Motivation: Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss. Results: This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.

Personalized exercise recommendation is essential for enhancing learning efficiency by adapting educational content to individual student needs. However, current approaches often fail to simultaneously minimize the number of exercises while ensuring students achieve specific learning objectives, and they overlook the nuanced knowledge structures revealed during problem-solving. To address these limitations, this paper introduces a novel framework for dynamic requirement-driven exercise recommendation (DRER), which combines confusion-aware knowledge tracing with nonlinear combinatorial optimization. DRER consists of two key stages. First, in the knowledge tracing stage, a hybrid knowledge concept matrix is constructed to model both the inherent relationships among knowledge concepts and the latent structures revealed through student interactions. Second, in the exercise recommendation stage, the problem is formulated as a Minimum nonlinear weighted set cover problem (MNLWSCP), aiming to identify the smallest set of exercises that enables students to reach a predefined proficiency threshold (e.g., 0.6). To solve this efficiently, a heuristic-based local search algorithm is proposed. Extensive experiments on real-world and academic datasets validate the effectiveness of the framework, demonstrating its ability to significantly reduce the number of recommended exercises while ensuring high accuracy in achieving learning objectives. This work represents a significant integration of data mining and combinatorial optimization, offering a scalable and practical solution for personalized education.

Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliques that can cover the maximum weight in a vertex weighted graph. In this work, we propose a novel local search algorithm called TOPKWCLQ for the DTKWC search problem which mainly includes two strategies. First, a restart strategy is adopted, which repeated the construction and updating processes of the maximal weight clique set. Second, a scoring heuristic is designed by giving different priorities for maximal weight cliques in candidate set. Meanwhile, a constraint model of the DTKWC search problem is constructed such that the research concerns can be evaluated. Experimental results show that the proposed algorithm TOPKWCLQ outperforms than the comparison algorithm on large-scale real-world graphs.