Unit Shortest Path Algorithm with Maximum Adjacency Point Storage

In network analysis, we can implement the unit shortest path algorithm with maximum adjacency point storage. This approach leverages the maximum number of adjacent nodes per node (termed the network's maximum adjacency count) to define matrix dimensions. The algorithm constructs an adjacency node matrix M-PJ by setting the maximum adjacency count as column size and total node count as row size, effectively capturing network topology. Matrix rows follow ascending node numbering, where row I contains all nodes adjacent to node I. When a node's adjacency count falls below the maximum, zero-padding completes the row. Based on M-PJ, we create an initial judgment matrix M-PDJ by populating corresponding edge weights in identical positions. The implementation typically involves nested loops for matrix initialization and Dijkstra-like iterations for path calculation. Using both matrices, we can compute shortest paths between any node pairs, providing deeper insights into network connectivity and optimal routing patterns. Key functions include matrix normalization for efficient memory usage and priority queues for path exploration.