Optimizing Frequent Subgraph Mining for Single Large Graph

Dhiman, Aarzoo; Jain, Sarika

doi:10.1016/j.procs.2016.06.085

Cited by 10 publications

(4 citation statements)

References 12 publications

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“…There are well-established techniques being developed for transforming complex data structures into graph data embeddings, examples include obtaining samples of graphs independently, as observed sub-graphs/communities in a larger graph over time or as sub-graphs in a larger graph, see [34,35]. Alternatively, one can use graph construction methods such as graph lasso methods to construct graph valued data, see [36].…”

Section: Methodsmentioning

confidence: 99%

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Gudmundarson,

Peters

2024

PLoS ONE

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In this work we seek to enhance the frameworks practitioners in asset management and wealth management may adopt to asses how different screening rules may influence the diversification benefits of portfolios. The problem arises naturally in the area of Environmental, Social, and Governance (ESG) based investing practices as practitioners often need to select subsets of the total available assets based on some ESG screening rule. Once a screening rule is identified, one constructs a dynamic portfolio which is usually compared with another dynamic portfolio to check if it satisfies or outperforms the risk and return profile set by the company. Our study proposes a novel method that tackles the problem of comparing diversification benefits of portfolios constructed under different screening rules. Each screening rule produces a sequence of graphs, where the nodes are assets and edges are partial correlations. To compare the diversification benefits of screening rules, we propose to compare the obtained graph sequences. The method proposed is based on a machine learning hypothesis testing framework called the kernel two-sample test whose objective is to determine whether the graphs come from the same distribution. If they come from the same distribution, then the risk and return profiles should be the same. The fact that the sample data points are graphs means that one needs to use graph testing frameworks. The problem is natural for kernel two-sample testing as one can use so-called graph kernels to work with samples of graphs. The null hypothesis of the two-sample graph kernel test is that the graph sequences were generated from the same distribution, while the alternative is that the distributions are different. A failure to reject the null hypothesis would indicate that ESG screening does not affect diversification while rejection would indicate that ESG screening does have an effect. The article describes the graph kernel two-sample testing framework, and further provides a brief overview of different graph kernels. We then demonstrate the power of the graph two-sample testing framework under different realistic scenarios. Finally, the proposed methodology is applied to data within the SnP500 to demonstrate the workflow one can use in asset management to test for structural differences in diversification of portfolios under different ESG screening rules.

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Section: Methodsmentioning

confidence: 99%

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Gudmundarson,

Peters

2024

PLoS ONE

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Section: Related Workmentioning

confidence: 99%

“…There are some algorithms which calculate the support for all mined subgraphs, such as gSpan [29], GraMi [10], O-FSM [7], ScaleMine [1], and SSIGRAM [24]. In isomorphism solving, there are several difficulties [7], because a lot of different appearances of a subgraph (isomorphisms) can overlap [10,12]. A process called graph compression used by SumISO [21] is to group vertices into super vertices, SumISO only searches isomorphisms on these compressed representations of the graphs.…”

Section: Related Workmentioning

confidence: 99%

CCGraMi: An Effective Method for Mining Frequent Subgraphs in a Single Large Graph

Nguyen

Zelinka

Diep

2021

mendel

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In modern applications, large graphs are usually applied in the simulation and analysis of large complex systems such as social networks, computer networks, maps, traffic networks. Therefore, graph mining is also an interesting subject attracting many researchers. Among them, frequent subgraph mining in a single large graph is one of the most important branches of graph mining, it is defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. In which, the GraMi algorithm is considered the state of the art approach and many algorithms have been proposed to improve this algorithm. In 2020, the SoGraMi algorithm was proposed to optimize the GraMi algorithm and presented an outstanding performance in terms of runtime and storage space. In this paper, we propose a new algorithm to improve SoGraMi based on connected components, called CCGraMi (Connected Components GraMi). Our experiments on four real datasets (both directed and undirected) show that the proposed algorithm outperforms SoGraMi in terms of running time as well as memory requirements.

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“…In addition, the real-life data set has been used to approximate the results [17].A more robust and distributed method to mine a massive graph is crucial. So, DistGraph algorithms solve the false negative problem in mining graphs to minimize communication in computational nodes [18]. Specifically, frequent subgraph mining considers small graphs in large graphs.…”

Section: Related Workmentioning

confidence: 99%

An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs

Sahu*,

Chawla,

Khare

2020

IJITEE

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Pattern Mining is the key mechanism to manage large scale data element. Frequent subgraph mining (FSM) considers isomorphism which is a subprocess of pattern mining is a well-studied problem in the data mining. Graphs are considered as a standard structure in many domains such as protein-protein interaction network in biological networks, wired or wireless interconnection networks, web data, etc. FSM is the task of finding all frequent subgraphs from a given database i.e. a single big graph or database of many graphs, whose support is greater than the given threshold value. Many databases consider small graphs for solving complex problems. The classification of graph depends upon the application requirement. A good mining architecture may prevent a lot of memory and time. This paper follows the Grami structure for the analysis of frequent subgraph mining and also introduces the 20% threshold policy for the enhancement of the directed pattern graphs. The constraint satisfaction problem (CSP) has been discussed and analyzed using the Grami approach. The proposed model is compared to Grami on twitter dataset based on the evaluation of time and memory consumed. The proposed algorithm shows an improvement of 3-4 % for both the parameters. The results show that the performance of Grami approach has been improved which shows a 6.6% reduction in time and 21% improvement in memory consumption using the proposed approach.

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Optimizing Frequent Subgraph Mining for Single Large Graph

Cited by 10 publications

References 12 publications

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

Assessing portfolio diversification via two-sample graph kernel inference. A case study on the influence of ESG screening

CCGraMi: An Effective Method for Mining Frequent Subgraphs in a Single Large Graph

An Enhancement of Grami Based on Threshold Policy for Pattern Big Graphs

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