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Everything about Clustering coefficient
Measure of how connected and clustered a node is in its graph
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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Clustering coefficientA 5-minute orientation
- Skim a recent paper: Finding compact and well-separated clusters: Clustering using silhouette coefficientsInfluential work since 2022
- Pick up a classic book: Communications, Signal Processing, and SystemsThe most-reprinted book on the subject
- Read the landmark paper: Generalizations of the clustering coefficient to weighted complex networksCited 591 times
- Go sideways: Erdős–Rényi modelA closely related topic
- Go sideways: Betweenness centralityA closely related topic
📖 Definitions
Noun A quantitative measure of how often the nodes of a graph cluster together, defined as the ratio of the number of links among the nodes to the maximum possible number of links.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Generalizations of the clustering coefficient to weighted complex networks
Identification of Essential Proteins Based on Edge Clustering Coefficient
Network clustering coefficient without degree-correlation biases
Clustering coefficient and community structure of bipartite networks
Approximating Clustering Coefficient and Transitivity
Link prediction with node clustering coefficient
Feature Selection via Correlation Coefficient Clustering
Coefficient Beta and Hierarchical Item Clustering
🆕 Influential since 2022
Finding compact and well-separated clusters: Clustering using silhouette coefficients
Multi-agent reinforcement learning clustering algorithm based on silhouette coefficient
KM-MIC: An improved maximum information coefficient based on K-Medoids clustering
Partition Coefficient and Partition Entropy in Fuzzy C Means Clustering
Rigorizing the use of the coefficient of variation to diagnose fracture periodicity and clustering
📚 Books worth reading
Communications, Signal Processing, and SystemsQilian Liang · 2012
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Medical image analysisAtam P. Dhawan · 2010
Treaty Occupationay ToxicologySuelen Queiroz · 2010- 📕Research DesignMarjo Hoefnagels, Keith Stribley · 2017
Data science from scratchJoel Grus · 2015
Food security, poverty, and nutrition policy analysisSuresh Chandra Babu, Prabuddha Sanyal · 2009
Logistic regressionScott W. Menard · 2009
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
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