Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. Hover, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. ,  study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works ll in many b-scale graphs.  propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates,  show that our algorithm can be naturally extended to handle edge deletion.  also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties.  conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle b-scale graphs. As an example,  are the first to handle a b graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory.
['Dong Wen', 'Lu Qin', 'Ying Zhang', 'Xuemin Lin', 'Jeffrey Xu Yu']
I am writing a research paper on a idea I/O efficient Core Graph Decomposition at web scale
[{"from": "human", "value": "I am writing a research paper on a idea I/O efficient Core Graph Decomposition at web scale"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Core decomposition is a fundamental graph problem with a large number of applications. Most existing approaches for core decomposition assume that the graph is kept in memory of a machine. Nevertheless, many real-world graphs are big and may not reside in memory. In the literature, there is only one work for I/O efficient core decomposition that avoids loading the whole graph in memory. Hover, this approach is not scalable to handle big graphs because it cannot bound the memory size and may load most parts of the graph in memory. In addition, this approach can hardly handle graph updates. ,  study I/O efficient core decomposition following a semi-external model, which only allows node information to be loaded in memory. This model works ll in many b-scale graphs.  propose a semi-external algorithm and two optimized algorithms for I/O efficient core decomposition using very simple structures and data access model. To handle dynamic graph updates,  show that our algorithm can be naturally extended to handle edge deletion.  also propose an I/O efficient core maintenance algorithm to handle edge insertion, and an improved algorithm to further reduce I/O and CPU cost by investigating some new graph properties.  conduct extensive experiments on 12 real large graphs. Our optimal algorithm significantly outperform the existing I/O efficient algorithm in terms of both processing time and memory consumption. In many memory-resident graphs, our algorithms for both core decomposition and maintenance can even outperform the in-memory algorithm due to the simple structures and data access model used. Our algorithms are very scalable to handle b-scale graphs. As an example,  are the first to handle a b graph with 978.5 million nodes and 42.6 billion edges using less than 4.2 GB memory."}]