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Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this(More)
Nowadays, access networks connecting fixed users are independently planned from the mobile backhaul, which interconnects the base stations. This independence causes high capital expenditures due to the investment of having two networks instead of a single one. The recently proposed Hybrid PON architecture could be used to offer more bandwidth to the base(More)
Graphlet frequency distribution (GFD) has recently become popular for characterizing large networks. However, the computation of GFD for a network requires the exact count of embedded graphlets in that network, which is a computationally expensive task. As a result, it is practically infeasible to compute the GFD for even a moderately large network. In this(More)
Traditional data mining mechanisms with their robustly defined classification techniques have certain limitations to express to what extent the class labels of the test data hold. This problem leads to the fact that a false positive or false negative data point has no quantitative value to express to what degree it is false/true. This situation becomes much(More)
As users' preferences shift continuously, recommendation system has to learn quickly from them. It is an interesting online learning problem as recommender does not have any prior knowledge about the distribution of items over the users. In this work, we generate a small recommendation set from a large number of items, with an intention that at least one of(More)
We study a recommendation system problem, in which the system must be able to cover as many users’ preferences as possible while these preferences change over time. This problem can be formulated as a variation of the maximum coverage problem; specifically we introduced a novel problem of Online k-Hitting Set, where the number of sets and elements within(More)
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