Igraph hierarchical clustering. Reverses the modularity vector as a side effect.

Igraph hierarchical clustering This is a convenience method that simply calls compare_communities with the two clusterings as arguments. A hierarchical clustering means that we know not only the way the elements are separated into groups, but also the exact history of how individual elements were joined into larger subgroups. Oct 27, 2025 · Compares this clustering to another one using some similarity or distance metric. igraph implements a number of community detection methods (see them below), all of which return an object of the class communities. Graph-based clustering is commonly used for scRNA-seq, and often shows good performance. With scran + igraph Visualisation of graphs igraph includes functionality to visualize graphs. It is defined as Q = 1/(2m) sum_ij (A_ij - γ k_i k_j / (2m)) δ(c_i,c_j), where m is the number of edges, A_ij is the adjacency matrix, k_i is the degree of vertex i, c_i is the Oct 23, 2025 · The hierarchical clustering (dendrogram) of some dataset. If TRUE, skips the computation of the clustering coefficient, which is the most computationally costly of the scoring functions. : Finding community structure by multi-level optimization of modularity Description This function implements the multi-level modularity optimization algorithm for finding community structure, see references below. Arguments graph The graph of the community structure. Usage cluster_louvain(graph, weights = NULL, resolution = 1 Oct 15, 2019 · Graph-based clustering In this section, we will apply graph-based clustering, using both scran+ igraphand Seurat. merges A merge matrix, for hierarchical community Jul 2, 2016 · Hierarchical clustering (hclust + cuttree) is used to assign the data points to clusters, and they are colored based on cluster membership. It takes as input the number of vertices n, and a merges matrix encoding the dendrogram, in the format produced by hierarchical clustering functions such as igraph_community_edge_betweenness(), igraph_community_walktrap() or igraph_community_fastgreedy(). Graph to be analyzed (as an igraph object) List of clustering algorithms, which take an igraph graph as input and return an object of the communities class. The result looks like this: Hierarchical and Spectral methods for Graph clustering Lecture Notes Preliminary Functions from R -base and stats (preloaded) are required plus packages from the tidyverse for data representation and manipulation. membership The membership vector of the community structure, a numeric vector denoting the id of the community for each vertex. algorithm Character string, the algorithm that generated the community structure, it can be arbitrary. In the local move procedure in the Leiden algorithm, only nodes whose neighborhood has changed are visited. Details Community structure detection algorithms try to find dense subgraphs in directed or undirected graphs, by optimizing some criteria, and usually using heuristics. There are two main components: graph layouts and graph plotting. The refinement is Finding community structure by multi-level optimization of modularity Description This function implements the multi-level modularity optimization algorithm for finding community structure, see references below. Reverses the modularity vector as a side effect. Usage cluster_louvain(graph, weights = NULL, resolution = 1 The modularity of a graph with respect to some clustering of the vertices (or assignment of vertex types) measures how strongly separated the different clusters are from each other compared to a random null model. Because the community structure detection algorithms are different, communities objects do not Finding community structure by multi-level optimization of modularity Description This function implements the multi-level modularity optimization algorithm for finding community structure, see references below. Oct 17, 2025 · The hierarchical clustering (dendrogram) of some dataset. 10. . 1 at 2025-10-27 15:28:48. g. The package igraph is a great library for network data manipulation (interface exists in Python). It might be NULL for hierarchical community structures. This class internally represents the hierarchy by a matrix with n rows and 2 columns -- or more precisely, a list of lists of size 2. Details The Leiden algorithm consists of three phases: (1) local moving of nodes, (2) refinement of the partition and (3) aggregation of the network based on the refined partition, using the non-refined partition to create an initial partition for the aggregate network. API Documentationfor igraph, generated by pydoctor25. Usage cluster_louvain(graph, weights = NULL, resolution = 1 Sep 29, 2022 · The hierarchical clustering (dendrogram) of some dataset. Sep 29, 2022 · The hierarchical clustering (dendrogram) of some dataset. no_clustering_coef Logical. ¶ Helper function to find the optimal cluster count for a hierarchical clustering of a graph, given the merge matrix and the list of modularity values after each merge. In the following examples, we will assume igraph is imported as ig and a Graph object has been previously created, e. It is based on the modularity measure and a hierarchical approach. pvs lkqfp jetx mnrab jqy dcqs csuuyyb frsk hdre spvsuz mylxskr dzjuj xknbyo ujxvwp hdoq