To address the issues of structural unfairness and classification inaccuracy of Graph ATtention network （GAT） model in node classification tasks， a Graph Neural Network （GNN） node classification model incorporating clustering coefficients， named GATcc（GAT with clustering coefficient）， was proposed. Firstly， by introducing the clustering coefficients of neighboring nodes as structural information， and combining trainable weight parameters， the representation ability of the topological structure in the attention mechanism was enhanced. Then， feature scaling was employed to optimize node embeddings， and residual connections were added to mitigate the risk of feature over-smoothing. Experimental results on six real datasets demonstrate that the proposed model outperforms the mainstream models， such as Graph Isomorphism Network （GIN） and GOAT （Graph Ordering Attention Network）， in classification accuracy. For instance， compared to the baseline model GAT on the Cora dataset， the proposed model has the classification accuracy improved by 4.03 percentage points， the structural bias reduced from 0.31% to 0.11%， and the classification accuracy of isolated nodes improved by 3.69 percentage points. In conclusion， the proposed model not only achieves significant improvements in classification performance， but also shows superiority in structural fairness and stability.

Vehicular Ad-hoc NETwork （VANET） is vital for constructiong intelligent transportation systems because of obvious advantages in sharing traffic data， improving driving efficiency and reducing traffic accidents. Meanwhile， problems such as secure communication of vehicle-to-vehicle and vehicle-to-infrastructure， privacy-preserving of vehicles （e.g.， identity privacy， location privacy）， and efficient authentication of traffic messages need to be solved urgently. To achieve a trade-off between security and efficiency， firstly， the recently proposed scheme， namely Conditional Privacy-Preserving CertificateLess Aggregate Signature scheme （CPP-CLAS）， was analyzed and proved to be unable to resist the public key replacement attack. Then， based on this scheme， a new certificateless conditional privacy-preserving authentication scheme for VANET was proposed， in which the secure channels were not required during partial private key generation of vehicles. In addition， aggregate verification and batch verification were employed to verify a batch of signatures in the scheme. Finally， the proposed scheme was proved to have unforgeability under random oracle model. Performance analysis show that compared with the similar schemes， the proposed scheme improves the computational efficiency of the signature phase by at least 66.76% and reduces the communication bandwidth demand by at least 16.67% without increasing the verification overhead， verifying that the proposed scheme is more suitable for resource-constrained VANET.

Because multiple kernel learning can avoid selection of kernel functions and parameters effectively， and graph clustering can fully mine complex structural information between samples， Multiple Kernel Graph Clustering （MKGC） has received widespread attention in recent years. However， the existing MKGC methods suffer from the following problems： graph learning technique complicates the model， the high rank of graph Laplacian matrix cannot ensure the learned affinity graph to contain accurate c connected components （block diagonal property）， and most of the methods ignore the high-order structural information among the candidate affinity graphs， making it difficult to fully utilize the multiple kernel information. To tackle these problems， a novel MKGC method was proposed. First， a new graph learning method based on capped simplex projection was proposed to directly project the kernel matrices onto graph simplex， which reduced the computational complexity. Meanwhile， a new block diagonal constraint was introduced to keep the accurate block diagonal property of the learned affinity graphs. Moreover， the low-rank tensor learning was introduced in capped simplex projection space to fully mine the high-order structural information of multiple candidate affinity graphs. Compared with the existing MKGC methods on multiple datasets， the proposed method has less computational cost and high stability， and has great advantages in Accuracy （ACC） and Normalized Mutual Information （NMI）.