免责声明：作者是 Erlang 的新手。
想象一下，我们有一个由 1M 个节点组成的图，每个节点有 0-4 个邻居（边从每个节点发到这些邻居，所以图是有向和连通的）。
这是我选择的数据结构：
为了存储图表，我使用基于 ETS 表的 digraph。这允许快速 (O(1)) 访问节点的邻居。
对于未访问的节点列表，我使用 gb_sets:take_smallest （节点已经排序，取完后同时删除）。
对于前辈列表，我使用 dict 结构，它允许以以下方式存储前辈：{Node1,Node1_predecessor},{Node2,Node2_predecessor}。
对于访问节点的列表，我使用一个简单的列表。
问题：
- 当我尝试在 digraph 结构和 Unvisited_nodes 结构中更新节点的权重时，代码变得非常难以阅读和维护。将一个“对象”与需要在两个数据结构中同时更新的“字段”保持一致似乎不是正确的方法。这样做的正确方法是什么？
- 同样的问题是关于前辈名单的。我应该在哪里存储节点“对象”的前任“字段”？也许在图中（有向图结构）？
- 也许我应该根据过程和消息而不是对象（节点和边）及其字段（权重）重新考虑整个 Dijkstra 算法？
升级版：
这是基于 Antonakos 建议的代码：
dijkstra(Graph,Start_node_name) ->
io:format("dijkstra/2: start~n"),
Paths = dict:new(),
io:format("dijkstra/2: initialized empty Paths~n"),
Unvisited = gb_sets:new(),
io:format("dijkstra/2: initialized empty Unvisited nodes priority queue~n"),
Unvisited_nodes = gb_sets:insert({0,Start_node_name,root},Unvisited),
io:format("dijkstra/2: Added start node ~w with the weight 0 to the Unvisited nodes: ~w~n", [Start_node_name, Unvisited_nodes]),
Paths_updated = loop_through_nodes(Graph,Paths,Unvisited_nodes),
io:format("dijkstra/2: Finished searching for shortest paths: ~w~n", [Paths_updated]).
loop_through_nodes(Graph,Paths,Unvisited_nodes) ->
%% We need this condition to stop looping through the Unvisited nodes if it is empty
case gb_sets:is_empty(Unvisited_nodes) of
false ->
{{Current_weight,Current_name,Previous_node}, Unvisited_nodes_updated} = gb_sets:take_smallest(Unvisited_nodes),
case dict:is_key(Current_name,Paths) of
false ->
io:format("loop_through_nodes: Found a new smallest unvisited node ~w~n",[Current_name]),
Paths_updated = dict:store(Current_name,{Previous_node,Current_weight},Paths),
io:format("loop_through_nodes: Updated Paths: ~w~n",[Paths_updated]),
Out_edges = digraph:out_edges(Graph,Current_name),
io:format("loop_through_nodes: Ready to iterate through the out edges of node ~w: ~w~n",[Current_name,Out_edges]),
Unvisited_nodes_updated_2 = loop_through_edges(Graph,Out_edges,Paths_updated,Unvisited_nodes_updated,Current_weight),
io:format("loop_through_nodes: Looped through out edges of the node ~w and updated Unvisited nodes: ~w~n",[Current_name,Unvisited_nodes_updated_2]),
loop_through_nodes(Graph,Paths_updated,Unvisited_nodes_updated_2);
true ->
loop_through_nodes(Graph,Paths,Unvisited_nodes_updated)
end;
true ->
Paths
end.
loop_through_edges(Graph,[],Paths,Unvisited_nodes,Current_weight) ->
io:format("loop_through_edges: No more out edges ~n"),
Unvisited_nodes;
loop_through_edges(Graph,Edges,Paths,Unvisited_nodes,Current_weight) ->
io:format("loop_through_edges: Start ~n"),
[Current_edge|Rest_edges] = Edges,
{Current_edge,Current_node,Neighbour_node,Edge_weight} = digraph:edge(Graph,Current_edge),
case dict:is_key(Neighbour_node,Paths) of
false ->
io:format("loop_through_edges: Inserting new neighbour node ~w into Unvisited nodes~n",[Current_node]),
Unvisited_nodes_updated = gb_sets:insert({Current_weight+Edge_weight,Neighbour_node,Current_node},Unvisited_nodes),
io:format("loop_through_edges: The unvisited nodes are: ~w~n",[Unvisited_nodes_updated]),
loop_through_edges(Graph,Rest_edges,Paths,Unvisited_nodes_updated,Current_weight);
true ->
loop_through_edges(Graph,Rest_edges,Paths,Unvisited_nodes,Current_weight)
end.