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

The Representativeness Heuristic

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Relationship Formation

Relationship Formation

What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...

Social Proof

Social proof is a form of persuasion based on comparison and conformity. People compare their behavior and actions to what others are doing and will change to conform to do what their peers do.

In- and Out-Groups

In- and Out-Groups

People all belong to a gender, race, age, and social economic group. These groups provide a powerful source of our identity and self-esteem (Tajfel & Turner, 1979) and serve as our in-groups. An in-group is a group that we identify with or see ourselves as belonging to.

You might also read

Related Articles

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

Sort by

Same author

Inhibitory effect of 17β‑estradiol on triglyceride synthesis in skeletal muscle cells is dependent on ESR1 and not ESR2.

Molecular medicine reports·2019

Same author

Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network.

Scientific reports·2019

Same author

Tumor cell pH detection based on CdSe quantum dots' fluorescence charateristics.

Technology and health care : official journal of the European Society for Engineering and Medicine·2019

Same author

Pericardial malignant solitary fibrous tumour with right atrial invasion - a case report and literature review.

The Journal of international medical research·2019

Same author

Dexamethasone-induced Krüppel-like factor 9 expression promotes hepatic gluconeogenesis and hyperglycemia.

The Journal of clinical investigation·2019

Same author

Analysis of the Yp11.2 Deletion Region of Phenotypically Normal Males with an <i>AMELY</i>-Null Allele in the Chinese Han Population.

Genetic testing and molecular biomarkers·2019

Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026

Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026

Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026

Same journal

Exploring the Antiparasitic Activity of the Sea Cucumber Isostichopus sp. aff. badionotus From the Northern Coast of Colombia Against Trypanosoma cruzi.

TheScientificWorldJournal·2026

Same journal

Kalanchoe ceratophylla (Crassulaceae): The True Identity of Sidingin, a Medicinal Plant From Sumatra, Based on Morphological and Molecular Evidence.

TheScientificWorldJournal·2026

Same journal

Genetic Variation of Chicken Growth Differentiation Factor-9 Gene and Association With Egg Characteristics: A Systematic Review.

TheScientificWorldJournal·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 27, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

A collaborative recommend algorithm based on bipartite community.

Yuchen Fu¹, Quan Liu², Zhiming Cui²

¹Suzhou Industrial Park Institute of Services Outsourcing, Suzhou, Jiangsu 215123, China ; School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China.

Thescientificworldjournal

|June 24, 2014

Summary

This summary is machine-generated.

Recommendation algorithms using bipartite networks improve accuracy and diversity by considering network topology. This study introduces a new algorithm focusing on local network characteristics for better collaborative recommendations.

Related Experiment Videos

Last Updated: Apr 27, 2026

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

Area of Science:

Computer Science
Network Analysis
Recommender Systems

Background:

Traditional recommendation algorithms often overlook network topology, limiting accuracy and diversity.
Existing methods primarily focus on global network structures, neglecting important local characteristics.

Purpose of the Study:

To propose a novel link community partitioning algorithm based on label propagation.
To develop a collaborative recommendation algorithm leveraging bipartite communities for improved results.

Main Methods:

Implemented a label propagation-based link community partitioning algorithm.
Developed a collaborative recommendation algorithm utilizing the identified bipartite communities.
Conducted numerical experiments using benchmark and real-world datasets for validation.

Main Results:

The proposed bipartite network-based recommendation algorithm demonstrates superior performance in accuracy and diversity compared to traditional methods.
Considering local network topology significantly enhances collaborative recommendation processing.
Experimental results validate the effectiveness of the proposed algorithms.

Conclusions:

Network topology, particularly local characteristics, plays a crucial role in enhancing recommendation system performance.
The proposed label propagation and bipartite community-based algorithms offer a promising approach for improving collaborative recommendations.
This research highlights the importance of integrating network structure analysis into the design of advanced recommendation systems.