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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

158
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...
158
Protein Networks02:26

Protein Networks

4.1K
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,...
4.1K
Neural Circuits01:25

Neural Circuits

1.8K
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...
1.8K
Time-Series Graph00:54

Time-Series Graph

4.6K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.6K
¹H NMR of Labile Protons: Temporal Resolution01:10

¹H NMR of Labile Protons: Temporal Resolution

1.3K
Protons bonded to heteroatoms such as nitrogen and oxygen exhibit a range of chemical shift values. This is due to the varying degree of hydrogen bonding between the proton and the heteroatom in other molecules. The extent of hydrogen bonding affects the electron density around the proton, thereby giving different chemical shift values for the protons in the proton NMR spectrum.
The –OH proton in alcohols typically appears in the range of δ 2 to 5 ppm but can vary depending on the specific...
1.3K
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

1.3K
The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Developmental manganese exposure, but not concurrent lead, alters social processing selectively in male mice.

iScience·2026
Same author

Evaluating cognitive biases in AI-assisted mammography interpretation: a simulation reader study of explainable AI across radiologist experience levels.

European radiology·2026
Same author

Exogenous microbial consortia modulate rhizosphere microbiome and yield of grafted tomato grown in the mediterranean greenhouse.

BMC plant biology·2026
Same author

Explainable Artificial Intelligence in Mammography: A Systematic Review of Methods, Evaluation Practices, and Clinical Readiness.

Diagnostics (Basel, Switzerland)·2026
Same author

Viewpoint on the Consequences and Mitigation of Cognitive Bias in the Radiological Interpretation of Breast Cancer Imaging Using Artificial Intelligence.

JMIR medical informatics·2026
Same author

Should AI results be disclosed in mammography reports? A randomised survey study of patient responses to concordant and discordant interpretations.

European radiology·2026

Related Experiment Video

Updated: Sep 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K

MODIT: MOtif DIscovery in Temporal Networks.

Roberto Grasso1,2, Giovanni Micale2, Alfredo Ferro2

  • 1Department of Physics and Astronomy, University of Catania, Catania, Italy.

Frontiers in Big Data
|March 14, 2022
PubMed
Summary
This summary is machine-generated.

We present MODIT, a new algorithm for motif discovery in temporal networks. MODIT efficiently counts motifs of any size, outperforming existing methods for complex temporal graph analysis.

Keywords:
data miningmotif countingmotif search algorithmsnetwork analysisnetwork motifstemporal networks

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.2K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.4K

Related Experiment Videos

Last Updated: Sep 30, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.1K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.2K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.4K

Area of Science:

  • Graph theory
  • Network science
  • Computational complexity

Background:

  • Temporal networks represent interactions with timestamps.
  • Motif search in temporal networks identifies recurring subgraphs respecting chronological order.
  • Existing algorithms struggle with computational complexity for large or specific motifs.

Purpose of the Study:

  • To introduce MODIT (MOtif DIscovery in Temporal Networks), an efficient algorithm for motif counting in temporal networks.
  • To address the limitations of current algorithms in handling large and complex motifs.
  • To provide a scalable solution for motif discovery in temporal network analysis.

Main Methods:

  • MODIT is inspired by the TemporalRI algorithm for temporal subgraph isomorphism.
  • The algorithm is designed for counting motifs of arbitrary size in temporal networks.
  • Experimental evaluation on medium and large-sized networks was conducted.

Main Results:

  • MODIT efficiently retrieves large motifs (more than 3 nodes and 3 edges) within hours.
  • The algorithm demonstrates superior performance compared to state-of-the-art methods.
  • MODIT shows effectiveness on various medium and large-sized temporal networks.

Conclusions:

  • MODIT offers an efficient and scalable solution for motif discovery in temporal networks.
  • The algorithm overcomes computational challenges, enabling analysis of larger motifs.
  • MODIT advances the field of temporal network analysis and motif search.