Sørensen Coefficient#

The Sørensen Coefficient, also called the Sørensen-Dice similarity coefficient, quantifies the similarity and overlap between two samples.

It is defined as two times the size of the set intersection divided by the sum of the size of the two sets. The value ranges from 0 to 1.

Sørensen coefficient

\[ \left(2 * |A \cap B| \right) \over \left(|A| + |B| \right) \]

In graphs, the sets refer to the set of connected nodes or neighborhood of nodes A and B.

Learn more about Sørensen Coefficient

When to use Sørensen Coefficient#

  • When you want to compare nodes with vastly different sized neighborhoods.

  • When the intersection of the node neigborhoods is more important than the overall similarity

When not to use Sørensen Coefficient#

  • In directed graphs

  • Comparing graphs with different underlying data relationships.

  • In weighted graphs, while cuGraph does have a weighted Sørensen implementation, the algorithm did not originally use weights.

How computationally expensive is it?#

While cuGraph’s parallelism mitigates run cost, Big O notation is still the standard to compare algorithm execution time. The cost to run O(n * m) where n is the number of nodes in the graph and m is the number of groups to test.

Copyright (c) 2023-2025, NVIDIA CORPORATION.

Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.