Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.

Silicon doped with selenium beyond the solid solubility limit was prepared by picosecond pulsed laser mixing of an evaporation-deposited Se film with the underlying p-Si wafer. Photodiodes fabricated from this material exhibit enhanced spectral response over the range from 400 to 1600 nm. The responsivity strongly depends on reverse bias voltage and pulsed laser fluence. At 5 V bias, a room-temperature responsivity of 16 A W −1 at 1000 nm is obtained. At below-bandgap wavelengths, measurable responsivities of 15 mA W −1 at 1330 nm and 12 mA W −1 at 1550 nm are also observed. Extended Se impurity states might form within Si bandgap and contribute to the prominent photoresponse in these photodiodes.