A community detection (CD) method is usually evaluated by what extent it is able to discover the 'ground-truth' community structure of a network. A certain 'node-centric metadata' is used to define the ground-truth partition. However, nodes in real networks often have multiple metadata types (e.g., occupation, location); each can potentially form a ground-truth partition. Our experiment with 10 CD methods on 5 datasets (having multiple metadata-based ground-truth partitions) show that the metadata-based evaluation is misleading because there is no single CD method that can outperform others by detecting all types of metadata-based partitions. We further show that the community structure obtained from the CD methods is usually topologically stronger than any metadata-based partitions. Finally, we suggest a new task-based evaluation framework for CD methods and show that a certain type of CD methods is useful for a certain type of task.
|Title of host publication||The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||2|
|Publication status||Published - 2018 Apr 23|
|Event||27th International World Wide Web, WWW 2018 - Lyon, France|
Duration: 2018 Apr 23 → 2018 Apr 27
|Name||The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018|
|Conference||27th International World Wide Web, WWW 2018|
|Period||18/4/23 → 18/4/27|
Bibliographical noteFunding Information:
ACKNOWLEDGMENTS The work was partially supported by the Ramanujan Fellowship, SERB-DST, Govt. of India. REFERENCES
© 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications