Towards Graph Summary and Aggregation: A Survey

You, Jane; Pan, Qiuping; Shi, Wei; Zhang, Zhipeng; Hu, Jianhua

doi:10.1007/978-3-642-41629-3_1

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…The work also presents an operation to allow users to drill down to a larger summary for more details and roll up to a concise summary with fewer details. A survey [24] discusses various summarization techniques for attributed graphs.…”

Section: Related Workmentioning

confidence: 99%

SsAG: Summarization and sparsification of Attributed Graphs

Ali¹,

Ahmad²,

Beg³

et al. 2021

Preprint

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The large sizes of real-world graphs in many domains make graph processing computationally prohibitive. A practical approach is to perform analytics on the compressed summary of the graph that retains the essential structure with bounded error. Many summarization methods have been proposed; however, these methods have three main shortcomings: i) mainly focused on minimizing the reconstruction error and often produce dense and memory extensive summaries ii) most of them do not scale to large graphs and iii) current techniques usually do not consider node attributes during summarization. Although, these attributes play an important role in drawing actionable analytics from real-world graphs. We devise a unified approach, SsAG, that is scalable, incorporates graph topology and node attributes for summarization and produces sparse summaries. We propose SsAG, a probabilistic approach to select nodes for merging by incorporating both the graph topology and node attribute information to make a summary. Furthermore, we present a sparsification approach to reduce the size of the summary with a negligible impact on reconstruction error. We also present analytical bounds on the runtime and provide an approximation guarantee on the quality of our solution. We compare SsAG with the state-of-the-art methods to show that SsAG is comparable in quality and more efficient and scalable. We further demonstrate the goodness of SsAG by accurately and efficiently answering the queries related to the graph structure and attribute information using the summary only.

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Section: Related Workmentioning

confidence: 99%

SsAG: Summarization and sparsification of Attributed Graphs

Ali¹,

Ahmad²,

Beg³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous work on surveying the graph summarization literature is scarce. You et al [2013] present some summarization algorithms for static graphs, focusing mostly on grouping-and compression-based methods. The tutorial by Lin et al [2013] provides more specific categorization and descriptions of ongoing work, but again only addresses static graph summarization.…”

Section: Differences From Prior Surveysmentioning

confidence: 99%

Graph Summarization Methods and Applications

et al. 2018

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While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Efficient computational methods for condensing and simplifying data are thus becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.

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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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Towards Graph Summary and Aggregation: A Survey

Cited by 5 publications

References 16 publications

SsAG: Summarization and sparsification of Attributed Graphs

SsAG: Summarization and sparsification of Attributed Graphs

Graph Summarization Methods and Applications

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

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