Random graph modeling: A survey of the concepts

M Drobyshevskiy, D Turdakov - ACM computing surveys (CSUR), 2019 - dl.acm.org
Random graph (RG) models play a central role in complex networks analysis. They help us
to understand, control, and predict phenomena occurring, for instance, in social networks …

TurboGraph++ A scalable and fast graph analytics system

S Ko, WS Han - Proceedings of the 2018 international conference on …, 2018 - dl.acm.org
Existing distributed graph analytics systems are categorized into two main groups: those that
focus on efficiency with a risk of out-of-memory error and those that focus on scale-up with a …

Partitioner selection with ease to optimize distributed graph processing

N Merkel, R Mayer, TA Fakir… - 2023 IEEE 39th …, 2023 - ieeexplore.ieee.org
For distributed graph processing on massive graphs, a graph is partitioned into multiple
equally-sized parts which are distributed among machines in a compute cluster. In the last …

Mapreduce for graphs processing: New big data algorithm for 2-edge connected components and future ideas

D Dahiphale - IEEE Access, 2023 - ieeexplore.ieee.org
Finding connectivity in graphs has numerous applications, such as social network analysis,
data mining, intra-city or inter-cities connectivity, neural network, and many more. The …

Play like a vertex: A stackelberg game approach for streaming graph partitioning

Z Ding, Y Xiang, S Wang, X Xie, SK Zhou - Proceedings of the ACM on …, 2024 - dl.acm.org
In the realm of distributed systems tasked with managing and processing large-scale graph-
structured data, optimizing graph partitioning stands as a pivotal challenge. The primary …

EvoGraph: An effective and efficient graph upscaling method for preserving graph properties

H Park, MS Kim - Proceedings of the 24th ACM SIGKDD International …, 2018 - dl.acm.org
Nowadays, many researchers and industry groups often suffer from the lack of a variety of
large-scale real graphs. Although a lot of synthetic graph generation methods,(or models) …

MS-BioGraphs: Sequence similarity graph datasets

MK Esfahani, P Boldi, H Vandierendonck… - arXiv preprint arXiv …, 2023 - arxiv.org
Progress in High-Performance Computing in general, and High-Performance Graph
Processing in particular, is highly dependent on the availability of publicly-accessible …

Fastsgg: Efficient social graph generation using a degree distribution generation model

C Wang, B Wang, B Huang, S Song… - 2021 IEEE 37th …, 2021 - ieeexplore.ieee.org
With the popularity of social networks, large-scale social graphs are necessary to evaluate
the algorithms for various social network analysis tasks, especially in the era of big data. An …

Temporal Graph Generation Featuring Time-Bound Communities

S Zheng, C Wang, C Wu, Y Lou… - 2024 IEEE 40th …, 2024 - ieeexplore.ieee.org
Synthetic graph datasets are crucial for the assessment of network analysis algorithms,
providing a measure of their effectiveness and efficiency. However, most existing generation …

A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDDxs)

Z Feng, W Mayer, K He, S Kwashie, M Stumptner… - IEEE …, 2020 - ieeexplore.ieee.org
Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of
technologies, have now become a new form of knowledge representation, and a …