Graph invariants | Network theory | Network analysis | Algebraic graph theory
In graph theory, a clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterised by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes (Holland and Leinhardt, 1971; Watts and Strogatz, 1998). Two versions of this measure exist: the global and the local. The global version was designed to give an overall indication of the clustering in the network, whereas the local gives an indication of the embeddedness of single nodes. (Wikipedia).
Clustering Coefficient - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering Coefficient Code - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering Coefficient - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Bipartite IV - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering Coefficient - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering Coefficient Quiz - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
Clustering (2): Hierarchical Agglomerative Clustering
Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.
From playlist cs273a
Randomizing Clustering Coefficient - Intro to Algorithms
This video is part of an online course, Intro to Algorithms. Check out the course here: https://www.udacity.com/course/cs215.
From playlist Introduction to Algorithms
CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jO8OsE Jure Leskovec Computer Science, PhD In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate sy
From playlist Stanford CS224W: Machine Learning with Graphs
Statistical analysis of networks - Professor Gesine Reinert, University of Oxford
Networks have become increasingly popular as representations of complex data. How can we make sense of such data? The first class will cover some network summaries and some parametric models for networks, while the second class concerns statistical inference using these network summaries a
From playlist Data science classes
Lecture 08 -Jack Simons Electronic Structure Theory- Coupled-cluster theory
Coupled-cluster (CC) theory; analogy to cluster expansion in statistical mechanics; the CC equations are quartic. (1)Jack Simons Electronic Structure Theory- Session 1- Born-Oppenheimer approximation http://www.youtube.com/watch?v=Z5cq7JpsG8I (2)Jack Simons Electronic Structure Theory
From playlist U of Utah: Jack Simons' Electronic Structure Theory course
Rémi Imbach, New York University Title: Practical Advances in Complex Roots Clustering Abstract: We are interested in computing clusters of complex roots of polynomials and square polynomials systems. Root clustering differs of root isolation in that it can be performed softly, i.e. avoi
From playlist Fall 2019 Symbolic-Numeric Computing Seminar
Cluster algebras from surfaces II: expansion formulas, good bases,... (Lecture 2) by Jon Wilson
PROGRAM :SCHOOL ON CLUSTER ALGEBRAS ORGANIZERS :Ashish Gupta and Ashish K Srivastava DATE :08 December 2018 to 22 December 2018 VENUE :Madhava Lecture Hall, ICTS Bangalore In 2000, S. Fomin and A. Zelevinsky introduced Cluster Algebras as abstractions of a combinatoro-algebra
From playlist School on Cluster Algebras 2018
Statistical inference for networks: Professor Gesine Reinert, University of Oxford
Professor Gesine Reinert, Oxford University Research interests Applied Probability, Computational Biology, and Statistics. In particular: Stein’s method, networks, word count statistics Have you heard about the phenomenon that everyone is six handshakes away from the President? The six d
From playlist Data science classes
Cluster algebras from surfaces II: expansion formula (Lecture 1) by Jon Wilson
PROGRAM :SCHOOL ON CLUSTER ALGEBRAS ORGANIZERS :Ashish Gupta and Ashish K Srivastava DATE :08 December 2018 to 22 December 2018 VENUE :Madhava Lecture Hall, ICTS Bangalore In 2000, S. Fomin and A. Zelevinsky introduced Cluster Algebras as abstractions of a combinatoro-algebra
From playlist School on Cluster Algebras 2018
Network Analysis. Lecture 4. Small world and dynamical growth models.
Barabasi-Albert model. Preferential attachment. Time evolition of node degrees. Node degree distribution. Average path length and clustering coefficient. Small world model. Watts-Strogats model. Transition from regular to random. Lecture slides: http://www.leonidzhukov.net/hse/2015/netwo
From playlist Structural Analysis and Visualization of Networks.
From playlist Clustering Algorithms
CS224W: Machine Learning with Graphs | 2021 | Lecture 14.2 - Erdos Renyi Random Graphs
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GzPg4L Jure Leskovec Computer Science, PhD We introduce the simplest model for graph generation, Erdös-Renyi graph (E-R graphs, Gnp graphs). The Gnp random graphs
From playlist Stanford CS224W: Machine Learning with Graphs