LAA - Inductive Community Detection Algorithm Based on Label Aggregation.

The research task of discovering nodes sharing the same attributes and dense connection is community detection, which has been proved to be a useful tool for network analysis. However, the existing approaches are transductive, even for original networks with structures or attributes changed, retraining was required to get the results. The rapid changes and explosive growth of information makes real-world application have great expectations for inductive community detection models that can quickly obtain results. In this paper, we proposed Label Aggregation Algorithm (LAA), an inductive community detection algorithm based on label aggregation. Like the traditional label propagation algorithm, LAA uses labels to indicate the community to which the node belongs. The difference is that LAA takes the advantages of network representation learning's ability for information aggregation to generate nodes' final labels by aggregating the labels propagated from local neighbors. The experimental results show that LAA has excellent generalization capabilities to handle overlapping community detection task.