We study the spammablility of ranking functions on the web. Although graph-theoretic ranking functions, such as Hubs and Authorities and PageRank exist, there is no graph theoretic notion of how spammable such functions are. We introduce a very general cost model that only depends on the observation that changing the links of a page that you own is free, whereas changing the links on pages owned by others requires effort or money. We define spammability to be the ratio between the amount of benefit one receives for one's spamming efforts and the amount of effort money one must spend to spam. The more effort money it takes to get highly ranked, the less spammable the function. Our model helps explain why both hubs and authorities and standard PageRank are very easy to spam. Although standard PageRank is easy to spam, we show that there exist spam-resistant PageRanks. Specifically, we propose a ranking method, Min-k-PPR, that is the component-wise min of a set of personalized PageRanks centered on k trusted sites. Our main results are that Min-k-PPR is, itself, a type of PageRank and that it is expensive to spam. We elucidate a surprisingly elegant algebra for PageRank. We define the space of all possible PageRanks and show that this space is closed under some operations. Most notably, we show that PageRanks are closed under (normalized) component-wise min, which establishes that (normalized) Min-k-PPRis a PageRank. This algebraic structure is also key to demonstrating the spam resistance of Min-k-PPR.
Other work is geared towards modifications of the mechanism. For instance, Global Hitting Time @cite_12 was designed as a transformation of to counter cross-reference link spam, where nodes link each other to increase their rank, but it still suffers if the number of spammers is large. Variants include Personalized Hitting Time @cite_14 .
@cite_14 @cite_12
2037242246 2743620784 2097426907 2004797988
Popular reputation systems for linked networks can be manipulated by spammers who strategically place links. The reputation of node v is interpreted as the world's opinion of v's importance. In PageRank [4], v's own opinion can be seen to have considerable influence on her reputation, where v expresses a high opinion of herself by participating in short directed cycles. In contrast, we show that expected hitting time -- the time to reach v in a random walk -- measures essentially the same quantity as PageRank, but excludes v's opinion. We make these notions precise, and show that a reputation system based on hitting time resists tampering by individuals or groups who strategically place outlinks. We also present an algorithm to efficiently compute hitting time for all nodes in a massive graph; conventional algorithms do not scale adequately. The region-based Convolutional Neural Network (CNN) detectors such as Faster R-CNN or R-FCN have already shown promising results for object detection by combining the region proposal subnetwork and the classification subnetwork together. Although R-FCN has achieved higher detection speed while keeping the detection performance, the global structure information is ignored by the position-sensitive score maps. To fully explore the local and global properties, in this paper, we propose a novel fully convolutional network, named as CoupleNet, to couple the global structure with local parts for object detection. Specifically, the object proposals obtained by the Region Proposal Network (RPN) are fed into the the coupling module which consists of two branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to capture the local part information of the object, while the other employs the RoI pooling to encode the global and context information. Next, we design different coupling strategies and normalization ways to make full use of the complementary advantages between the global and local branches. Extensive experiments demonstrate the effectiveness of our approach. We achieve state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7 on VOC07, 80.4 on VOC12, and 34.4 on COCO. Codes will be made publicly available. This paper proposes a modification to the existing anticollision protocol put forth in version 1.0 protocol specification for 900MHz Class 0 RFID Tag. The version 1.0 specification uses a binary tree approach to singulate one RF tag ID at a time. The proposed change reduces the overall read time of a given number of RFID tags by resetting to the appropriate node, for every consecutive read cycle. The present standard resets to the root node of the binary tree for every read cycle. This paper describes the development of a novel metaheuristic that combines an electromagnetic-like mechanism (EM) and the great deluge algorithm (GD) for the University course timetabling problem. This well-known timetabling problem assigns lectures to specific numbers of timeslots and rooms maximizing the overall quality of the timetable while taking various constraints into account. EM is a population-based stochastic global optimization algorithm that is based on the theory of physics, simulating attraction and repulsion of sample points in moving toward optimality. GD is a local search procedure that allows worse solutions to be accepted based on some given upper boundary or level'. In this paper, the dynamic force calculated from the attraction-repulsion mechanism is used as a decreasing rate to update the level' within the search process. The proposed method has been applied to a range of benchmark university course timetabling test problems from the literature. Moreover, the viability of the method has been tested by comparing its results with other reported results from the literature, demonstrating that the method is able to produce improved solutions to those currently published. We believe this is due to the combination of both approaches and the ability of the resultant algorithm to converge all solutions at every search process.