Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Associative Learning01:27

Associative Learning

982
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...
982
Higher Mental Functions of Brain: Learning and Memory01:26

Higher Mental Functions of Brain: Learning and Memory

1.8K
Memory is one of the most vital higher mental functions of the brain. Memory is closely related to learning because it enables us to retain information and experiences from our past to use them in our present life. It also helps us to remember facts, events, and skills, such as riding a bike or swimming. There are two types of memory — declarative memory, which involves memorizing facts or events, and procedural memory, which enables us to remember how to do something like writing or...
1.8K
Neural Circuits01:25

Neural Circuits

2.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.4K
Observational Learning01:12

Observational Learning

709
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
709
Introduction to Learning01:18

Introduction to Learning

760
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
760
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

407
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...
407

You might also read

Related Articles

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

Sort by
Same author

Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.

Chemical reviews·2026
Same author

Embedding social determinants in mHealth for pediatric oncology: co-designing a patient-centred tool for febrile neutropenia in resource-limited settings.

International journal for equity in health·2025
Same author

Mixture of checkpoint experts for explainable seizure detection using wearable devices.

Scientific reports·2025
Same author

Social and economic predictors of under-five stunting in Mexico: a comprehensive approach through the XGB model.

Journal of global health·2025
Same author

Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations.

Frontiers in big data·2024
Same author

A community focused approach toward making healthy and affordable daily diet recommendations.

Frontiers in big data·2023
Same journal

Explainable Machine Learning-Based Prediction of Postoperative Hypoxemia in Elderly Patients Undergoing General Anesthesia.

Big data·2026
Same journal

Big Data-Driven Video Anomaly Detection Using VideoMAE for Visual Analytics in CCTV Surveillance.

Big data·2026
Same journal

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.

Big data·2026
Same journal

Personalized Driven Instruction Through Explainable Agentic AI in Multicultural Higher Education Environments.

Big data·2026
Same journal

Big Data-Driven Explainable Agentic AI Decision Frameworks for Enterprise Innovation in FinTech Ecosystems.

Big data·2026
Same journal

An Edge-Enabled Low-Latency Cross-Lingual Speech-to-Text Framework for Efficient Human-Robot Interaction.

Big data·2026
See all related articles

Related Experiment Video

Updated: Dec 11, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

421

HONEM: Learning Embedding for Higher Order Networks.

Mandana Saebi1, Giovanni Luca Ciampaglia2, Lance M Kaplan3

  • 1Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, Indiana, USA.

Big Data
|August 22, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces Higher Order Network Embedding (HONEM), a novel method for network representation learning. HONEM effectively captures complex, non-Markovian dependencies, improving performance on various network analysis tasks.

Keywords:
higher order networknetwork embeddingnetwork representation learning

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

886
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K

Related Experiment Videos

Last Updated: Dec 11, 2025

Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

421
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

886
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.5K

Area of Science:

  • Network science
  • Machine learning
  • Data mining

Background:

  • Representation learning on networks simplifies manual feature engineering.
  • Existing methods use first-order networks, missing higher-order dependencies.
  • This limitation can lead to inaccurate network representations and poor task performance.

Purpose of the Study:

  • To develop a novel embedding method for higher-order networks (HONs).
  • To address the limitations of first-order network embedding methods.
  • To capture non-Markovian higher-order dependencies within network structures.

Main Methods:

  • Introduced Higher Order Network Embedding (HONEM).
  • Designed HONEM specifically for higher-order network structures.
  • Evaluated HONEM against state-of-the-art methods.

Main Results:

  • HONEM effectively captures non-Markovian higher-order dependencies.
  • Demonstrated superior performance in node classification and network reconstruction.
  • Achieved better results in link prediction and network visualization.

Conclusions:

  • HONEM offers a significant advancement in network representation learning.
  • The method accurately represents underlying phenomena in complex networks.
  • HONEM enhances performance across diverse network analysis tasks.