Centrality in Collaboration: A Novel Algorithm for Social Partitioning Gradients in Community Detection for Multiple Oncology Clinical Trial Enrollments

TL;DR

In the detection of oncologist working groups, the present study evaluates three community detection algorithms: Girvan-Newman, Louvain and an algorithm developed by the author that lays the groundwork for future subgroup analysis of clustered interventions.

Abstract

Patients at a comprehensive cancer center who do not achieve cure or remission following standard treatments often become candidates for clinical trials. Patients who participate in a clinical trial may be suitable for other studies. A key factor influencing patient enrollment in subsequent clinical trials is the structured collaboration between oncologists and most responsible physicians. Possible identification of these collaboration networks can be achieved through the analysis of patient movements between clinical trial intervention types with social network analysis and community detection algorithms. In the detection of oncologist working groups, the present study evaluates three community detection algorithms: Girvan-Newman, Louvain and an algorithm developed by the author. Girvan-Newman identifies each intervention as their own community, while Louvain groups interventions in a manner that is difficult to interpret. In contrast, the author's algorithm groups interventions in a way that is both intuitive and informative, with a gradient evident in social partitioning that is particularly useful for epidemiological research. This lays the groundwork for future subgroup analysis of clustered interventions.

Figures (10)

• Figure 1: Two simple graphs with directed and undirected edges. Direction is noted by arrowheads at the end of the edges.
• Figure 2: Patient movement between clinical trials classified by intervention type at PM. Nodes indicate the treatment type, and labeled edges indicate the movement (subsequent enrollment) of patients between clinical trials in a given intervention of the same type (self loop), or differing. Among the clinical trials, some interventions can be classified into broader categories consisting of targeted therapies or immunotherapy. This has been identified in the data with "T:" and "I:" prefixes respectively.
• Figure 3: A simple network demonstrating an edge with a high edge-betweenness centrality, highlighted in red. The network consists of two densely connected clusters, with the red edge serving as the sole connection between them. This edge is crucial for communication between the two clusters, as most of the shortest paths that connect nodes from opposite clusters pass through it.
• Figure 4: Reproduced illustration of the Louvain algorithm (originally designed by Blondel et al (2008)).
• Figure 5: A simple network highlighting node degree. The center node (colored red) possesses the highest number of connections and as a result possesses the highest degree and degree centrality index.