In this paper, we introduce a novel method for embedding weighted graphs, designed to capture the significance of relationships in real-world weighted graph data. Unlike existing models that treat all relationships equally, our approach incorporates edge weights directly into the embedding process, enhancing the representation of highly weighted connections. By introducing modifications to both the scoring and loss functions, our method emphasizes the importance of weighted relationships during training. Theoretical analysis shows that our model maintains efficiency and scalability while significantly improving the capacity to represent weighted graph structures. Experimental results demonstrate that our approach consistently outperforms baseline methods, including TransE, in tasks involving weighted relationships, showcasing its robustness and applicability.

Hyperspectral Imaging (HSI) captures rich spectral information, but its classification remains challenging due to high dimensionality, scarcity of labeled samples, and class imbalance. These factors exacerbate overfitting, inter-class similarity, and intra-class variability, especially in low-label scenarios. This paper introduces a novel framework that integrates contrastive learning into a Graph Convolutional Network (GCN) within a self-representation learning paradigm. A spectral similarity-based graph is first constructed to model meaningful afnities between pixels. Latent embeddings are then generated via GCN propagation, which indirectly incorporates local spatial context. These embeddings are refined using a joint contrastive objective combining Centroid Metric Loss and Triplet Loss with Semi-Hard Negative Mining to enhance intra-class compactness and inter-class separation. A separate classification module is trained with Cross-Entropy Loss, allowing the model to decouple feature discrimination from label supervision. This dual objective design promotes the emergence of minority-class samples, reduces overfitting, and yields a more structured and balanced embedding space. Extensive experiments on the Indian Pines and Pavia University datasets confirm the effectiveness of the proposed method, which consistently improves classification accuracy and robustness over existing models.

Knowledge graph embedding has emerged as a fundamental technique to represent entities and relationships in knowledge graphs within low-dimensional vector spaces. Among these methods, translation-based approaches stand out by treating relations as translations from head entities to tail entities, achieving state-of-the-art results. However, the training process of these methods can be prohibitively time-consuming, especially for large knowledge graphs, posing significant challenges in practical applications. As knowledge graphs grow in size and complexity, surpassing the capacities of existing systems, there is an urgent need for scalable solutions in knowledge representation learning. These graphs, comprising millions of nodes and billions of edges, serve as powerful data structures for representing and understanding complex networks of knowledge. Translation-based models, particularly TransE, have been instrumental in encoding structured information about entities and their relationships in low-dimensional embedding spaces. However, the current implementation of TransE is constrained to single-node machines, limiting its scalability and applicability. To address these limitations, this paper proposes leveraging parallel computing technique, such as parallel TransE with graph partitioning, to enhance scalability and efficiency in knowledge graph embedding.

In the last decades, learning over graph data has became one of the most challenging tasks in deep learning. The generally proposed Graph Neural Network (GNN) framework computes a hidden state for every node in the graph by applying nonlinear transformations to its neighborhood. Then updates the node of interest's hidden state. Nevertheless, the node features can contain discriminative information. That can get lost over GNN transformations. In this work, we present a new variant of GNN architecture where we combine node features and GNN activations to learn node representations in the graph. We conduct extensive experiments on two graph prediction tasks (node classification and link prediction) and show that our method can match and outperforms state-of-the-art results on five challenging datasets.

Heterogeneous graphs (HG) are an effective way of abstracting complex systems, including social, biological, and economic systems. However, modeling these graphs is challenging due to their high dimensionality, sparsity, and heterogeneity. Traditional approaches designed for homogeneous graphs struggle to handle the diverse types of entities and relationships present in HG, leading to a loss of information and potentially inaccurate embeddings. To address these challenges, we introduce a novel method, Trans-Trip: Translation-based embedding with Triplets for Heterogeneous Graphs, that leverages the power of triplets of (entity, relation, entity) to accurately represent the various types of relationships among nodes and links. Trans-trip effectively captures the rich semantics embedded in HGs, overcoming the challenges of global coherence and entity projection. By leveraging the flexibility and interpretability of triplets, our method can handle the multi-types nodes/links present in HG and can capture complex higher-order structures. We demonstrate the effectiveness of our proposed method on several benchmark datasets, showing that it outperforms existing embedding methods. Trans-trip provides a more accurate and interpretable representation of HG, which can be used across various fields, such as biology, social networks, and e-commerce.

Breast-cancer (BC) is the most common invasive cancer in women, with considerable death. Given that, BC is classified as a hormone-dependent cancer, when it collides with pregnancy, different questions may arise for which there are still no convincing answers. To deal with this issue, two new frameworks are proposed within this paper: CoRaM and Dist-CoRaM. The former is the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare Association rules from gene expression data. The proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results. The latter, the Dist-CoRaM approach, is a big-data processing based on Apache spark framework, dealing with correlation mining from micro-array pregnancy associated breast-cancer assays (PABC) data. It is successfully applied on the (GSE31192) gene expression matrix (GEM). The correlated patterns of gene-sets shed light on the fact that PABC exhibits heightened aggressiveness compared to cancers for Non-PABC women. Our findings suggest that higher levels of estrogen and progesterone hormones, unfortunately, are very keen to the increase of the tumor aggressiveness and the proliferation of the cancer.