Graph Clustering Task
(Redirected from Community Detection)
A Graph Clustering Task is a Graph Analysis Task that is a Clustering Task (to discover subgraphs).
- AKA: Group Detection, Graph Decomposition.
- Context:
- Input: an Unlabeled Graph.
- output: one or more Subgraphs.
- It can be solved by a Graph Clustering System)that implements a graph clustering algorithm).
- It can range from being an Unsupervised Graph Clustering Task to being a Semi-Supervised Graph Clustering Task.
- …
- Example:
- ?? Discover whether a Graph is a Bipartite Graph.
- …
- Counter-Example(s):
- See: Partitional Clustering; Graph Clustering; Graph Mining;----
References
2011
- (Aggarwal, 2011) ⇒ Charu C. Aggarwal. (2011). “Graph Clustering.” In: (Sammut & Webb, 2011) p.459
- (Sharara & Getoor, 2011) ⇒ Hossam Sharara; Lise Getoor. (2011). “Group Detection.” In: (Sammut & Webb, 2011) p.489
2007
- (Schaeffer, 2007) ⇒ Satu Elisa Schaeffer. (2007). “Survey: Graph Clustering.” In: Computer Science Review Journal, 1(1). doi:10.1016/j.cosrev.2007.05.001
- QUOTE: Graph clustering is the task of grouping the vertices of the graph into clusters taking into consideration the edge structure of the graph in such a way that there should be many edges within each cluster and relatively few between the clusters. Graph clustering in the sense of grouping the vertices of a given input graph into clusters, which is the topic of this survey, should not be confused with the clustering of sets of graphs based on structural similarity; such clustering of graphs as well as measures of graph similarity is addressed in other literature [38,124,168,169,202,206], although many of the techniques involved are closely related to the task of finding clusters within a given graph.
As the field of graph clustering has grown quite popular and the number of published proposals for clustering algorithms as well as reported applications is high, we do not even pretend to be able to give an exhaustive survey of all the methods, but rather an explanation of the methodologies commonly applied and pointers to some of the essential publications related to each research branch
- QUOTE: Graph clustering is the task of grouping the vertices of the graph into clusters taking into consideration the edge structure of the graph in such a way that there should be many edges within each cluster and relatively few between the clusters. Graph clustering in the sense of grouping the vertices of a given input graph into clusters, which is the topic of this survey, should not be confused with the clustering of sets of graphs based on structural similarity; such clustering of graphs as well as measures of graph similarity is addressed in other literature [38,124,168,169,202,206], although many of the techniques involved are closely related to the task of finding clusters within a given graph.