Network Compression by Node and Edge Mergers

Toivonen, Hannu; Fang, Zhou; Hartikainen, Aleksi; Hinkka, Atte

doi:10.1007/978-3-642-31830-6_14

Cited by 9 publications

(5 citation statements)

References 18 publications

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“…Those in the first class are sampling methods in which a simplified network is represented by a random sample of the original network (e.g., random node selection [28], random link selection [29], snowball sampling [30], random walk sampling [9] and forest fire [9]). Methods in the second class obtain simplified networks by merging nodes and links into supernodes and superlinks based on different characteristics, such as the distance between nodes (e.g., cluster-growing and box-tiling renormalization [31]), node and link attributes (e.g., link weights [32] and node attributes [33]) or community structure (e.g., balanced propagation and modularity optimization [34]).…”

Section: Simplification Methodsmentioning

confidence: 99%

Assessing the effectiveness of real-world network simplification

Blagus¹,

Šubelj²,

Bajec³

2014

Physica A: Statistical Mechanics and its Applications

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Many real-world networks are large, complex and thus hard to understand, analyze or visualize. Data about networks are not always complete, their structure may be hidden, or they may change quickly over time. Therefore, understanding how an incomplete system differs from a complete one is crucial. In this paper, we study the changes in networks submitted to simplification processes (i.e., reduction in size). We simplify 30 real-world networks using six simplification methods and analyze the similarity between the original and simplified networks based on the preservation of several properties, for example, degree distribution, clustering coefficient, betweenness centrality, density and degree mixing. We propose an approach for assessing the effectiveness of the simplification process to define the most appropriate size of simplified networks and to determine the method that preserves the most properties of original networks. The results reveal that the type and size of original networks do not affect the changes in the networks when submitted to simplification, whereas the size of simplified networks does. Moreover, we investigate the performance of simplification methods when the size of simplified networks is 10% that of the original networks. The findings show that sampling methods outperform merging ones, particularly random node selection based on degree and breadth-first sampling.

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Section: Simplification Methodsmentioning

confidence: 99%

Assessing the effectiveness of real-world network simplification

Blagus¹,

Šubelj²,

Bajec³

2014

Physica A: Statistical Mechanics and its Applications

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“…First, Network sampling means obtaining the main network structure through sampling representative nodes or edges, which is extensively overviewed in the study of Ahmed, Neville, and Kompella (2014). Second, to turn the original network into a smaller skeleton, coarse graining means merging nodes or edges according to certain rules (Itzkovitz et al, 2004), such as the attribute of node or edge (Toivonen et al, 2012), community structure (Blagus et al, 2012). The most common one is the filter-based method.…”

Section: Literature Reviewmentioning

confidence: 99%

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Xing

Han

2021

Journal of Data and Information Science

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Purpose With the availability and utilization of Inter-Country Input-Output (ICIO) tables, it is possible to construct quantitative indices to assess its impact on the Global Value Chain (GVC). For the sake of visualization, ICIO networks with tremendous low- weight edges are too dense to show the substantial structure. These redundant edges, inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis (SNA). In this case, we need a method to filter such edges and obtain a sparser network with only the meaningful connections. Design/methodology/approach In this paper, we propose two parameterless pruning algorithms from the global and local perspectives respectively, then the performance of them is examined using the ICIO table from different databases. Findings The Searching Paths (SP) method extracts the strongest association paths from the global perspective, while Filtering Edges (FE) method captures the key links according to the local weight ratio. The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks. Research limitations There are still two limitations in this research. One is that the computational complexity may increase rapidly while processing the large-scale networks, so the proposed method should be further improved. The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology. Practical implications The network pruning methods we proposed will promote the analysis of the ICIO network, in terms of community detection, link prediction, and spatial econometrics, etc. Also, they can be applied to many other complex networks with similar characteristics. Originality/value This paper improves the existing research from two aspects, namely, considering the heterogeneity of weights and avoiding the interference of parameters. Therefore, it provides a new idea for the research of network backbone extraction.

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“…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

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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.

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Network Compression by Node and Edge Mergers

Cited by 9 publications

References 18 publications

Assessing the effectiveness of real-world network simplification

Assessing the effectiveness of real-world network simplification

Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

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

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