Name
Abstract | ||
|---|---|---|
In most real-world networks, nodes/vertices tend to be organized into tightly-knit modules known as communities or clusters such that nodes within a community are more likely to be connected or related to one another than they are to the rest of the network. Community detection in a network (graph) is aimed at finding a partitioning of the vertices into communities. The goodness of the partitioning is commonly measured using modularity. Maximizing modularity is an NP-complete problem. In 2008, Blondel et al. introduced a multi-phase, multi-iteration heuristic for modularity maximization called the Louvain method. Owing to its speed and ability to yield high quality communities, the Louvain method continues to be one of the most widely used tools for serial community detection. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.parco.2022.102898 | Parallel Computing |
Keywords | DocType | Volume |
Distributed community detection,Heterogeneous systems,Multi-GPU,Parallel Louvain,Parallel graph algorithms | Journal | 111 |
ISSN | Citations | PageRank |
0167-8191 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nitin Gawande | 1 | 0 | 0.34 |
| Sayan Ghosh | 2 | 17 | 8.98 |
| Mahantesh Halappanavar | 3 | 218 | 33.64 |
| Antonino Tumeo | 4 | 0 | 0.34 |
| Kalyanaraman, Ananth | 5 | 221 | 31.95 |