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#0 dbbase_sql->halt(Invalid SQL: select count(id) from dev_comment where pid='63538' and iffb='1') called at [/data/www/shop/includes/db.inc.php:54] #1 dbbase_sql->query(select count(id) from {P}_comment where pid='63538' and iffb='1') called at [/data/www/shop/comment/module/CommentContent.php:65] #2 CommentContent() called at [/data/www/shop/includes/common.inc.php:551] #3 printpage() called at [/data/www/shop/comment/html/index.php:13]
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Database error: Invalid SQL: select * from dev_comment where pid='63538' and iffb='1' order by id limit 0,10
MySQL Error: 1194 (Table 'dev_comment' is marked as crashed and should be repaired)
#0 dbbase_sql->halt(Invalid SQL: select * from dev_comment where pid='63538' and iffb='1' order by id limit 0,10) called at [/data/www/shop/includes/db.inc.php:54] #1 dbbase_sql->query(select * from {P}_comment where pid='63538' and iffb='1' order by id limit 0,10) called at [/data/www/shop/comment/module/CommentContent.php:167] #2 CommentContent() called at [/data/www/shop/includes/common.inc.php:551] #3 printpage() called at [/data/www/shop/comment/html/index.php:13]
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发布于:2019-3-13 19:54:24  访问:31 次 回复: 篇
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Ties involving a and b, but clearly simsr actually decreases. SimRank
Thus, their similarity measure discards the concept of random stroll and replaces it with "the PRN1008 Formula typical similarity of the maximal matching amongst their neighbors" [Lin et ymj.2016.57.6.1427 al. SimRank++: Antonellis et al. [Antonellis et al. 2008] partially compensates for this unwanted reduce by inserting an proof factor. The additional neighbors in common, the larger the proof of similarity. They define evidence asNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript(7)exactly where N(a) is the undirected neighbor set of a. If a and b have only one particular neighbor in frequent, e = 1/2. As the variety of neighbors increases, e 1. This yields the following similarity definition:(8)The quite narrow range [0.5, 1] on the proof factor, even so, leads to the issue that sime () values are no longer bounded to a maximum of 1 or even to a continual. As an alternative, the maximum is determined by the maximum value of N(a) ?N(b) for the graph. The authors make a single far more extension to help edge-weighted graphs. Their final measure is known as SimRank++:(9)PSimRank: Fogaras and R z [Fogaras and R z 2005] realize that the trigger of improper weighted of neighbor-matching in SimRank is because of the paired-random walk model. Ignoring the decay continuous c for the moment, SimRank values are equal for the probability that two simultaneous random walkers, starting at nodes a and b, will eventually encounter one another. Even in the best case scenario, in which a and b have all of the identical neighbors in typical, in order that N(a) = CPAA.S108966 N(b), the probability that the two walkers will happen to pick out precisely the same neighbor is 1/da, which decreases as the degree increases. To emend this scenario, Fogaras and R z introduce coupled random walks. They partition the occasion space into three instances: 1.two.three.ACM Trans Knowl Discov Data. Author manuscript; obtainable in PMC 2014 November 06.Jin et al.PageNote that in case 1, which we would take into consideration the direct similarity of a and b, is described by the Jaccard Coefficient. As expected, the sum of these probabilities equals 1. We are able to then compute a similarity measure which requires the common formNIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author ManuscriptNoting that you will discover |I(a)\I(b)| ?|I(b)| neighbor-pairs in Case 2 and |I(b)\I(a)| ?|I(a)| in Case three, this produces the logical but somewhat unwieldy formula:(10)MatchSim: The authors of MatchSim [Lin et al. 2009] take this emendment of random walking to its limit. They observe that when a human 2278-0203.186164 compares the capabilities of two objects, a human does not choose random attributes to find out if they match. Rather, folks appear to determine if there exists an alignment of capabilities that produces an ideal or near-perfect matching. Thus, their similarity measure discards the concept of random walk and replaces it with "the typical similarity with the maximal matching amongst their neighbors" [Lin et ymj.2016.57.six.1427 al. 2009]:(11)where m represents the maximal matching.
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