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.

Recent studies in recommender systems focus on addressing data sparsity and cold-start problems by utilizing side information, such as tags, images, and testimonials. Among these, user-written testimonials (purchase reviews) are precious for analyzing personal preferences, and many methods have been developed based on this context. Generally, existing methods apply 2D text convolution followed by selecting important words using the attention mechanism. However, the text convolution scheme inevitably suffers from information loss since the number of words in reviews commonly exceeds hundreds. To address this limitation, we focus on the Large Language Model (LLM), which has shown promising results in various fields, including search engines, natural language processing, and healthcare. In particular, LLM has demonstrated excellent performance in text summarization and QA tasks, leading to the development of text-based recommender systems. Nevertheless, LLM alone struggles to perform collaborative filtering, which is essential in a recommender system. Thus, we propose LLM-based text summarization before applying 2D convolution, followed by the widely used collaborative filtering mechanism. This approach can improve recommendation quality by removing unnecessary words in advance, reducing the smoothing effect while capturing the rich user-item interactions. Our method is integrated with recent text-based recommendation algorithms, which have proven to improve the quality of all baselines by about 16.9 % on average. We conduct experiments and ablation studies using benchmark datasets, demonstrating that our method is scalable and efficient.

Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-driven solutions are feasible, this is complicated by significant data sparsity due to the low proportion of wetlands (3-6%) in many areas of interest in the southwestern US. This makes it hard to develop data-driven models that can help guide the identification of additional wetland areas. To solve this limitation, we propose two strategies: (1) knowledge transfer from regions with rich wetlands (such as the Eastern US) to regions with sparser wetlands (such as the Southwestern area). , and (2) spatial data enrichment strategy that relies on an adaptive propagation mechanism. This mechanism differentiates between node pairs that have positive and negative impacts on each other for Graph Neural Networks (GNNs). We conduct rigorous experiments to substantiate our proposed method's effectiveness, robustness, and scalability compared to state-of-the-art baselines. Additionally, an ablation study demonstrates that each module is essential in prioritizing potential wetlands.

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.