Mobile Crowd-Sensing (MCS) has emerged as a promising solution for large-scale data collection in Internet of Things (IoT) scenarios. However, the malicious behavior of smart terminals, such as providing corrupted and falsified data or deliberately spreading false data, poses a significant threat to the credibility of MCS services. At present the mainstream trust evaluation schemes evaluate its trust value through the accumulation of terminal interaction experience, but the inherent defects of these schemes will make MCS service suffer serious trust-decaying destruction. And the current trust computing methods do not yet take into account the balance between the quality of service completion and the crowd-sensing collaboration experience of the terminal. To solve these problems, this paper proposes a novel general grey buffer trust evaluation framework (TrustGrey), which is used to evaluate the trust relationship of smart terminals. Specially, we construct the trust value calculation model of grey trust state for smart terminal to make up for the inherent defects of normal trust value calculation model. The grey buffer of sudden drop is designed in the trust value calculation model to avoid trust-decaying destruction. And we design a quick recovery mechanism of grey buffer to avoid detecting false alarm caused by the sudden drop of trust, as well as avoiding the loss of multiple damage of intelligent malicious terminal by an irreversible black value reduction mechanism. Then, we design a supply-demand equilibrium based dynamic recruitment mechanism to dynamically coordinate the recruitment process of service requests by comprehensively considering the importance level of service requests and the credibility of terminals, so as to balance the experience of service originators and completion parties. Experiments on real word datasets highlight the advantages of our proposed framework TrustGrey. And, the experiments also show that TrustGrey has considerable versatility.

Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node encoding, existing methods suffer from representation collapse which tends to map all data into the same representation. Consequently, the discriminative capability of the node representation is limited, leading to unsatisfied clustering performance. To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner. Specifically, in our method, we first design a siamese network to encode samples. Then by forcing the cross-view sample correlation matrix and cross-view feature correlation matrix to approximate two identity matrices, respectively, we reduce the information correlation in the dual-level, thus improving the discriminative capability of the resulting features. Moreover, in order to alleviate representation collapse caused by over-smoothing in GCN, we introduce a propagation regularization term to enable the network to gain long-distance information with the shallow network structure. Extensive experimental results on six benchmark datasets demonstrate the effectiveness of the proposed DCRN against the existing state-of-the-art methods. The code of DCRN is available at https://github.com/yueliu1999/DCRN and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.