Graph algorithms | Network theory | Link analysis
In network theory, link prediction is the problem of predicting the existence of a link between two entities in a network. Examples of link prediction include predicting friendship links among users in a social network, predicting co-authorship links in a citation network, and predicting interactions between genes and proteins in a biological network. Link prediction can also have a temporal aspect, where, given a snapshot of the set of links at time , the goal is to predict the links at time .Link prediction is widely applicable. In e-commerce, link prediction is often a subtask for recommending items to users. In the curation of citation databases, it can be used for record deduplication. In bioinformatics, it has been used to predict protein-protein interactions (PPI). It is also used to identify hidden groups of terrorists and criminals in security related applications. (Wikipedia).
Network Analysis. Lecture 18. Link prediction.
Link prediction problem. Proximity measures. Scoring algorithms. Prediction by supervised learning. Performance evaluation. Lecture slides: http://www.leonidzhukov.net/hse/2015/networks/lectures/lecture18.pdf
From playlist Structural Analysis and Visualization of Networks.
CODE: GRAPH Link Prediction w/ DGL on Pytorch and PyG Code Example | GraphML | GNN
For Graph ML we make a deep dive to code LINK Prediction on Graph Data sets with DGL and PyG. We examine the main ideas behind LINK Prediction and how to code a link prediction example in PyG and DGL - Deep Graph Library. DGL - Easy Deep Learning on Graphs with framework agnostic coding (e
From playlist Node & Edge Classification, Link Prediction w/ GraphML
VISUAL Code: Graph LINK PREDICTION | How AI Recommender work | Graph ML
HOW does LINK PREDICTION work in recommender systems for graph neural networks? Is it the intelligent code in GRAPH ML? Or is the secret of node embedding and link prediction somewhere else? In the topological structure of the computational graph? Explore the secret of why link prediction
From playlist Node & Edge Classification, Link Prediction w/ GraphML
Adding Connections on LinkedIn
In this video, you’ll learn how to add connections on LinkedIn. Visit https://edu.gcfglobal.org/en/linkedin/adding-connections-on-linkedin/1/ for our text-based lesson. We hope you enjoy!
From playlist LinkedIn
In this video, you’ll learn about how links function in HTML. We hope you enjoy! To learn more, check out our Basic HTML tutorial here: https://edu.gcfglobal.org/en/basic-html/ #html #links #coding
From playlist HTML
What are Connected Graphs? | Graph Theory
What is a connected graph in graph theory? That is the subject of today's math lesson! A connected graph is a graph in which every pair of vertices is connected, which means there exists a path in the graph with those vertices as endpoints. We can think of it this way: if, by traveling acr
From playlist Graph Theory
How to Make Predictions in Regression Analysis
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys How to Make Predictions in Regression Analysis
From playlist Statistics
GNN - Comprehensive Evaluation in Link Prediction
More info + to join: https://community.ai.science/comprehensive-evaluations-for-link-prediction
From playlist Mega Meetup VIII
NEURAL Bellman-Ford NETWORK - 2022 Neural BFNet - Graph Neural Networks w/ Link Prediction AI
Neural Bellman-Ford Networks - A brand-new representation learning framework based on paths for link prediction: A. representation of a pair of nodes as the generalized sum of all path representations between the nodes, B. with each path representation as the generalized product of the e
From playlist Learn Graph Neural Networks: code, examples and theory
CS224W: Machine Learning with Graphs | 2021 | Lecture 8.2 - Training Graph Neural Networks
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3mgWjr5 Jure Leskovec Computer Science, PhD Now that we have discussed methods for augmenting graphs to improve graph representations, we will talk about methods f
From playlist Stanford CS224W: Machine Learning with Graphs
Stanford CS224N NLP with Deep Learning | Winter 2021 | Lecture 15 - Add Knowledge to Language Models
For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/31fNyFN To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course
From playlist Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2021
AQA GCSE Biology Paper 2 | 2023 Exam Predictions | Combined and Separate Science | 9th June 2023
I want to help you achieve the grades you (and I) know you are capable of; these grades are the stepping stone to your future. Even if you don't want to study science or maths further, the grades you get now will open doors in the future. Tutoring - We can match you with an experienced t
AQA GCSE Biology Paper 1 | 2023 Exam Predictions | Combined and Separate Science | 16th May 2023
I want to help you achieve the grades you (and I) know you are capable of; these grades are the stepping stone to your future. Even if you don't want to study science or maths further, the grades you get now will open doors in the future. Tutoring - We can match you with an experienced t
C++ cache locality and branch predictability
Cache me outside, how bout that? People always talk about Big O time for analyzing speed, but Big O isn't the only important factor in writing performant code. Two important things to keep in mind are cache locality (locality of reference) and branch predictability. In this video, we go o
From playlist C/C++