A compact representation of graph databases

Álvarez, Sandra González; Brisaboa, Nieves R.; Ladra, Susana; Pedreira, Óscar

doi:10.1145/1830252.1830255

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…Therefore, based on a directed attributed multigraph model [4], we propose an affiliated-party interest-related network (APIRN) for characterizing APs, IRRs and their properties for ATTE identification. The APIRN model is described as follows:…”

Section: Definitions Abbreviations and Apirn Modelmentioning

confidence: 99%

“…(2) We find that the properties of affiliated parties (APs) and interestrelated relationships (interactive relationships between APs, which are denot-ed as IRRs) are essential for detecting ATTE. Thus, we generate an APIRN that is based on a directed attributed multi-graph-based model [4] for characterizing APs, IRRs and their properties via heterogeneous network fusion.…”

Section: Introductionmentioning

confidence: 99%

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Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Ruan

Yan

Dong

et al. 2019

Information Sciences

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Affiliated-transaction-based tax evasion (ATTE) is a new strategy in tax evasion that is carried out via legal-like transactions between a group of companies that have heterogeneous, complex and covert interactive relationships to evade taxes. Existing studies cannot effectively detect ATTE behaviors since (i) they perform well only for determining the abnormal financial status of individuals and ineffectively address the interactive relationships among companies, (ii) they aim at detecting ATTE from the perspective of structural characteristics, which leads to a poor false-positive rate, and (iii) few of them perform well in most sectors of companies. Effectively detecting suspicious groups according to both structural characteristics of ATTE groups and business characteristics of ATTE means (BC-ATTEM) remains an open issue. In this paper, we propose an affiliated-parties interest-related network (APIRN) for modeling affiliated parties, interest-related relationships, and their properties for identifying ATTE. Then, we identify the behavioral patterns of ATTE via topological pattern abstraction from APIRN and the-

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Section: Definitions Abbreviations and Apirn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Ruan

Yan

Dong

et al. 2019

Information Sciences

View full text Add to dashboard Cite

show abstract

“…They focus on two-way regular-path queries (2RPQs) as these queries can express navigating graphs with paths defined by regular expressions. Alvarez et al [18] present a new model and representation of general graph databases, where nodes and edges are typed, labeled, and possibly attributed, and graphs may be multigraphs. They also discuss efficient implementation of graph navigation operations.…”

Section: K 2 Tree-based Schemesmentioning

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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“…This topic is approached in a wide spectrum of areas such as succinct data structures [8] where the most memory efficient data structures are considered. The second approach deals with graph compression in specific domains such as graph databases [1], web graphs [2], hierarchical schemes [7] and many others. These compression techniques mostly exploit domain specific knowledge and in many cases established compression techniques are utilized from e.g.…”

Section: Introductionmentioning

confidence: 99%

Graph automorphisms for compression

Cibej

Mihelič

2020

Open Computer Science

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Detecting automorphisms is a natural way to identify redundant information presented in structured data. When such redundancies are detected they can be used for data compression. In this paper we explore two different classes of graphs to capture this intuitive property of automorphisms. Symmetry-compressible graphs are the first class which introduces the basic concepts but use only global symmetries for the compression. In order for this concept to be more practical, we need to use local symmetries. Thus, we extend the basic graph class with Near Symmetry compressible graphs. Furthermore, we develop two algorithms that can be used to compress practical instances and empirically evaluate them on a set of realistic graphs.

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A compact representation of graph databases

Cited by 11 publications

References 9 publications

Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

Identifying suspicious groups of affiliated-transaction-based tax evasion in big data

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

Graph automorphisms for compression

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