Is community detection the same as clustering?

Is community detection the same as clustering?

Node clustering (also known as community detection in social network analysis) aims to find such a grouping (labelling) of nodes so that nodes in the same group are closer to each other rather than to the nodes from outside of the group (Malliaros & Vazirgiannis, 2013) .

What are the various kinds of community detection techniques?

Community Detection Techniques. Community detection methods can be broadly categorized into two types; Agglomerative Methods and Divisive Methods. In Agglomerative methods, edges are added one by one to a graph which only contains nodes. Edges are added from the stronger edge to the weaker edge.

What is community detection in social networks?

Community detection is an important research area in social networks analysis where we are concerned with discovering the structure of the social network. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs.

Why do community structures exist?

Importance. Community structures are quite common in real networks. Communities allow us to create a large scale map of a network since individual communities act like meta-nodes in the network which makes its study easier.

What is community detection problem?

Community Detection is one of the fundamental problems in network analysis, where the goal is to find groups of nodes that are, in some sense, more similar to each other than to the other nodes. Source: Randomized Spectral Clustering in Large-Scale Stochastic Block Models.

What is the importance of having a community structure in a community?

Community structure is an important feature of complex networks, which indicates the fact that nodes are gathered into several groups. Each group is a community of nodes where the density of edges within communities is higher than among communities (Girvan and Newman, 2002).

How is community detection similar to clustering algorithms?

One can argue that community detection is similar to clustering. Clustering is a machine learning technique in which similar data points are grouped into the same cluster based on their attributes. Even though clustering can be applied to networks, it is a broader field in unsupervised machine learning which deals with multiple attribute types.

How is community detection used in network analysis?

Community detection is very applicable in understanding and evaluating the structure of large and complex networks. This approach uses the properties of edges in graphs or networks and hence more suitable for network analysis rather than a clustering approach.

How is community detection used in machine learning?

Community detection can be used in machine learning to detect groups with similar properties and extract groups for various reasons. For example, this technique can be used to discover manipulative groups inside a social network or a stock market.

What are the differences between community detection and grouping?

Initially, every vertex belongs to a separate community, and communities are merged iteratively such that each merge is locally optimal (i.e. yields the largest increase in the current value of modularity). The algorithm stops when it is not possible to increase the modularity any more, so it gives you a grouping as well as a dendrogram.