Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Protein Networks

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

Protein Networks

Protein Networks

Network Covalent Solids

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

Avoidance Learning and Learned Helplessness

Avoidance Learning and Learned Helplessness

Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...

Quantitative Aspects of Drug-Receptor Interaction

Quantitative Aspects of Drug-Receptor Interaction

The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Mechanistic Insight into Self-Gelation Involved in Prescription Design for Optimization of Tablet Performance.

Molecular pharmaceutics·2026

Same author

Experimental and theoretical quantification of focused ultrasonic cavitation intensity.

The Journal of the Acoustical Society of America·2026

Same author

GWO-Optimized BPNN for Abrasion Resistance Prediction of Nano-SiO<sub>2</sub> and Hybrid Fiber Reinforced Geopolymer Gel Concrete.

Gels (Basel, Switzerland)·2026

Same author

Advanced nanobiosensors for the detection of neurovascular damage in cerebral infarction: prospects and challenges.

Journal of biological engineering·2026

Same author

Investigating the efficacy of propranolol in treating chronic stress combined with colorectal cancer in mice and its antitumor potential.

European journal of pharmacology·2026

Same author

Mechanistic Insight into Formation and Release Enhancement of Sulindac-Amino Acid Small-Molecule Hydrogels.

Pharmaceutical research·2026

Search research articles

Related Experiment Video

Updated: Feb 5, 2026

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks.

Yu Shi¹, Huan Gui², Qi Zhu¹

¹Dept. of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL USA.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining

|September 18, 2018

Summary

This study introduces ASPEM, a novel framework for network embedding in heterogeneous information networks (HINs). ASPEM effectively captures semantic nuances by preserving information across multiple aspects, outperforming existing methods in classification and link prediction tasks.

Keywords:

Heterogeneous information networks graph mining network embedding representation learning

More Related Videos

Author Spotlight: Exploring Acupuncture in Alzheimer's Research from Thread-Embedding Techniques to Clinical Trials

Author Spotlight: Exploring Acupuncture in Alzheimer's Research from Thread-Embedding Techniques to Clinical Trials

Published on: May 10, 2024

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Feb 5, 2026

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Assessment of Social Cognition in Non-human Primates Using a Network of Computerized Automated Learning Device ALDM Test Systems

Published on: May 5, 2015

Author Spotlight: Exploring Acupuncture in Alzheimer's Research from Thread-Embedding Techniques to Clinical Trials

Author Spotlight: Exploring Acupuncture in Alzheimer's Research from Thread-Embedding Techniques to Clinical Trials

Published on: May 10, 2024

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Data Science
Network Science
Machine Learning

Background:

Heterogeneous Information Networks (HINs) are prevalent in real-world applications.
The heterogeneity in HINs presents challenges in aligning typed edges and capturing semantic subtleties.
Network embedding is a powerful technique for learning network representations for downstream tasks.

Purpose of the Study:

To propose ASPEM, a novel embedding learning framework for HINs that preserves semantic information across multiple aspects.
To encapsulate information regarding each aspect individually within the embedding process.
To develop an unsupervised method for selecting relevant aspects for embedding.

Main Methods:

Introduced the concept of 'aspects' as units representing underlying semantic facets in HINs.
Developed the ASPEM (Aspect-aware Embedding) framework to learn network representations based on multiple aspects.
Implemented an unsupervised approach for aspect selection using dataset-wide statistics.
Evaluated ASPEM on real-world datasets for classification and link prediction tasks.

Main Results:

ASPEM effectively preserves semantic information by considering multiple aspects individually.
The proposed unsupervised aspect selection method is effective.
ASPEM demonstrates superior performance compared to baseline network embedding methods on classification and link prediction tasks.
Experimental results validate the efficacy of ASPEM on real-world datasets.

Conclusions:

ASPEM offers a robust approach to network embedding in HINs by leveraging multiple semantic aspects.
The framework's ability to handle semantic subtlety and its unsupervised aspect selection provide significant advantages.
ASPEM advances the field of network representation learning for complex, heterogeneous data.