| 摘要 |
Technologies for automatic graph partitioning include a computing device that approximates a vertex centrality weight for each vertex of a graph and then approximates, based on the approximate vertex centrality weight, an approximate edge centrality value for each edge of the graph. The computing device may repeatedly delete an edge having the highest edge centrality value and test if the graph has been disconnected. If the graph is disconnected, the computing device calculates a cluster quality metric. If the cluster quality does not decrease, the computing device realizes a new clustering of the graph based on the disconnected partitions. If the cluster quality metric decreases, the computing device reintroduces a deleted edge. The computing device recalculates the approximate vertex centrality weights and edge centrality values after reintroducing a deleted edge, deleting a predefined number of edges, or realizing a new clustering. Other embodiments are described and claimed. |
