Graph Representation Learning for Classification and Anomaly Detection

Graph-structured data is ubiquitous across diverse domains, including social networks, recommendation systems, brain networks, computational chemistry, biology, sensor networks, and transportation networks. Graph neural networks have recently emerged as a powerful paradigm for the analysis of graph-structured data due to their ability to effectively capture complex relationships and learn expressive graph node representations through iterative aggregation of information from neighboring nodes. These learned representations can then be used in various downstream tasks such as node classification and anomaly detection. In this thesis, we introduce a graph representation learning model for semi-supervised node classification. The proposed feature-preserving model addresses the challenges of oversmoothing and shrinking effects by introducing a nonlinear smoothness term into the feature diffusion mechanism of graph convolutional networks. We conduct comprehensive experiments on diverse benchmark datasets demonstrating that our approach consistently outperforms or matches state-of-the-art baseline methods. Inspired by the concept of implicit fairing in geometry processing, we also propose a graph fairing convolutional network architecture for semi-supervised anomaly detection. The proposed model leverages a feature propagation rule derived directly from the Jacobi iterative method and incorporates skip connections between initial node features and each hidden layer, facilitating robust information propagation throughout the network. Our extensive experiments on five benchmark datasets showcase the superior performance of our graph fairing convolutional network compared to existing anomaly detection methods. In addition, we propose an unsupervised anomaly detection approach on graph-structured data by designing a graph encoder-decoder architecture and a locality-constrained pooling strategy. This pooling mechanism extracts local patterns and reduces the impact of irrelevant global graph information, enhancing the discriminative power of the learned features. In the decoding phase, an unpooling operation followed by a graph deconvolutional network reconstructs the graph data. Extensive experiments on six benchmark datasets demonstrate that our graph encoder-decoder model outperforms competitive baseline methods

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Robust watermarking schemes for multimedia protection

The rapid growth of digital multimedia data and the increasing use of the World Wide Web have triggered the need for the protection of multimedia contents (images, 3D graphics, audio, video). Digital watermarking has been proposed as an effective solution to the multimedia protection. The great challenge, however, is to devise efficient and robust watermarking schemes to tackle the fundamental tradeoff between robustness, data payload, and imperceptibility. This thesis is devoted to two robust watermarking schemes that we have developed for copyright protection. The first watermarking scheme uses discrete wavelet transform (DWT) and nonnegative matrix factorization (NMF). The core idea consists of decomposing an image into four wavelet sub-bands and then applying NMF to the blocks of each sub-band, followed by an eigen-decomposition distortion step. The second watermarking scheme uses multiple parameter discrete fractional Fourier (MPDFRF) transform and DWT. The original cover image is decomposed into four wavelet sub-bands, followed by a segmentation of each sub-band into blocks. Then the MPDFRF transform is applied to each block. The rest of the thesis is devoted to 3D mesh compression and fingerprinting. We propose a mesh compression technique using the mesh umbrella matrix. The key idea is to apply eigendecomposition to the umbrella matrix, and then retain the eigenvalues and eigenvectors which contain the low frequency coefficients. We also propose a mesh fingerprinting method by partitioning a 3D mesh into sub-meshes to produce the fingerprint vectors for all the sub-meshe