What is community network detection?

What is community network detection?

Community detection, also called graph partition, helps us to reveal the hidden relations among the nodes in the network. Many algorithms have been developed to detect communities (Clauset et al., 2004; Girvan and Newman, 2002; Lancichinetti and Fortunato, 2009).

How does Igraph community detection work?

igraph 0.6 will also include the state-of-the-art Infomap community detection algorithm, which is based on information theoretic principles; it tries to build a grouping which provides the shortest description length for a random walk on the graph, where the description length is measured by the expected number of bits …

What is Spinglass algorithm?

Spinglass Algorithm. This algorithm by Reichardt and Bornholdt [31] is an optimization method relying on an analogy between the statistical mechanics of complex networks and physical spin glass models.

How can communities be detected?

Communities can be found in many types of networks. They show up in social networks (cliques, common interests, “old boy” networks), biological networks (functional groupings in metabolic networks1), and even academic structures like research citations. In some networks, communities are easy to spot visually.

Is there a weighted network community detection algorithm?

At present, community detection methods are mostly focused on the investigation at unweighted networks. However, real-world networks are always complex, and unweighted networks are not sufficient to reflect the connections among real-world objects. Hence, this paper proposes a community detection algorithm based on a deep sparse autoencoder.

How to use weighted modularity for community detection?

We propose a community detection approach based on a community quality metric, named as weighted modularity. We validate the performance of the proposed method in several benchmark networks and show that the proposed method is promising in different settings. 1. Introduction

Are there any algorithms to detect overlapping communities?

Most of the existing algorithms for community detection aim at providing a hard-partition of the network in which any node should belong to only one community. They are unable to detect overlapping communities in which a node is allowed to belong to multiple communities.

How does community detection affect the accuracy of community detection?

The traditional community detection algorithm only considers the connections among nodes and neighbors but ignores the connections among nodes and second-order neighbors, which affects the accuracy of community detection. In a real social network, a friend of person’s friends is more likely to be his friend than are other person.