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Recent advances in deep learning have tremendously leveraged the performance of network representation learning (NRL). Multiple deep learning-based NRL models have been proposed recently to effectively handling primitive tasks of information network analysis and mining (INAM) domain, including link prediction (LP). LP is considered as an important one due to its multiple applications in many disciplines. In the recent few years, LP in dynamic networks has attracted a lot of attention from researchers to propose novel algorithms for better capturing both rich structural and evolutional information of complex information networks (INs). However, recent models are mainly concentrated on preserving the sequential representations of a given network over time. They have largely ignored other important structural features, such as: intracommunity which contributes to the creation of links between network nodes. In this article, we propose a novel community-driven dynamic NRL technique upon the RNN+GCN framework, called: ComGCN. Specifically, the ComGCN model is a combination of microscopic (node embedding-based) and mesoscopic (intracommunity-based) dynamic network embedding approach which enable effectively handling the LP problem in context of dynamism. Extensive experiments on real-world dynamic networks demonstrated the effectiveness of the proposed model compared with recent state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Abundant data with limited labeling are a widespread bottleneck in multilabel learning. Active learning (AL) is an effective solution to gradually enhance model robustness, however how to effectively extend instance selection criteria to multilabel case remains challenging. Considering the label specificity and label correlation, a granular batch mode-based ranking active model for the multilabel (GBRAML) is proposed. Taking a bottom-up view, three granulation operators are successively constructed to formulate three granular structures. In low-level granulation operator, auxiliary label is introduced to enhance the informativeness and representativeness of each label. The contribution of labels to the usefulness of instances is incorporated with pair-wise label correlation, and is considered in the middle-level granulation operator. The labeling priority is determined by ranking the scorings coming from high-level granulation operator. To alleviate the impact of skewed label correlation, we take a three-way strategy on fitness of representative label correlation, thus a three-way GBRAML model (TGBRAML) is devised. Extensive experiments on six multilabel benchmark demonstrate GBRAML gains 5.4% and 210.1% improvement on MicroF1 and Average Precision over state-of-the-art batch mode multilabel active learning. The effectiveness of three-way decisions in multilabel AL is also verified. [ABSTRACT FROM AUTHOR]

In computing with words, it has been stressed that words mean different things for different people, which entails that decision makers (DMs) have personalized individual semantics (PISs) attached to linguistic expressions in linguistic group decision making (GDM). In particular, the PISs of DMs are not fixed, and they will be changing during the consensus building process, which indicates the necessary of continual PIS learning. Therefore, in this article, we propose a continual PIS-learning-based consensus approach in linguistic GDM. Specifically, a continual PIS learning model with the consistency-driven methodology is proposed to update the PISs taking into account all the linguistic preference data given by DMs during the consensus process. Then, the consensus measurement and feedback recommendation based on PIS are developed to detect the consensus process. Finally, numerical examples and simulation analysis are presented to illustrate and justify the use of the continual PIS-learning-based consensus approach. [ABSTRACT FROM AUTHOR]