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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Empirical strategy for stretching probability distribution in neural-network-based regression.

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Node Classification in Networks via Simplicial Interactions.

Eunho Koo, Tongseok Lim

    IEEE Transactions on Neural Networks and Learning Systems
    |March 3, 2025
    PubMed
    Summary

    This study introduces a novel objective function for semi-supervised node classification using higher-order networks. It enhances accuracy in complex scenarios by better capturing network structures than traditional methods.

    Area of Science:

    • Network Science
    • Machine Learning
    • Data Mining

    Background:

    • Node classification assumes densely connected nodes share similar attributes.
    • Assessing node cohesiveness and defining dense connections are critical.
    • Traditional models struggle with higher-order network structures.

    Purpose of the Study:

    • Propose a probability-based objective function for semi-supervised node classification leveraging higher-order networks.
    • Introduce the stochastic block tensor model (SBTM) for realistic network generation.
    • Enhance graph neural network (GNN) performance in node classification.

    Main Methods:

    • Developed a probability-based objective function for higher-order networks.
    • Proposed the stochastic block tensor model (SBTM) for graph generation.

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  • Integrated the objective function with GNN-based node classification.

    • Main Results:

    • The proposed objective function effectively classifies nodes in higher-order networks.
    • SBTM accurately models higher-order structures in generated networks.
    • Integration with GNNs improved classification performance, especially in challenging scenarios.

    Conclusions:

    • Higher-order network models outperform pairwise models in difficult node classification tasks.
    • The proposed objective function enhances GNN-based node classification by learning network structures.
    • This approach offers improved performance for semi-supervised node classification.