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Abstract Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous featu...

ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data aug...

Modern software evolves rapidly, accumulates architectural debt, and develops cross-module dependencies that complicate reliable maintenance. We present EvoGraphCoder, an evolutionary graph-reasoning framework for self-a...

Abstract The pre-training molecular representation learning (MRL) has shown considerable potential in computer-aided drug discovery. Recently, many multimodal pre-training MRL methods have been presented, incorporating m...

Graph-based text classification has become an effective paradigm for modeling corpus-level structure, particularly under limited supervision. However, most existing approaches rely on document–word representations...

Abstract Heterogeneous graphs are widely employed in applications such as social networks, recommendation systems, and bioinformatics. However, node attributes in real-world heterogeneous graphs are often missing or corr...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

The process of thinking in formulating a mathematical model requires cognitive processes. In learning mathematics, one of the factors that influence mathematics ability is gender differences.  This research explores and...

Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. However, favoring a more simplified, spheri...

ABSTRACT The analysis of spatiotemporal data is essential across many fields, such as transportation, meteorology and healthcare. Data gathered in practical applications often suffer from incompleteness due to device fai...

Abstract Credit risk assessment is a critical function in financial analytics, requiring models that can adapt to diverse borrower profiles while providing clear and interpretable insights. Although a range of data drive...

Abstract Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band struct...

Background: Students’ prior mathematical knowledge plays a critical role in their success in university mathematics, yet it is often treated diagnostically rather than instructionally. Aim: This study examines studen...

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arith-metic, but the origin of these regularities has remained o...

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statisti...

Materias / palabras clave: graph algorithms, graph databases, graph representation learning, knowledge graphs, knowledge representation, linked data; Science: Mathematics: Instruments and machines: Electronic computers. Computer science