Paper detail

Nodal domain partition and the number of communities in networks

It is difficult to detect and evaluate the number of communities in complex networks, especially when the situation involves with an ambiguous boundary between the inner- and inter-community densities. In this paper, Discrete Nodal Domain Theory could be used to provide a criterion to determine how many communities a network would have and how to partition these communities by means of the topological structure and geometric characterization. By capturing the signs of certain Laplacian eigenvectors we can separate the network into several reasonable clusters. The method leads to a fast and effective algorithm with application to a variety of real networks data sets.

preprint2012arXivOpen access

Signal facts

What is known right now

Open access3 authors2 topics

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.