adjacency_matrix & adjacency_list

/*
 *           3--------4--------5
 *           |        |        |
 *           |        |        | 
 *           |        |        |
 *           1--------2--------6
 *            \      /
 *             \    /
 *              \  /
 *                0
 */


#include<bits/stdc++.h>
using namespace std;

int adjacency_matrix[][7]= {
    {0,1,1,0,0,0,0},
    {1,0,1,1,0,0,0},
    {1,1,0,0,1,0,0},
    {0,1,0,0,1,0,0},
    {0,0,1,1,0,1,0},
    {0,0,0,0,1,0,1},
    {0,0,0,0,0,1,0}
};

typedef struct point{
    int value;
    point * next;
}List,*Link;

void matrix_to_list(int n){
    Link adjacency_list[n];  //just a pointer,not node
    int i,j;
    for(i=0;i<n;i++){
        adjacency_list[i] = NULL;
    }
    for(i=0;i<n;i++){
        for(j=0;j<n;j++){
            if(adjacency_matrix[i][j]==1){
                Link p = new List;
                p->next = adjacency_list[i];
                p->value = j;
                adjacency_list[i] = p;
            }
        }
    }
    Link p;
    for(i=0;i<n;i++){
        p = adjacency_list[i];
        cout<<i;
        while(p){
            cout<<"--->"<<p->value;
            p = p->next;
        }
        cout<<endl;
    }
}

int main(){
    matrix_to_list(7);
}
adjacency_matrix = nx.adjacency_matrix(graph_B).todense() nodes = list(graph_B.nodes()) node_to_idx = {node: i for i, node in enumerate(nodes)} n = len(nodes) adj_matrix = np.zeros((n, n)) for (e1, e2, data) in graph_B.edges(data=True): i = node_to_idx[e1] j = node_to_idx[e2] ratio = data[&ldquo;ratio&rdquo;] # 从边属性中获取 ratio(例如 length) adj_matrix[i][j] = 1 adj_matrix[j][i] = 1 normalized = [] for row in adj_matrix: s = sum(row) if s == 0: # 处理全零行:此处保持全0,也可调整为其他逻辑 normalized_row = [0.0 for _ in row] else: normalized_row = [x / s for x in row] normalized.append(normalized_row) transition_matrix=normalized transition_matrix=np.array(normalized,dtype=float) Ga=graph_B GG = nx.Graph() GG.add_nodes_from(range(len(adjacency_matrix))) # 添加边 for i in range(len(adjacency_matrix)): for j in range(i+1, len(adjacency_matrix)): if adjacency_matrix[i][j] == 1: GG.add_edge(i, j) def generate_random_walk(transition_matrix, start_state, num_steps): num_states = transition_matrix.shape[0] current_state = start_state random_walk = [current_state] for _ in range(num_steps): probabilities = transition_matrix[current_state] if np.isnan(probabilities).any(): next_state = num_states random_walk.append(next_state) current_state = next_state else: next_state = np.random.choice(num_states, p=probabilities) random_walk.append(next_state) current_state = next_state return random_walk walks=[] for i in range(0,134): start_state = i # 初始状态 num_steps = args.num_steps # 随机游走的步数 num_steps = 64 random_walk = generate_random_walk(transition_matrix, start_state, num_steps) walks.append(random_walk) walk_str = [] for walk in walks: tmp = [] for node in walk: tmp.append(str(node)) walk_str.append(tmp) 用word2vec对游走序列生成嵌入向量 model = Word2Vec(walk_str, vector_size=128, window=args.window_size, min_count=0, sg=1, workers=args.workers) 改写我的代码 实现node2vec ,不应用现成的库
最新发布
03-18
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