Opening book details…
Can I read Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs on EtoBox?
Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs by Jungeun Kim; Jae-Gil Lee; Sungsu Lim is a Computer Science article available to read on EtoBox.
What is Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs about?
A__multi-layer graph__consists of multiple layers of weighted graphs, where the multiple layers represent the different aspects of relationships. Considering multiple aspects (i.e., layers) together is essential to achieve a comprehensive and consolidated view. In this article, we propose a novel framework of__differential flattening__, which facilitates the analysis of multi-layer graphs, and apply this framework to community detection. Differential flattening merges multiple graphs into a single graph such that the graph structure with the maximum clustering coefficient is obtained from the single graph. It has two distinct features compared with existing approaches. First, dealing with multiple layers is done__independently__of a specific community detection algorithm, whereas previous approaches rely on a specific algorithm. Thus, any algorithm for a single graph becomes applicable to multi-layer graphs. Second, the contribution of each layer to the single graph is determined__automatically__for the maximum clustering coefficient. Since differential flattening is formulated by an optimization problem, the optimal solution is easily obtained by well-known algorithms such as inte
Who reads Differential Flattening: A Novel Framework for Community Detection in Multi-Layer Graphs?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jungeun Kim; Jae-Gil Lee; Sungsu Lim
- Publisher
- ACM
- Published
- 2016
- Language
- EN
- Field
- Computer Science (Physical Sciences)