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Social networks are a crucial component of the Internet of People (IoP) which is the frontier of the next generation of Internet of Things (IoT). Predicting a large number of unknown node labels with few known labels is one of the challenging problems in social networks analysis. Fortunately, Graph Convolution Network (GCN) and subsequent variants have achieved impressive performance on Semi-Supervised Node Classification (SSNC). However, existing works only focus on the case of clean labels and rarely study the problem of SSNC under Noisy Labels (SSNCNL), which is a more challenging and practical problem in the field of weakly supervised learning. To address this challenge, in this paper, we propose a novel Dual Mutual Robust Graph Convolutional Network named DMRGCN, which is inspired by deep mutual learning and robust learning in the domain of image recognition. Specifically, we first employ two GCNs with different learning abilities to construct network architecture. Then we define a new loss function which is a weighted sum of the supervised loss, mutual loss, and robust loss. Finally, we train the network in the paradigm of pseudo-siamese network. Experimental results on three social network benchmark datasets with different levels of noise on labels demonstrate that DMRGCN outperforms the vanilla GCN and several variants in terms of classification accuracy. In particular, under the two conditions of clean labels and noisy labels, the accuracy of node classification obtained by our proposed DMRGCN can be 3.05% and 6.44% higher than that of the vanilla GCN, respectively.

Currently, electroencephalogram (EEG) is mostly analyzed in a supervised way, which requires EEG labels (e.g., EEG classification). With the ever-increasing amount of unlabeled/mislabeled EEG in neuropsychiatric disorder diagnosis, BCI, and rehabilitation, manually labeling of EEG data is a labor intensive and time-consuming process, and few labs have developed algorithms to analyze EEG in an unsupervised manner (i.e., EEG clustering). In this paper, we propose a cooperative game inspired approach to cluster multi-trial EEG data. The idea is to map multi-trial EEG clustering to the coalition formation in a cooperative game, and then identify cluster center (the EEG trial with highest Shapley value) and assign EEG trials into proper clusters based on their cross correlation-transformed Shapley values. We demonstrate the mapped EEG cooperative game is convex, and it leads to an algorithm for multi-trial EEG clustering named CoGEEGc. The CoGEEGc yields high-quality multi-trial EEG clustering with respect to intra-cluster compactness and inter-cluster scatter. We show that CoGEEGc outperforms 15 state-of-the-art EEG or time series clustering approaches through detailed experimentation on real-world multi-trial EEG datasets. Comparison against 15 methods with four theoretical properties of clustering further illustrates the superiority of CoGEEGc, as it satisfies two properties while other approaches only satisfy one.

Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and nonstationary time series, has been recently widely applied in neurocognitive disorder diagnoses and brain-machine interface developments. With its specific features, unlabeled EEG is not well addressed by conventional unsupervised time-series learning methods. In this article, we handle the problem of unlabeled EEG time-series clustering and propose a novel EEG clustering algorithm, that we call mwcEEGc. The idea is to map the EEG clustering to the maximum-weight clique (MWC) searching in an improved Fréchet similarity-weighted EEG graph. The mwcEEGc considers the weights of both vertices and edges in the constructed EEG graph and clusters EEG based on their similarity weights instead of calculating the cluster centroids. To the best of our knowledge, it is the first attempt to cluster unlabeled EEG trials using MWC searching. The mwcEEGc achieves high-quality clusters with respect to intracluster compactness as well as intercluster scatter. We demonstrate the superiority of mwcEEGc over ten state-of-the-art unsupervised learning/clustering approaches by conducting detailed experimentations with the standard clustering validity criteria on 14 real-world brain EEG datasets. We also present that mwcEEGc satisfies the theoretical properties of clustering, such as richness, consistency, and order independence.

IEEE The latent friend recommendation in online social media is interesting, yet challenging, because the user-item ratings and the user-user relationships are both sparse. In this paper, we propose a new dual implicit mining-based latent friend recommendation model that simultaneously considers the implicit interest topics of users and the implicit link relationships between the users in the local topic cliques. Specifically, we first propose an algorithm called all reviews from a user and all tags from their corresponding items to learn the implicit interest topics of the users and their corresponding topic weights, then compute the user interest topic similarity using a symmetric Jensen-Shannon divergence. After that, we adopt the proposed weighted local random walk with restart algorithm to analyze the implicit link relationships between the users in the local topic cliques and calculate the weighted link relationship similarity between the users. Combining the user interest topic similarity with the weighted link relationship similarity in a unified way, we get the final latent friend recommendation list. The experiments on real-world datasets demonstrate that the proposed method outperforms the state-of-the-art latent friend recommendation methods under four different types of evaluation metrics.

The Dulong Sn-Zn deposit is one of the largest cassiterite-sulfide deposits in China. There are different opinions regarding the genesis of this deposit, partly due to the lack of reliable geochronological data. We report here results of the first attempt to determine the magmatic and mineralization ages of this deposit and related late-Yanshanian granites through U-Pb dating of cassiterite and zircon. Four cassiterite samples from the Manjiazhai section yields a means 206Pb/238U age of 79.8+/- 3.2Ma (MSWD = 3.16), and a 238U/204Pb-206Pb/204Pb isochronal age of 82.0 +/- 9.6Ma (MSWD = 4.81) using the TIMS U-Pb method. Zircon from a concealed granite at the Manjiazhai section yielded a means 206Pb/238U age of 92.9 +/- 1.9Ma (N = 10, MSWD = 0.71), and zircon derived from a granite-porphyry outcrop at the Tongjie section yielded a means 206Pb/238U age of 86.9 +/- 1.4Ma (N = 9, MSWD = 3.70), both obtained using the SHRIMP U-Pb method. These ages suggest that the tin (copper) mineralization in the Dulong Sn-Zn deposit was mainly related to the late Cretaceous magmatic hydrothermal events. The geochronological data of the Gejiu (Sn) and the Bainiuchang (Ag) super-large deposits and related Yanshanian granites indicate that there was a large-scale granitic magmatism and mineralization event during the Cretaceous in southeastern Yunnan, which might have been related to the lithospheric extension of the South China Block in late Mesozoic.