Message-passing neural networks are widely employed in various graph mining applications. However, these methods are susceptible to the scarcity of labeled data, which often leads to overfitting. Our observations suggest that sparse initial vectors further exacerbate this issue by failing to fully represent the range of learnable parameters. This sparsity can hinder the optimization of specific dimensions in the initial projection matrix, as the training samples may not adequately span these parameters. To overcome this challenge, we propose a novel perturbation technique that introduces variability to the initial features and the projection hyperplane. Notably, even without employing grid search, we demonstrate that shifting with a small estimated value mitigates this problem more effectively than other perturbation methods. Experimental results on real-world datasets reveal that our technique significantly enhances node classification accuracy in semi-supervised scenarios.

The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target) has emerged recently. Nevertheless, existing methodologies assume an Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex user-item interactions. This paper advocates a hyperbolic CDR approach for modeling review-based user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. Extensive experiments substantiate the efficiency, robustness, and scalability of the proposed model. The source code is given here https://github.com/ChoiYoonHyuk/HEAD.

Global food security is increasingly at risk due to factors like climate change and population growth, necessitating advancements in agricultural technology. Digital breeding, a method centered around genotype-phenotype selection using next-generation sequencing (NGS), offers a solution by enabling the identification of genetic mutations and predicting crop traits with greater speed and precision compared to traditional approaches. This automated breeding process efficiently gathers and analyzes genotype and phenotype data, improving key traits such as growth, yield, and tolerance while minimizing human intervention. Despite advancements in sequencing technologies, challenges remain due to the high cost and impracticality of acquiring extensive genomic datasets. To address these limitations, this study explores data augmentation strategies using deep learning techniques, focusing on their success in other fields like computer vision. Unlike conventional Generative Adversarial Networks (GANs), which face stability issues, we present a novel approach using a stacked convolutional and LSTM architecture. This model leverages SNP position information to capture correlations within genomic regions and introduces a specialized metric to evaluate the quality of augmented data. The effectiveness of the proposed phenotype prediction model is demonstrated through real-world testing on a collection of 192 tomato varieties, highlighting its potential to revolutionize breeding processes and improve agricultural outcomes.

Food shortages, driven by population growth and climate change, pose a significant global challenge. Addressing this issue requires a dual focus on increasing food production and optimizing land use efficiency. Rather than expanding farmland, current efforts emphasize enhancing crop productivity through plant breeding. Especially, plant breeding research has emerged as a critical solution to the escalating challenges posed by a rapidly growing population and unpredictable climate changes. While plant breeding has a history of contributing to improved crop productivity, addressing current food problems requires the application of innovative technologies. Therefore, digital breeding, which incorporates new technologies, has gained prominence recently. Digital breeding uses high-throughput sequencing technology known as next-generation sequencing (NGS) to decode genome sequences and collect mutation information from diverse individuals. This method selects individuals with specific traits by analyzing the relationship between genotype and phenotype. By leveraging genotype information, digital breeding accurately pinpoints individuals with desired characteristics, significantly expediting the breeding process compared with traditional or molecular breeding methods. The distinctive advantages of digital breeding, such as precise genome selection, make it a promising approach for addressing food security challenges arising from population growth and climate change. Consequently, ongoing research in this domain, particularly combining big data and artificial intelligence technology, further underscores the significance of these advancements. This study explores the significance of artificial intelligence-based genome selection techniques, and associated technologies and outlines future research.

Graph Neural Networks (GNNs) have proven to be powerful in many graph-based applications. However, they fail to generalize well under heterophilic setups, where neighbor nodes have different labels. To address this challenge, we employ a confidence ratio as a hyper-parameter, assuming that some of the edges are disassortative (heterophilic). Here, we propose a two-phased algorithm. Firstly, we determine edge coefficients through subgraph matching using a supplementary module. Then, we apply GNNs with a modified label propagation mechanism to utilize the edge coefficients effectively. Specifically, our supplementary module identifies a certain proportion of task-irrelevant edges based on a given confidence ratio. Using the remaining edges, we employ the widely used optimal transport to measure the similarity between two nodes with their subgraphs. Finally, using the coefficients as supplementary information on GNNs, we improve the label propagation mechanism which can prevent two nodes with smaller weights from being closer. The experiments on benchmark datasets show that our model alleviates over-smoothing and improves performance.

A cross-domain recommendation has shown promising results in solving data-sparsity and cold-start problems. Despite such progress, existing methods focus on domain-shareable information (overlapped users or same contexts) for a knowledge transfer, and they fail to generalize well without such requirements. To deal with these problems, we suggest utilizing review texts that are general to most e-commerce systems. Our model (named SER) uses three text analysis modules, guided by a single domain discriminator for disentangled representation learning. Here, we suggest a novel optimization strategy that can enhance the quality of domain disentanglement, and also debilitates detrimental information of a source domain. Also, we extend the encoding network from a single to multiple domains, which has proven to be powerful for review-based recommender systems. Extensive experiments and ablation studies demonstrate that our method is efficient, robust, and scalable compared to the state-of-the-art single and cross-domain recommendation methods.