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 …
to understand, control, and predict phenomena occurring, for instance, in social networks …
TurboGraph++ A scalable and fast graph analytics system
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 …
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
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 …
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 …
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
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 …
structured data, optimizing graph partitioning stands as a pivotal challenge. The primary …
EvoGraph: An effective and efficient graph upscaling method for preserving graph properties
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) …
large-scale real graphs. Although a lot of synthetic graph generation methods,(or models) …
MS-BioGraphs: Sequence similarity graph datasets
Progress in High-Performance Computing in general, and High-Performance Graph
Processing in particular, is highly dependent on the availability of publicly-accessible …
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 …
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 …
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)
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 …
technologies, have now become a new form of knowledge representation, and a …