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Related Concept Videos

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Graphs of Two-Variable Functions01:27

Graphs of Two-Variable Functions

A weather map provides a practical example of a function of two variables. Across a wide region such as the United States, temperatures vary from one location to another. Each location can be identified by two geographic coordinates: longitude and latitude. Since a single temperature value is assigned to each coordinate pair, the situation can be represented mathematically as a function with two inputs and one output.In mathematical notation, longitude and latitude can be labeled as x and y,...
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...

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Related Experiment Videos

Dynamic-n-Static Multiplex Graph Representation Learning for Improved Link Prediction.

Jia Yu, Mengjun Ding, Weiqiang Sun

    IEEE Transactions on Neural Networks and Learning Systems
    |June 15, 2026
    PubMed
    Summary
    This summary is machine-generated.

    We introduce DS-MGN, a novel model for link prediction in dynamic-n-static multiplex graphs. It leverages static layers to improve predictions in evolving dynamic layers, achieving state-of-the-art results.

    Related Experiment Videos

    Area of Science:

    • Graph Neural Networks
    • Network Science
    • Machine Learning

    Background:

    • Social systems feature diverse relations with unique temporal dynamics, often modeled as temporal multiplex graphs.
    • Dynamic-n-static multiplex graphs combine evolving dynamic layers with stable static layers, presenting unique challenges for link prediction.

    Purpose of the Study:

    • To propose DS-MGN, a representation learning model for dynamic-n-static multiplex graphs.
    • To enhance link prediction accuracy by utilizing information from static graph layers.

    Main Methods:

    • Developed DS-MGN, incorporating a cross-layer neighbor encoding (CLNE) scheme.
    • CLNE injects topological priors from static layers to enrich dynamic layer representations.
    • Created a new dynamic-n-static multiplex graph dataset for research.

    Main Results:

    • DS-MGN achieved state-of-the-art performance against 12 baselines on benchmark and custom datasets.
    • Demonstrated substantial performance improvements through the CLNE scheme.
    • Highlighted the model's scalability and effectiveness.

    Conclusions:

    • DS-MGN offers a robust solution for link prediction in complex dynamic-n-static multiplex graphs.
    • The CLNE scheme is adaptable and enhances existing neural network architectures.
    • The model shows promise for real-world applications like social network analysis and recommendation systems.