Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
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ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Community Detection Using Link Attribute-Based Classification |
ÀúÀÚ(Author) |
±èÁ¤¼±
Á¤¼öȯ
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Jeongseon Kim
Soohwan Jeong
Sungsu Lim
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 48 NO. 08 PP. 0959 ~ 0965 (2021. 08) |
Çѱ۳»¿ë (Korean Abstract) |
ºü¸£°í º¹ÀâÇÏ°Ô ÁøÈÇÏ´Â ¼¼»óÀ» ÀÌÇØÇϱâ À§ÇÏ¿© µ¥ÀÌÅ͸¦ ÅëÇØ Áö½ÄÀ» ¹ß°ßÇÏ´Â ½Ãµµ´Â Á¡Â÷ ´Ù¾çȵǰí ÀÖ´Ù. °³Ã¼µéÀÌ °ü°è¸¦ °®°í ¾ôÇôÀÖ´Â µ¥ÀÌÅ͸¦ ±×·¡ÇÁ·Î ¸ðµ¨¸µÇÏ°í ºÐ¼®ÇÏ´Â ±×·¡ÇÁ µ¥ÀÌÅÍ ºÐ¼®Àº ÃֽŠ±â°èÇнÀ ±â¹ý°ú Á¢¸ñµÇ¸é¼ ¸¹Àº °ü½ÉÀ» ²ø°í ÀÖ´Ù. º» ³í¹®¿¡¼´Â ±×·¡ÇÁ Ä¿¹Â´ÏƼ ±¸Á¶¸¦ ¹ß°ßÇϱâ À§ÇÑ »õ·Î¿î ¹æ¹ý·ÐÀ» Á¦¾ÈÇÑ´Ù. Ä¿¹Â´ÏƼ ³»ºÎ ¹× ¿ÜºÎ¿¡ Á¸ÀçÇÏ´Â ¸µÅ©µéÀÌ ´Ù¸¥ ¼Ó¼º°ªÀ» °®µµ·Ï ÇÏ´Â À¯»çµµ, °î·ü ±â¹Ý ¼Ó¼ºµé¿¡ ´ëÇØ ºÐ¼®ÇÏ°í, À̸¦ È°¿ëÇÏ¿© Ä¿¹Â´ÏƼ ±¸Á¶¿¡ ¿µÇâÀ» ´ú ³¢Ä¡´Â ¸µÅ©¸¦ Á¦°ÅÇÏ¿© ´õ Èñ¼ÒÇÑ ±×·¡ÇÁ¿¡¼ ´õ Çâ»óµÈ Ä¿¹Â´ÏƼ ±¸Á¶¸¦ ã¾Æ³»´Â ¾Ë°í¸®ÁòÀ» ¼³°è ¹× ºÐ¼®ÇÑ´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Attempts to discover knowledge through data are becoming gradually diversified to understand a fast and complex world. Graph data analysis, which models and analyzes correlated data as graphs, is drawing much attention as it is combined with the latest machine learning techniques. In this work, we propose a novel methodology for discovering graph community structures. We analyze similarity, curvature-based attributes to allow links existing inside and outside the community to have different attribute values, and exploit them to design and analyze algorithms that eliminate links that affect the community structure less to find better community structures on sparse graphs. |
Å°¿öµå(Keyword) |
Ä¿¹Â´ÏƼ ¹ß°ß
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community detection
graph clustering
link attribute
graph sparsification
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