## 4 Results  
### Social Network Analysis  
The visualization of the doctor affiliation network in online health communities is presented in Figure 1. Each node in the network represents a doctor, and the edges between nodes indicate that these doctors belong to the same hospital. Doctor nodes for depression, leukemia, and diabetes are rendered in orange, green, and purple, respectively. Relevant network indicators are shown in Tables 1 and 2.  
The average degree indicates the average number of edges connected to each node (doctor); the number of connected components represents the number of independent subnetworks in the network (typically corresponding to different hospitals); the modularity index measures the quality of network division into different modules (hospitals), with higher values indicating clearer divisions; graph density measures the ratio of actual existing edges to potentially existing edges in the network.  
The results in Figure 2 and Table 1 demonstrate that under different disease-type classifications, the depression doctor network exhibits the highest average degree (17.378) and a relatively high graph density (0.007), indicating extremely dense connections among doctors within the same hospital in the depression network. This may reflect that medical teams related to depression collaborate more closely within hospitals, or that the hospitals involved have a higher number of depression specialist doctors with a more concentrated distribution. The graph density of all disease types remains low (0.002-0.007), showing that although internal connections are dense, the proportion of edges in the overall network is still minimal. The diabetes doctor network possesses the highest number of connected components (1240) and modularity index (0.982), indicating that diabetes doctors are distributed across most hospitals, with extremely tight internal connections and almost no cross-hospital connections. In comparison, the connected component numbers for depression (720) and leukemia (371) doctor networks are relatively fewer, implying that doctors in these disease types are concentrated in fewer hospitals, with equally dense internal connections.  
In the visualization analysis, using Gephi to plot the network topology of high-recommendation-rate doctors (top 20%), we discovered that high-recommendation-rate depression doctors are primarily concentrated in a few core medical institutions (such as Beijing Anding Hospital and Shanghai Mental Health Center), forming dense cross-departmental subnetworks. In contrast, high-recommendation-rate diabetes doctors are scattered across multiple tertiary hospitals, relying on the integration of internal resources within each hospital.  
The results in Figure 3 and Table 2 reveal distinctive network characteristics across professional title categories. Chief doctors exhibited the highest average degree (9.353), indicating approximately 9 connections per chief doctor within each hospital, suggesting dense internal professional networks. Conversely, resident doctors demonstrated the lowest average degree (0.804), implying minimal interconnectivity within hospitals. Connected component analysis revealed hospital distribution: chief and deputy chief doctors were distributed across approximately 950 hospitals, attending doctors across 682 hospitals, and resident doctors across 209 hospitals. All professional title categories demonstrated high modularity indices (>0.94), indicating clear network partitioning with highly dense internal hospital connections and minimal inter-hospital linkages. The overall graph density remained low (0.002-0.003), reflecting the minimal proportion of potential connections  
Figure 2: Subnetworks of Doctors by Different Disease Types. (**a**) Depression Doctor Subnetwork; (**b**) Leukemia Doctors Subnetwork; (**c**) Diabetes Doctors Subnetwork.  
realized within the network. This predominantly resulted from the numerous independent hospitals, leading to inherently low network connectivity.