Community is an important topological characteristic of complex networks, which is significant for understanding the structural feature and organizational function of networks, and community detection has recently attracted considerable research effort. Among community detection methods, label propagation technology is widely used because of its linear time complexity. However, due to the randomness of the node order of label updating and the order of label launching in label propagation, the instability of community detection approaches based on label propagation becomes a challenge. In this paper, a new label propagation algorithm, Node Ability based Label Propagation Algorithm (NALPA), is proposed to discover communities in networks. Inspired from human society and radar transmission, we design four node abilities (propagation ability, attraction ability, launch ability and acceptance ability), label influence and a new label propagation mechanism to deal with the instability and enhance the efficiency. Experimental results on 42 synthetic and 14 real-world networks demonstrate that NALPA outperforms state-of-the-art approaches in most cases. In a case study, NALPA is applied to a drug network in Traditional Chinese Medicine (TCM) and can discover the drug communities where drugs have similar efficacy for treating Chronic GlomeruloNephritis (CGN).

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Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification. However, most existing approaches focus on learning a single linear metric, which is unsuitable for nonlinear relationships and heterogeneous datasets with locality information. Besides, the hard samples in the training set account for only a small part, which may fail to characterize the global geometry of the metric embedding space. In this paper, we propose a novel deep multiple metric learning (DMML) method for time series classification. DMML contains a convolutional network component to extract nonlinear features of time series. For exploiting locality information, the last feature layer of the convolutional network is divided into several nonoverlapping groups and a separate metric learner is built on each group to get multiple metrics. In order to reduce the correlations among learners and facilitate robust metric learning, we design an adversarial negative generator to synthesize different hard negative complements for different metric learners. Moreover, an auxiliary loss is introduced to increase the robustness of DMML for the magnitude of distance. Extensive experiments on UCR datasets demonstrate the effectiveness of DMML for time series classification.