In this paper we propose an algorithm, which is an improvement of identification of web communities by [1], to extract research communities from bibliography data. Web graph is huge graph structure consisting nodes and edges, which represent web pages and hyperlinks. An web community is considered to be a set of web pages holding a common topic, in other words, it is a dense subgraph of web graph. Such subgraphs obtained by the max-flow algorithm [1] are called max-flow communities. We then improve this algorithm by introducing the strategy for selection of community nodes. The effectiveness of our improvement is shown by experiments on finding research communities from CiteSeer bibliography data. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Horiike, T., Takahashi, Y., Kuboyama, T., & Sakamoto, H. (2009). Extracting research communities by improved maximum flow algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5712 LNAI, pp. 472–479). https://doi.org/10.1007/978-3-642-04592-9_59
Mendeley helps you to discover research relevant for your work.