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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ÇÐȸÁö > µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

µ¥ÀÌÅͺ£À̽º ¿¬±¸È¸Áö(SIGDB)

Current Result Document : 1 / 8   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ´ë¿ë·® ±×·¡ÇÁ¿¡¼­ È¿À²ÀûÀÎ µ¿Àû ±×·¡ÇÁ ¿ä¾à ±â¹ý
¿µ¹®Á¦¸ñ(English Title) An Efficient Dynamic Graph Summarization in Massive Graph
ÀúÀÚ(Author) ¼­È£Áø   ±èÇö¿í   ¹Ú±â¼º   ÀÌ¿µ±¸   HoJin Seo   Hyunwook Kim   Kisung Park   Young-Koo Le  
¿ø¹®¼ö·Ïó(Citation) VOL 32 NO. 01 PP. 0002 ~ 0012 (2016. 04)
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(Korean Abstract)
±×·¡ÇÁ ¿ä¾àÀº ´ë¿ë·® ±×·¡ÇÁ¿¡¼­ ¹ÐÁýµµ°¡ ³ôÀº ºÎºÐ±×·¡ÇÁ¸¦ ¾ÐÃà Ç¥ÇöÇÏ´Â ±â¹ýÀÌ´Ù. ÀÌ·¯ÇÑ ±×·¡ÇÁ ¿ä¾àÀº ¿¬°á¼º, Á¤µµ ºñ´ëĪ¼º µî°ú °°Àº ±×·¡ÇÁ°¡ °®´Â °íÀ¯ÇÑ ±¸Á¶Àû Á¤º¸¸¦ ºÐ¼®Çϱâ À§ÇØ »ç¿ëµÈ´Ù. ±âÁ¸ ±×·¡ÇÁ ¿ä¾à ±â¹ýÀº ±×·¡ÇÁ¸¦ Â÷¼ö°¡ ³ôÀº Çãºê Á¤Á¡À» ±âÁØÀ¸·Î ´Ù¼öÀÇ ºÎºÐ ±×·¡ÇÁµé·Î ¹Ýº¹ÀûÀ¸·Î ºÐÇÒÇÏ°í, ºÐÇÒµÈ ºÎºÐ ±×·¡ÇÁµéÀ» °¢°¢ ¿ä¾àÇÏ¿´´Ù. ±×·¯³ª µ¿Àû ȯ°æÀ» °í·ÁÇÏÁö ¾Ê¾Æ ±×·¡ÇÁ°¡ º¯°æµÉ ¶§¸¶´Ù Àüü ±×·¡ÇÁ¿¡ ´ëÇØ ´Ù½Ã ¿ä¾à ±¸Á¶¸¦ Ž»öÇؾßÇϱ⠶§¹®¿¡ ¸Å¿ì ¿À·£ ¼öÇà½Ã°£À» °®°Ô µÈ´Ù. º» ³í¹®Àº ´ë¿ë·® ±×·¡ÇÁ¿¡¼­ È¿À²ÀûÀÎ µ¿Àû ±×·¡ÇÁ ¿ä¾à ±â¹ýÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ±â¹ýÀº Çãºê Èĺ¸ ÁýÇÕ°ú ÀÌÀü ÁÖ±âÀÇ ºÐÇÒµÈ ºÎºÐ ±×·¡ÇÁ¸¦ ÀÌ¿ëÇÏ¿© Àüü ±×·¡ÇÁ¿¡ ´ëÇÑ ÀçºÐÇÒ ¾øÀÌ È¿À²ÀûÀ¸·Î ÀÌÀü ÁÖ±âÀÇ ¿ä¾à ±¸Á¶¸¦ °»½ÅÇÑ´Ù. ½ÇÇèÀ» ÅëÇÏ¿© Á¦¾ÈÇÏ´Â ±â¹ýÀÌ ±âÁ¸ÀÇ ±â¹ýº¸´Ù ÃÖ´ë 57% ¼öÇà½Ã°£ÀÌ Çâ»óµÊÀ» º¸ÀδÙ.
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(English Abstract)
Graph summarization is a technique for compressing highly dense subgraphs in a massive graph. This technique can be utilized for analyzing the topological characteristics of graphs such as connectivity and skewness of graphs. The state-of-the-art graph summarization technique incrementally divides a large graph into subgraphs based on the hub nodes having higher degrees, and then summarizes each of the divided subgraphs. However, This summarization technique does not consider the frequent changes in a dynamic graph. Therefore, this technique requires a very long execution time since it must summarize the whole graph again whenever the graph changes. In this paper, we propose an efficient dynamic graph summarization technique for a massive graph. The proposed technique updates only the previously summarized subgraphs which are the subjects to be changed. Our technique can identify those summarized subgraphs using the set of hub node candidates without considering the whole graph. In our experiments, we observe that the proposed summarization technique can reduce the runtime by up to 57% compared to the state-of-the-art graph summarization technique.
Å°¿öµå(Keyword) ±×·¡ÇÁ ¿ä¾à   ±×·¡ÇÁ ¸¶ÀÌ´×   µ¿Àû ±×·¡ÇÁ   Graph Summarization   Graph Mining   Dynamic Graph  
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