Network theory | Equivalence (mathematics)
Similarity in network analysis occurs when two nodes (or other more elaborate structures) fall in the same equivalence class. There are three fundamental approaches to constructing measures of network similarity: structural equivalence, automorphic equivalence, and regular equivalence. There is a hierarchy of the three equivalence concepts: any set of structural equivalences are also automorphic and regular equivalences. Any set of automorphic equivalences are also regular equivalences. Not all regular equivalences are necessarily automorphic or structural; and not all automorphic equivalences are necessarily structural. (Wikipedia).
This video introduces similarity and explains how to determine if two figures are similar or not. http://mathispower4u.com
From playlist Number Sense - Decimals, Percents, and Ratios
Similarity And Congruence: Similar Triangle in Triangle (Find Smaller) (Grade 5) - Maths Revision
Topic: Similarity And Congruence: Similar Triangle in Triangle (Find Smaller) Do this paper online for free: https://www.onmaths.com/similarity-and-congruence/ Grade: 5 This question appears on calculator higher and foundation GCSE papers. Practise and revise with OnMaths. Go to onmaths.c
From playlist Similarity And Congruence
Similarity And Congruence: Similar Triangle in Triangle (Find Larger) (Grade 5) - Maths Revision
Topic: Similarity And Congruence: Similar Triangle in Triangle (Find Larger) Do this paper online for free: https://www.onmaths.com/similarity-and-congruence/ Grade: 5 This question appears on calculator higher and foundation GCSE papers. Practise and revise with OnMaths. Go to onmaths.co
From playlist Similarity And Congruence
Similarity & Dissimilarity | Introduction to Data Mining part 17
In this Data Mining Fundamentals tutorial, we introduce you to similarity and dissimilarity. Similarity is a numerical measure of how alike two data objects are, and dissimilarity is a numerical measure of how different two data objects are. We also discuss similarity and dissimilarity for
From playlist Introduction to Data Mining
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
Lecture 6. Structural properties of networks
Network Science 2021 @ HSE http://www.leonidzhukov.net/hse/2021/networks/
From playlist Network Science, 2021
What is the similarity of triangles for SSS
👉 Learn how to solve with similar triangles. Two triangles are said to be similar if the corresponding angles are congruent (equal). Note that two triangles are similar does not imply that the length of the sides are equal but the sides are proportional. Knowledge of the length of the side
From playlist Similar Triangles
Network Analysis. Lecture 7. Structural Equivalence and Assortative Mixing
Structural and regular equivalence. Similarity metrics. Correlation coefficient and cosine similarity. Assortative mixing and homophily. Modularity. Assortativity coefficient. Mixing by node degree. Assortative and disassortative networks Lecture slides: http://www.leonidzhukov.net/hse/20
From playlist Structural Analysis and Visualization of Networks.
Similarity And Congruence: Simple Similar Triangles (Find Large) (Grade 5) - GCSE Maths Revision
Topic: Similarity And Congruence: Simple Similar Triangles (Find Large) Do this paper online for free: https://www.onmaths.com/similarity-and-congruence/ Grade: 5 This question appears on calculator higher and foundation GCSE papers. Practise and revise with OnMaths. Go to onmaths.com for
From playlist Similarity And Congruence
A New Framework for Modeling Brain Information Processing - Nikolaus Kriegeskorte
Nikolaus Kriegeskorte, Programme Leader at the Medical Research Council's Cognition and Brain Sciences Unit in Cambridge, UK, describes a framework for testing such massively multivariate brain-activity data.
From playlist Wu Tsai Neurosciences Institute
Fellow Short Talks: Dr Scott Hale, Oxford University
Bio Dr Scott A. Hale is a Faculty Fellow with expertise in both the social sciences and computer science. His research focuses on knowledge discovery, data mining, and the visualization of human behaviour in three substantive areas: multilingualism and user experience, mobilization/collec
From playlist Short Talks
NJIT Data Science Seminar: Steven Skiena, Stony Brook University
NJIT Institute for Data Science https://datascience.njit.edu/ Word and Graph Embeddings for Machine Learning Steven Skiena, Ph.D. Distinguished Professor Department of Computer Science Stony Brook University Distributed word embeddings (word2vec) provides a powerful way to reduce large
From playlist Talks
Citation networks as a window to science: a case study by Remco van der Hofstad
PROGRAM: ADVANCES IN APPLIED PROBABILITY ORGANIZERS: Vivek Borkar, Sandeep Juneja, Kavita Ramanan, Devavrat Shah, and Piyush Srivastava DATE & TIME: 05 August 2019 to 17 August 2019 VENUE: Ramanujan Lecture Hall, ICTS Bangalore Applied probability has seen a revolutionary growth in resear
From playlist Advances in Applied Probability 2019
Lecturer: Dr. Erin M. Buchanan Summer 2020 https://www.patreon.com/statisticsofdoom This video is part of my human language modeling class - this video set covers the updated version with both R and Python. Last in our series of vector space models is network analysis. We briefly cover
From playlist Human Language (ANLY 540)
Unit 7 Panel: Vision and Audition
MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: https://ocw.mit.edu/RES-9-003SU15 Instructor: Josh Tenenbaum, Hynek Hermansky, Josh McDermott, Gabriek Kreiman, Dan Yemens Panelists discuss developments in the fields of vision and audition, com
From playlist MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015
Marinka Zitnik (3/31/21): Graph representation learning and its applications to biomedicine
Title: Graph representation learning and its applications to biomedicine Abstract: The success of machine learning depends heavily on the choice of representations used for prediction tasks. Graph representation learning has emerged as a predominant choice for learning representations of
From playlist AATRN 2021
Social network analysis - Introduction to structural thinking: Dr Bernie Hogan, University of Oxford
Social networks are a means to understand social structures. This has become increasingly relevant with the shift towards mediated interaction. Now we can observe and often analyse links at a scale that far outpaces what was possible only decades ago. While this prompts new methodologies,
From playlist Data science classes
Similar Triangles Using Side-Side-Side and Side-Angle-Side
This video explains how to determine if two triangles are similar using SSS and SAS. Complete Video List: http://www.mathispower4u.yolasite.com
From playlist Similarity
Deep Learning: When and How | by Mikhail Trofimov | Kaggle Days Warsaw
"Deep Learning: When and How: Mikhail Trofimov Kaggle Days Warsaw was held May 2018, and gathered over 100 participants to meet, learn and code with Kaggle Grandmasters, and compete in our traditional offline competition. Kaggle Days are a global series of offline events for seasoned dat
From playlist Kaggle Days Warsaw Edition | by LogicAI + Kaggle