Summarizing scale-free networks based on virtual and real links

Bei, Yijun; Lin, Zhen; Chen, Deren

doi:10.1016/j.physa.2015.08.048

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…In [11] another method has been proposed to hybrid summarization of a graph. In this method, initially a graph is summarized based on vertex attributes, without take into consideration the graph structure, and then by moving nodes between super-nodes adjust the summary to the graph structure.…”

Section: Hybrid Summarizationmentioning

confidence: 99%

“…For this example, the relationship between sold products are received as a stream of edges, an edge represents each pair of sold products. Although a number of algorithms have been proposed for summarizing stationery attributed graphs based on both the structure and vertex attributes with varying contributions of each [6,8,11], to the best of our knowledge there is no method capable of summarizing a graph stream based on both the structure and vertex attributes or both. This is the main challenge of graph stream summarization.…”

Section: Introductionmentioning

confidence: 99%

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A new method for graph stream summarization based on both the structure and concepts

2019

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Graph datasets are common in many application domains and for which their graphs are usually massive. One solution to process such massive graphs is summarization. There are two kinds of graphs, stationary and stream. For stationary graphs, a number of summarization algorithms are available while for graph stream there is no a comprehensive summarization method that summarizes a graph stream based on the structure, vertex attributes or both with varying contributions. This is because of challenges of graph stream, which are volume of data and changing of data over time. In this paper, we propose a method based on sliding-window model for which summarizes a graph stream based on a combination of the structure and vertex attributes. We proposed a new structure for summary graphs and also proposed new methods for comparing two summary graphs. To the best of our knowledge, this is the first method that summarizes a graph stream based on both the structure and vertex attributes with varying contributions. Through extensive experiments on real dataset of Amazon co-purchasing products, we have demonstrated the performance of the proposed method.

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Section: Hybrid Summarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for graph stream summarization based on both the structure and concepts

2019

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“…The most important connection to graph compression is that in many of these schemes, the process of graph summarization also leads to size reduction. For example, vertices within a cluster are grouped to form a supervertex, and edges are merged into superedges [46,275,276,287,305,306,360,388,429,449,453,461,464], similarly to many hierarchical schemes in web graphs ( § 4.1.5). Some works use or discuss bisimulation, especially in the domain of RDF graphs [93,101].…”

Section: Lossless Summarization Of Graphsmentioning

confidence: 99%

Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

Besta,

Hoefler

2018

Preprint

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Various graphs such as web or social networks may contain up to trillions of edges. Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses and the pressure on the memory subsystem. Yet, selecting a proper compression method is challenging as there exist a plethora of techniques, algorithms, domains, and approaches in compressing graphs. To facilitate this, we present a survey and taxonomy on lossless graph compression that is the first, to the best of our knowledge, to exhaustively analyze this domain. Moreover, our survey does not only categorize existing schemes, but also explains key ideas, discusses formal underpinning in selected works, and describes the space of the existing compression schemes using three dimensions: areas of research (e.g., compressing web graphs), techniques (e.g., gap encoding), and features (e.g., whether or not a given scheme targets dynamic graphs). Our survey can be used as a guide to select the best lossless compression scheme in a given setting.

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“…While there are techniques available in literature that can reduce or summarise attributed graphs to reduce their complexity [11][12][13], these techniques cannot be applied to the type of attributed graphs used by the graph-based FDI methods, verbatim. It is, therefore, warranted to develop reduction techniques that can effectively reduce the size and complexity of the attributed graphs relevant to FDI.…”

Section: Introductionmentioning

confidence: 99%

Graph Complexity Reduction of Exergy-Based FDI—A Tennessee Eastman Process Case Study

Styger,

Uren,

van Schoor

2023

Energies

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When applying graph-based fault detection and isolation (FDI) methods to the attributed graph data of large and complex industrial processes, the computational abilities and speed of these methods are adversely affected by the increased complexity. This paper proposes and evaluates five reduction techniques for the exergy-graph-based FDI method. Unlike the graph reduction techniques available in literature, the reduction techniques proposed in this paper can easily be applied to the type of attributed graph used by graph-based FDI methods. The attributed graph data of the Tennessee Eastman process are used in this paper since it is a popular process to use for the evaluation of fault diagnostic methods and is both large and complex. To evaluate the proposed reduction techniques, three FDI methods are applied to the original attributed graph data of the process and the performance of these FDI methods used as control data. Each proposed reduction technique is applied to the attributed graph data of the process, after which all three FDI methods are applied to the reduced graph data to evaluate their performance. The FDI performance obtained with reduced graph data is compared to the FDI performance using the control data. This paper shows that, using the proposed graph reduction techniques, it is possible to significantly reduce the size and complexity of the attributed graph of a system while maintaining a level of FDI performance similar to that achieved prior to any graph reduction.

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Summarizing scale-free networks based on virtual and real links

Cited by 6 publications

References 23 publications

A new method for graph stream summarization based on both the structure and concepts

A new method for graph stream summarization based on both the structure and concepts

Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

Graph Complexity Reduction of Exergy-Based FDI—A Tennessee Eastman Process Case Study

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