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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) ¼Ò¼È ³×Æ®¿öÅ© »ó¿¡¼­ ¿ª¹æÇâ °æ·Î È°¼ºÈ­ ±â¹ý ±â¹Ý ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­ ¿¬±¸
¿µ¹®Á¦¸ñ(English Title) Reverse Path Activation-based Reverse Influence Maximization in Social Networks
ÀúÀÚ(Author) ¾Æ½Ã½º Å»·è´õ   ¾Æ´©ÆÊ Äí¸¶¸£ ¹ÙÀ̶ó±â   ±èµµÇö   È«Ãæ¼±   Ashis Talukder   Anupam Kumar Bairagi   Do Hyeon Kim   Choong Seon Hong  
¿ø¹®¼ö·Ïó(Citation) VOL 45 NO. 11 PP. 1203 ~ 1209 (2018. 11)
Çѱ۳»¿ë
(Korean Abstract)
¿µÇâ·Â ±Ø´ëÈ­(Influence Maximizaion)±â¹ýÀº ¼Ò¼È ³×Æ®¿öÅ©¿¡¼­ ¹ÙÀÌ·² ¸¶ÄÉÆÿ¡ ´ëÇÑ ¿µÇâ·Â ÀÖ´Â »ç¿ëÀÚ¸¦ ã´Â °ÍÀ» ´Ù·çÁö¸¸, ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­(Reverse Influence Maximizaion) ±â¹ýÀº ¿µÇâ ºñ¿ë ±Ø´ëÈ­ ¿µ¿ªÀÇ »õ·Î¿î ¿¬±¸ ¹æÇâÀ¸·Î ±âȸºñ¿ëÀ» ó¸®ÇÑ´Ù. ¿µÇâ·Â ±Ø´ëÈ­ ±â¹ýÀº ÀÌ·¯ÇÑ ½Ãµå³ëµå¸¦ ´ë»óÀ¸·Î ³×Æ®¿öÅ©¿¡¼­ ¿µÇâÀÌ ±Ø´ëÈ­µÇ´Â ¹æ½ÄÀ¸·Î ÀÛÀº ½Ãµå ÁýÇÕÀ» ÃßÁ¤ÇÑ´Ù. ÀϹÝÀûÀ¸·Î ½Ãµå³ëµå´Â ¿µÇâ·Â ±Ø´ëÈ­ ¹®Á¦¿¡¼­ óÀ½¿¡ È°¼ºÈ­µÇ´Â ³ëµå·Î °¡Á¤ÇÑ´Ù. ±×·¯³ª, ¿ì¸®´Â È°¼ºÈ­ µÈ ³ëµå°¡ ÃßÈÄ È°¼ºÈ­ µÉ ¿ÜºÎ ³ëµå¿¡ ¿µÇâÀ» ÁÖ´Â °Í°ú À¯»çÇÑ ¹æ½ÄÀ¸·Î ´Ù¸¥ ³ëµåÀÇ ¿µÇâÀ» ¹Þ¾Æ¾ßÇÑ´Ù°í ÁÖÀåÇÑ´Ù. ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­ ¹®Á¦´Â ¸ðµç ½Ãµå ³ëµå¸¦ È°¼ºÈ­Çϱâ À§ÇØ È°¼ºÈ­ÇؾßÇÏ´Â ÃÖ¼Ò ³ëµå ¼ö·Î Á¤ÀǵǴ ½Ãµå ºñ¿ëÀ» ã´Â ¹®Á¦ÀÌ´Ù. ÀÌ¿¡ º» ³í¹®¿¡¼­´Â ÃÖ¼Ò ±âȸ ºñ¿ëÀ» ã±â À§ÇØ Active Reverse Path ±â¹Ý ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­ ¸ðµ¨À» Á¦¾ÈÇÑ´Ù. º» ¸ðµ¨Àº Voting ¸ðµ¨°ú ÀüÅëÀûÀÎ Independent Cascade ¸ðµ¨À» ±â¹ÝÀ¸·Î ÇÑ´Ù. ¾Æ¿ï·¯ Àß ¾Ë·ÁÁø ¼¼ °¡Áö ¼Ò¼È ³×Æ®¿öÅ©ÀÇ ½ÇÁ¦ µ¥ÀÌÅÍ ¼ÂÀ» È°¿ëÇÏ¿© ¸ðµ¨À» ½Ã¹Ä·¹ÀÌ¼Ç ÇÏ¿´À¸¸ç, ±× °á°ú Á¦¾ÈÇÏ´Â ¸ðµ¨ÀÌ ±âÁ¸ ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­ ¸ðµ¨º¸´Ù ¿ì¼öÇÑ ¼º´ÉÀ» º¸¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
Influence Maximization (IM) deals with finding influential users for viral marketing in social networks, whereas Reverse Influence Maximization (RIM), a new research direction in the influence-maximization domain, deals with seeding cost, also known as opportunity cost. The IM estimates a small seed set in such a way that by targeting those seed nodes, the influence is maximized in the network. Generally, the seed nodes are assumed to be activated initially in the IM problem. However, we argue that seed nodes need to be influenced by some of their in-neighbor nodes in a similar way how an activated node influences its out-neighbors to be activated. The RIM problem finds the seeding cost, which is defined by the minimum number of nodes that must be activated in order to activate all the seed nodes. In this paper, we propose an Active Reverse Path-based Reverse Influence Maximization (ARP-RIM) model to find the minimum seeding cost. Our model is based on the voting model and the classic Independent Cascade model. We simulate our model with three real datasets of three popular social networks. The experimental result shows that the ARP-RIM model outperforms existing RIM models.
Å°¿öµå(Keyword) ¿µÇâ·Â ±Ø´ëÈ­   ¿ª¹æÇâ ¿µÇâ·Â ±Ø´ëÈ­   ±âȸ ºñ¿ë   Àç¹è ºñ¿ë   influence maximization   reverse influence maximization   opportunity cost   seeding cost  
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