This proceedings contains the papers presented at the 2004 IFIP International Conference on Network and Parallel Computing (NPC 2004), held at Wuhan, China, from October 18 to 20, 2004. The goal of the conference was ...
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ISBN:
(数字)9783540301417
ISBN:
(纸本)9783540233886
This proceedings contains the papers presented at the 2004 IFIP International Conference on Network and Parallel Computing (NPC 2004), held at Wuhan, China, from October 18 to 20, 2004. The goal of the conference was to establish an international forum for engineers and scientists to present their ideas and experiences in network and parallel computing. A total of 338 submissions were received in response to the call for papers. These papers werefrom Australia, Brazil,Canada,China, Finland, France, G- many, Hong Kong, India, Iran, Italy, Japan, Korea, Luxemburg, Malaysia, N- way, Spain, Sweden, Taiwan, UK, and USA. Each submission was sent to at least three *** paper was judged according to its originality,inno- tion, readability, and relevance to the expected audience. Based on the reviews received, a total of 69 papers were accepted to be included in the proceedings. Among the 69 papers, 46 were accepted as full papers and were presented at the *** accepted23papersasshortpapers;eachofthesepaperswas given an opportunity to have a brief presentation at the conference, followed by discussions in a poster session. Thus, due to the limited scope and time of the conference and the high number of submissions received, only 20% of the total submissions were included in the ?nal program.
Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods us...
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Graph pattern mining is essential for deciphering complex networks. In the real world, graphs are dynamic and evolve over time, necessitating updates in mining patterns to reflect these changes. Traditional methods use fine-grained incremental computation to avoid full re-mining after each update, which improves speed but often overlooks potential gains from examining inter-update interactions holistically, thus missing out on overall efficiency *** this paper, we introduce Cheetah, a dynamic graph mining system that processes updates in a coarse-grained manner by leveraging exploration domains. These domains exploit the community structure of real-world graphs to uncover data reuse opportunities typically missed by existing approaches. Exploration domains, which encapsulate extensive portions of the graph relevant to updates, allow multiple updates to explore the same regions efficiently. Cheetah dynamically constructs these domains using a management module that identifies and maintains areas of redundancy as the graph changes. By grouping updates within these domains and employing a neighbor-centric expansion strategy, Cheetah minimizes redundant data accesses. Our evaluation of Cheetah across five real-world datasets shows it outperforms current leading systems by an average factor of 2.63 ×.
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