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,...

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

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...

Social Exchange Theory

Social Exchange Theory

We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Observational Learning

Observational Learning

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...

You might also read

Related Articles

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

Sort by

Same author

The bZIP54 (GBF2)-SARD1 module regulates salicylic acid-mediated resistance to Pst DC3000 in Arabidopsis.

Plant physiology·2026

Same author

CRISPR/Cas9-based knockout of BnaLYK compromises pattern-triggered immunity and resistance to Sclerotinia sclerotiorum in Brassica napus.

BMC plant biology·2026

Same author

Dynamic range extension of a Shack-Hartmann wavefront sensor based on an image processing and sorting method.

Applied optics·2026

Same author

Pm6 from Triticum timopheevii encodes an NLR receptor that directly recognizes AvrPm6 to confer powdery mildew resistance in wheat.

Molecular plant·2025

Same author

Tetrastichus brontispae exploits host-derived On-miR-281-3p to suppress the phenoloxidase pathway and facilitate its development.

Pest management science·2025

Same author

Phase response measurement and dynamic distortion correction of a spatial light modulator using the Shack-Hartmann wavefront sensor.

Optics letters·2025

Search research articles

Related Experiment Video

Updated: Jun 19, 2025

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

Social Network Forensics Analysis Model Based on Network Representation Learning.

Kuo Zhao^1,2,3, Huajian Zhang¹, Jiaxin Li¹

¹School of Intelligent Systems Science and Engineering, Jinan University, Zhuhai 519070, China.

Entropy (Basel, Switzerland)

|July 26, 2024

Summary

This summary is machine-generated.

This study presents a Social Network Forensic Analysis model using network representation learning to identify key figures in criminal networks. The model enhances relational analysis for combating complex criminal activities.

Keywords:

gradient update hierarchical clustering network representation learning node vectorization node2vec algorithm social network forensics

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Jun 19, 2025

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

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Computer Science
Criminology
Data Mining

Background:

Massive data generation from digital communications necessitates advanced forensic analysis.
Identifying key figures and leadership structures in criminal networks is crucial for law enforcement.

Purpose of the Study:

To introduce a Social Network Forensic Analysis (SNFA) model for identifying and analyzing key figures in criminal networks.
To leverage network representation learning for improved relational analysis in forensic investigations.

Main Methods:

Integrated traditional web forensics with community algorithms and network representation learning (Deepwalk, Line, Node2vec).
Employed modified random walk sampling (BFS, DFS) and Continuous Bag-of-Words with Hierarchical Softmax for node vectorization.
Utilized hierarchical clustering with cosine and Euclidean distance measures to determine node influence and hierarchy.

Main Results:

Successfully vectorized criminal network nodes while preserving essential features and structural information.
Achieved enhanced precision in inter-node relationship analysis within criminal networks.
Optimized clustering for accurate identification of key figures and leadership structures.

Conclusions:

The SNFA model effectively identifies key figures and leadership in criminal networks.
The model improves relational analysis accuracy, offering advanced tools for combating complex criminal activities.
Network representation learning provides a robust framework for digital forensic investigations.