Synthetic Graph Generation for Systematic Exploration of Graph Structural Properties

Verstraaten, Merijn; Vărbănescu, Ana Lucia; Laat, Cees de

doi:10.1007/978-3-319-58943-5_45

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…2. An overview of the optimization process, where on the left we can see the distributions for the baseline dataset, on the middle the parameters that are optimized, and on the right the distributions for the result dataset specific characteristics as in [11], where graphs are generated using evolutionary computing. In contrast, to the best of our knowledge, the generation of a graph dataset that covers the metric space evenly has not been explored.…”

Section: Introductionmentioning

confidence: 99%

Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

Wassington¹,

Abadal²

2022

Preprint

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In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. Additionally, graphs have been used to model a wide variety of problems. Although synthetic graphs can be used to augment available real graph datasets to overcome selection bias, the generation of unbiased synthetic datasets is complex with current tools. In this work, we propose a method to find a synthetic graph dataset that has an even representation of graphs with different metrics. The resulting dataset can then be used, among others, for benchmarking graph processing techniques as the accuracy of different Graph Neural Network (GNN) models or the speedups obtained by different graph processing acceleration frameworks.

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Section: Introductionmentioning

confidence: 99%

Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

Wassington¹,

Abadal²

2022

Preprint

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“…In order to capture or account for universal properties observed in practical networks, a lot of mechanisms, approaches, and models were developed in the community of network science [1]. Currently, there are many important graph generation literature [17], [18], [19], graph generators [20], as well as packages [21], for example, NetwrokX 1 .…”

Section: Introductionmentioning

confidence: 99%

Edge corona product as an approach to modeling complex simplical networks

Wang,

Yi,

et al. 2020

Preprint

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Many graph products have been applied to generate complex networks with striking properties observed in real-world systems. In this paper, we propose a simple generative model for simplicial networks by iteratively using edge corona product. We present a comprehensive analysis of the structural properties of the network model, including degree distribution, diameter, clustering coefficient, as well as distribution of clique sizes, obtaining explicit expressions for these relevant quantities, which agree with the behaviors found in diverse real networks. Moreover, we obtain exact expressions for all the eigenvalues and their associated multiplicities of the normalized Laplacian matrix, based on which we derive explicit formulas for mixing time, mean hitting time and the number of spanning trees. Thus, as previous models generated by other graph products, our model is also an exactly solvable one, whose structural properties can be analytically treated. More interestingly, the expressions for the spectra of our model are also exactly determined, which is sharp contrast to previous models whose spectra can only be given recursively at most. This advantage makes our model a good test-bed and an ideal substrate network for studying dynamical processes, especially those closely related to the spectra of normalized Laplacian matrix, in order to uncover the influences of simplicial structure on these processes.

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“…The synthetic graphs, although more predictable and controllable, still do not cover all properties of interest, and results cannot be generalised to other types of graphs. We attempted to generate graphs with the desired properties ourselves, but were unable to scale the generation to graphs of sufficient size to do benchmarking [30].…”

Section: Introductionmentioning

confidence: 99%

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

Verstraaten¹,

Vărbănescu²,

Laat³

2017

Preprint

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While it is well-known and acknowledged that the performance of graph algorithms is heavily dependent on the input data, there has been surprisingly little research to quantify and predict the impact the graph structure has on performance. Parallel graph algorithms, running on many-core systems such as GPUs, are no exception: most research has focused on how to efficiently implement and tune different graph operations on a specific GPU. However, the performance impact of the input graph has only been taken into account indirectly as a result of the graphs used to benchmark the system.In this work, we present a case study investigating how to use the properties of the input graph to improve the performance of the breadth-first search (BFS) graph traversal.To do so, we first study the performance variation of 15 different BFS implementations across 248 graphs. Using this performance data, we show that significant speed-up can be achieved by combining the best implementation for each level of the traversal. To make use of this data-dependent optimization, we must correctly predict the relative performance of algorithms per graph level, and enable dynamic switching to the optimal algorithm for each level at runtime.We use the collected performance data to train a binary decision tree, to enable high-accuracy predictions and fast switching. We demonstrate empirically that our decision tree is both fast enough to allow dynamic switching between implementations, without noticeable overhead, and accurate enough in its prediction to enable significant BFS speedup. We conclude that our model-driven approach (1) enables BFS to outperform state of the art GPU algorithms, and (2) can be adapted for other BFS variants, other algorithms, or more specific datasets.

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Synthetic Graph Generation for Systematic Exploration of Graph Structural Properties

Cited by 4 publications

References 20 publications

Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

Bias Reduction via Cooperative Bargaining in Synthetic Graph Dataset Generation

Edge corona product as an approach to modeling complex simplical networks

Using Graph Properties to Speed-up GPU-based Graph Traversal: A Model-driven Approach

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