Your English writing platform
Discover LudwigSuggestions(2)
Exact(2)
Local computations are good for big graphs.
Hence this becomes an ideal choice for big graphs where these approximate MCS solutions can further be extended via McGregor algorithm to find MCS.
Similar(58)
These schemes are the bases and prerequisites for big graph partitioning.
Graph clustering is a computationally challenging and difficult task, especially for big graph data.
For big graph data, the problem becomes more challenging or even intractable.
Notice that the variables J and K have upper bounds and (bar{n}_{c}) is determined by the graph structure, the algorithm has a complexity of O(n) for big graph data.
GraphChi is a disk-based system for the analysis of big graphs on single off-the-shelf computers [ 16].
Aiming to address the cliques discovery from big graph, our recent work [66] adopted the formal concept analysis techniques and proposed a novel framework, called "cSketch" for identifying the cliques from big graphs.
We then present a theory of bounded evaluation and a resource-bounded framework for querying big graphs in Sect.
The framework can also incorporate other techniques for querying big graphs, by making big graphs small, including but not limited to the following.
As remarked earlier, when the data are dirty, query answers computed in the data may not be correct and may even do more harm than good, no matter how efficient and scalable our systems and algorithms are for querying big graphs.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com