Multimodule-Based Dynamic Community Detection for Enhancing Innovation Performance in Crowdsourcing Contests

Crowdsourcing contests have become an important method for individuals and organizations to solve complex problems by obtaining innovative solutions from external participants. As the number of participants continues to grow, the likelihood of undesirable outcomes increases, posing a great requirement for effective community detection algorithms. To provide platform owners with actionable and timely management strategies, this article proposes a multimodule-based dynamic community detection (MDCD) algorithm to facilitate the achievement of efficient, high-quality, and sustainable innovation. The MDCD algorithm uses a multimodule task learning framework containing four different modules, including heterogeneous temporal aggregation (HTA), representation reconstruction (RR), link prediction (LP), and node clustering (NC) modules, to gradually detect the community structure accurately. First, the HTA module obtains the initial node representation by capturing both spatial heterogeneity and temporal dependencies. Second, the RR module considers reconstructed topology and node attribute information to update the node representation via an encoder–decoder collaboration mechanism. Third, the LP module further optimizes the node representation by exploiting the predicted graph links, which helps increase the accuracy of community detection. Finally, the NC module leverages two metric learning methods to optimize a learnable clustering process based on the predicted node presentations, which helps platform owners achieve comprehensive results across multiple dimensions of innovation performance. The experimental results from real-world crowdsourcing platforms indicate that MDCD shows effectiveness in simultaneously improving the multidimensional innovation performance of crowdsourcing platforms and increasing solver engagement.