Summarisation of weighted networks

Fang, Zhou; Qu, Qiang; Toivonen, Hannu

doi:10.1080/0952813x.2017.1280089

Cited by 15 publications

(7 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The utility of the ChainSQL is evident from its business usecases in domains including finance and supplychain and therefore, it offers promising application scenarios for future. Future research on spatial data, dynamics, data analytics, sharding, and verification may be conducted based on the system [21,22,23,24,25,26,27,28,29,30,31].…”

Section: Discussionmentioning

confidence: 99%

A Blockchain Database Application Platform

Muzammal,

Qu,

Nasrulin

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

A blockchain is a decentralised linked data structure that is characterised by its inherent resistance to data modification, but it is deficient in search queries, primarily due to its inferior data formatting. A distributed database is also a decentralised data structure which features quick query processing and well-designed data formatting but suffers from data reliability. In this demonstration, we showcase a blockchain database application platform developed by integrating the blockchain with the database, i.e. we demonstrate a system that has the decentralised, distributed and audibility features of the blockchain and quick query processing and well-designed data structure of the distributed databases. The system features a tamper-resistant, consistent and cost-effective multi-active database and an effective and reliable data-level disaster recovery backup. The system is demonstrated in practice as a multi-active database along with the data-level disaster recovery backup feature.

show abstract

Section: Discussionmentioning

confidence: 99%

A Blockchain Database Application Platform

Muzammal,

Qu,

Nasrulin

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Compression of edge weighted graphs using locality sensitive hashing while preserving the edge weights is described [25]. Furthermore, compression of node and edge weighted graphs is described such that the weights on the path between two nodes in the summary graph should be similar to that in the original graph [26]. The paper also aims to preserve more information related to nodes with high weights.…”

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

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.

show abstract

“…A parameter ϵ bounds the error (details are algorithm-specific). We exhaustively analyzed existing schemes [14,40,47,61,67,93,103,104,115,126,130,141,[152][153][154]168]. We focus on SWeG, a recent scheme [141] that constructs supervertices with a generalized Jaccard similarity.…”

Section: Spanners With Slimmentioning

confidence: 99%

Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics

Besta,

Weber,

Gianinazzi

et al. 2019

Preprint

View full text Add to dashboard Cite

We propose Slim Graph: the first programming model and framework for practical lossy graph compression that facilitates high-performance approximate graph processing, storage, and analytics. Slim Graph enables the developer to express numerous compression schemes using small and programmable compression kernels that can access and modify local parts of input graphs. Such kernels are executed in parallel by the underlying engine, isolating developers from complexities of parallel programming. Our kernels implement novel graph compression schemes that preserve numerous graph properties, for example connected components, minimum spanning trees, or graph spectra. Finally, Slim Graph uses statistical divergences and other metrics to analyze the accuracy of lossy graph compression. We illustrate both theoretically and empirically that Slim Graph accelerates numerous graph algorithms, reduces storage used by graph datasets, and ensures high accuracy of results. Slim Graph may become the common ground for developing, executing, and analyzing emerging lossy graph compression schemes.

show abstract

Summarisation of weighted networks

Cited by 15 publications

References 54 publications

A Blockchain Database Application Platform

A Blockchain Database Application Platform

SsAG: Summarization and sparsification of Attributed Graphs

Slim Graph: Practical Lossy Graph Compression for Approximate Graph Processing, Storage, and Analytics

Contact Info

Product

Resources

About