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In such cases, a e − 1 e -factor approximation solution can be obtained by picking the item with the maximum marginal gain repeatedly [17].
Since both Algorithms 3 and 4 run in O(Δ T ) time, we can find the node with the maximum marginal gain in O(Δ T +n) time.
However, how to evaluate the expected influence propagation for selecting the nodes with the maximum marginal gain was left as an open problem, and the greedy algorithm in [14] was implemented by Monte Carlo (MC) simulation.
The second step contains two parts, the first part is to choose a node with the maximum marginal gain and the second part is to update the two lists: IL and PL.
Moreover, we develop two algorithms to update the two lists when adding a new seed so that the next one with the maximum marginal gain can be directly obtained without running the EISE.
Kempe et al. (ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 137 146, 2003) showed that the standard greedy algorithm, which selects the node with the maximum marginal gain repeatedly, brings a e − 1 e -factor approximation solution to this problem.
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Doping is not a marginal gain.
But that is the only marginal gain.
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The one thing you see from us is it's all marginal gain.
At the very highest level, every marginal gain has to be snatched, hoarded, cherished.
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