In network science, a biased random walk on a graph is a time path process in which an evolving variable jumps from its current state to one of various potential new states; unlike in a pure random walk, the probabilities of the potential new states are unequal. Biased random walks on a graph provide an approach for the structural analysis of undirected graphs in order to extract their symmetries when the network is too complex or when it is not large enough to be analyzed by statistical methods. The concept of biased random walks on a graph has attracted the attention of many researchers and data companies over the past decade especially in the transportation and social networks. (Wikipedia).
Brain Teasers: 12. A simple symmetric random walk
Very easy exercise about the first moments of a symmetric random walk.
From playlist Brain Teasers and Quant Interviews
What is a Walk? | Graph Theory
What is a walk in the context of graph theory? That is the subject of today's math lesson! A walk in a graph G can be thought of as a way of moving through G, where you start at any vertex in the graph, and then move to other vertices through the edges in the graph. In a walk, you are allo
From playlist Graph Theory
Laurent Massoulié : Non-backtracking spectrum of random graphs: community detection and ...
Abstract: A non-backtracking walk on a graph is a directed path such that no edge is the inverse of its preceding edge. The non-backtracking matrix of a graph is indexed by its directed edges and can be used to count non-backtracking walks of a given length. It has been used recently in th
From playlist Combinatorics
Graph Neural Networks, Session 6: DeepWalk and Node2Vec
What are Node Embeddings Overview of DeepWalk Overview of Node2vec
From playlist Graph Neural Networks (Hands-on)
Using the DFS Algorithm for Finding Long Paths in Random and... Graphs - Michael Krivelevich
Michael Krivelevich Using the DFS Algorithm for Finding Long Paths in Random and Pseudo-Random Graphs Tel Aviv University September 23, 2013 For more videos, visit http://video.ias.edu
From playlist Mathematics
Cobra Walks by Rajmohan Rajaraman
Games, Epidemics and Behavior URL: http://www.icts.res.in/discussion_meeting/geb2016/ DATES: Monday 27 Jun, 2016 - Friday 01 Jul, 2016 VENUE : Madhava lecture hall, ICTS Bangalore DESCRIPTION: The two main goals of this Discussion Meeting are: 1. To explore the foundations of policy d
From playlist Games, Epidemics and Behavior
"Algorithms" is Not a Four-Letter Word (Jamis Buck)
Why does the word "algorithms" convey such a sense of musty dustiness? It doesn't have to! Implementing algorithms can be a fantastic way to grow your craft, practice programming idioms and patters, learn new programming languages, and just generally have a good time! Come learn how to gen
From playlist Ruby Conference 2011
Statistics: Ch 4 Probability in Statistics (10 of 74) Random Walk: Average Displacement
Visit http://ilectureonline.com for more math and science lectures! To donate: http://www.ilectureonline.com/donate https://www.patreon.com/user?u=3236071 We learned from previous video each of the random walks is the average displacement is SQRT(n)=3.16, where n=number of tosses. Next
From playlist STATISTICS CH 4 STATISTICS IN PROBABILITY
Graph Theory: 20. Edge Weighted Shortest Path Problem
This video explains the problem known as the edge-weighted shortest path problem. The next two videos look at an algorithm which provides a solution to the problem. --An introduction to Graph Theory by Dr. Sarada Herke. For quick videos about Math tips and useful facts, check out my othe
From playlist Graph Theory part-4
Gradient descent, how neural networks learn | Chapter 2, Deep learning
Enjoy these videos? Consider sharing one or two. Help fund future projects: https://www.patreon.com/3blue1brown Special thanks to these supporters: http://3b1b.co/nn2-thanks Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks This video was support
From playlist Data Science
From playlist M. Graph Theory
Rumours, consensus and epidemics on networks (Lecture 2) by A Ganesh
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 r
From playlist Advances in Applied Probability 2019
Ravi Kumar - Random Walks and Graph Properties
https://indico.math.cnrs.fr/event/3475/attachments/2180/2572/Kumar_GomaxSlides.pdf
From playlist Google matrix: fundamentals, applications and beyond
Networks: Part 6 - Oxford Mathematics 4th Year Student Lecture
Network Science provides generic tools to model and analyse systems in a broad range of disciplines, including biology, computer science and sociology. This course (we are showing the whole course over the next few weeks) aims at providing an introduction to this interdisciplinary field o
From playlist Oxford Mathematics Student Lectures - Networks
Search games and Optimal Kakeya Sets - Yuval Peres
Yuval Peres Microsoft Research April 28, 2014 A planar set that contains a unit segment in every direction is called a Kakeya set. These sets have been studied intensively in geometric measure theory and harmonic analysis since the work of Besicovich (1919); we find a new connection to gam
From playlist Mathematics
Node2vec : TensorFlow + KERAS code in live COLAB | Graph NN 2022
Real-time COLAB to learn Node2vec for Graph representation learning in KERAS implementation for learning low-dimensional embeddings of nodes in a graph, w/ neighborhood-preserving objective. Download your COLAB: https://colab.research.google.com/github/keras-team/keras-io/blob/master/exa
From playlist Word2Vec and Node2vec (pure TensorFlow 2.7 + KERAS)
9 3 Random Contraction Algorithm 9 min
From playlist Algorithms 1