## 3 DC-SoC Consortium Chain Network Model
### Initial Clustering  
The conventional Gossip protocol employs unstructured random propagation for data transmission, potentially resulting in high latency and incomplete propagation. To address these issues, we opt to regard peers within each organization within the channel as data objects in the dataset. We then map the network latency between them as Euclidean distances in the vector space and employ clustering based on this to construct a rational network topology as a deterministic propagation structure within the organization, ensuring that each peer can complete block reception in the shortest time possible. It is important to note that clustering is conducted within each organization within the channel. Additionally, the clustering process for each organization is independent and does not impact the others. Clustering algorithms are widely used in research on contemporary blockchain networks, including their applications in social networks. For instance, [PERSON] et al. [27] described the Bitcoin transaction network as an undirected graph and employed spectral clustering to segment this network for mining interests. After conducting numerous comparisons, the paper concludes that density clustering is the most suitable method for practical applications within coalition chains. In real-world network environments, each blockchain peer operates on a physical machine, located in a distinct network location. Communication delay between peers is influenced by factors such as physical distance between machines, hardware performance, bandwidth, and more. Thus, peers can be abstractly visualized in a multidimensional vector space, with propagation delay as the Euclidean distance metric. However, the limited impact of propagation delay maintains this vector space as low-dimensional, making it unsuitable for high-dimensional clustering algorithms like spectral clustering. Alternatively, algorithms like K-Means segment peers into clusters through iterative processes. Yet, in this vector space, peer distribution often appears uniform yet irregular. As a result, creating effective propagation trees for clusters identified by algorithms like K-Means is challenging in this environment. Density clustering excels at identifying clusters of arbitrary shapes and efficiently pinpointing outliers. It facilitates identifying high-density connectivity regions, enabling efficient block distribution from the master peer to all channel peers, including leaf peers. Crucially, identifying outliers with density clustering does not imply their immediate removal. Instead, these outliers are included in analyses to mitigate their impact on network stability, due to factors like high latency or unstable connections. This approach is adopted because each peer in the blockchain typically represents a user. Therefore, removing users with poor network conditions is inappropriate, as it would compromise blockchain fairness and disrupt application usage. DBSCAN is a classic density clustering algorithm that utilizes two parameters: the radius _eps_ and the minimum number of neighboring peers _MinPts_. Starting from the initial input peers and their neighborhoods, it iterates continuously to identify core objects and their neighborhoods until the iterative process concludes. Subsequently, the first cluster is identified. If any peers remain in the peer set \(N\), the process continues with a second iteration until \(N\) is empty. However, the recursive nature of DBSCAN necessitates multiple iterations, leading to a worst-case time complexity of \(O(N^{2})\). Consequently, in large blockchain networks, DBSCAN incurs significant computational costs in creating the propagation structure, rendering any reduction in propagation latency insignificant. To address the aforementioned issue, we introduced DBSCAN++ within the framework of DC-SoC [28]. In the original DBSCAN algorithm, core objects are identified by traversing the entire peer set.