However, the time in which the changes take place is not specified in the patterns.…”

Section: Mining Evolving Graphsmentioning

confidence: 99%

Social Network Analysis and Mining for Business Applications

Bonchi

Castillo

Gionis

et al. 2011

ACM Trans. Intell. Syst. Technol.

257

117

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Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored.In this article we use a business process classification framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.

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A survey of pattern mining in dynamic graphs

Fournier-Viger

Cheng

et al. 2020

WIREs Data Min & Knowl

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Graph data is found in numerous domains such as for the analysis of social networks, sensor networks, bioinformatics, industrial systems, and chemistry. Analyzing graphs to identify useful and interesting patterns is an important research area. It helps understanding graphs, and hence support decision making. Since two decades, many graph mining algorithms have been proposed to identify patterns such as frequent subgraphs, paths, cliques, and trees. But most of them assume that graphs are static. This simplifying assumption makes it easy to design algorithms but discard information about how graphs evolve. This article provides a detailed survey of techniques for mining interesting patterns in dynamic graphs, which can serve both as an introduction and as a guide to recent advances and opportunities in this research area. The main tasks related to mining patterns in dynamic graphs are reviewed such as discovering frequent subgraphs, evolution rules, motifs, subgraph sequences, recurrent and triggering patterns, and trend sequences. In addition, an overview of strategies and approaches to solve dynamic graph mining problems is presented, and their advantages and limitations are highlighted. Various extensions are also discussed such as to discover patterns in data streams and big data. Finally, the article mentions several research opportunities.

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A Fast Method to Mine Frequent Subsequences from Graph Sequence Data

Cited by 39 publications

References 10 publications

Efficient Graph Sequence Mining Using Reverse Search

Efficient Graph Sequence Mining Using Reverse Search

Social Network Analysis and Mining for Business Applications

A survey of pattern mining in dynamic graphs

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