Go: package main

import (
	"bufio"
	"fmt"
	"os"
	"strconv"
)

const BIG_COST = 200000

type Path struct {
	node int
	cost int
}

func main() {
	sc := bufio.NewScanner(os.Stdin)
	sc.Split(bufio.ScanWords)

	nNodes := scanInt(sc)

	// 各頂点につながる頂点一覧を得るための隣接リスト
	graph := make([][]Path, nNodes)

	for i := 0; i < nNodes; i++ {
		u := scanInt(sc)
		k := scanInt(sc)
		graph[i] = make([]Path, k)
		for d := 0; d < k; d++ {
			graph[u][d] = Path{
				node: scanInt(sc),
				cost: scanInt(sc),
			}
		}
	}

	// 0 から各頂点への最小コストを保存する配列
	minCosts := make([]int, nNodes)

	minCosts[0] = 0
	for i := 1; i < nNodes; i++ {
		minCosts[i] = BIG_COST
	}

	// 各頂点が調査済みかどうかを保持する配列
	checked := make([]bool, nNodes)

	// 最小コストのパスを高速に取得するためのヒープ。
	// コストが小さいものほどルートに来るようにする。
	heap := NewHeap(func(a, b Path) bool {
		return a.cost < b.cost
	})

	heap.insert(Path{node: 0, cost: 0})
	for heap.size > 0 {
		u := heap.extractMax()

		// 隣接行列を使っていた時とは違い、ヒープには
		// 同じノードへのパスが複数含まれうる。そのため既によりコストの低い
		// パスが見つかっている場合は無視する。
		if minCosts[u.node] < u.cost {
			continue
		}

		checked[u.node] = true

		for _, v := range graph[u.node] {
			cost := v.cost + u.cost
			if !checked[v.node] && cost < minCosts[v.node] {
				minCosts[v.node] = cost
				heap.insert(Path{node: v.node, cost: cost})
			}
		}
	}

	w := bufio.NewWriter(os.Stdout)
	for i, cost := range minCosts {
		fmt.Fprintf(w, "%d %d\n", i, cost)
	}
	w.Flush()
}

func scanInt(sc *bufio.Scanner) int {
	sc.Scan()
	v, _ := strconv.Atoi(sc.Text())
	return v
}

// --- From ALDS1_9_C: Priority Queue

type Heap struct {
	size   int
	heap   []Path
	isHigh func(a, b Path) bool
}

func NewHeap(isHigh func(a, b Path) bool) Heap {
	heap := make([]Path, 1) // 先頭は不使用
	return Heap{size: 0, heap: heap, isHigh: isHigh}
}

func (h *Heap) insert(v Path) {
	h.size += 1
	if h.size > len(h.heap)-1 {
		h.heap = append(h.heap, v)
	} else {
		h.heap[h.size] = v
	}

	for i := h.size; i > 1; {
		parent := i / 2
		if h.isHigh(h.heap[parent], h.heap[i]) {
			break
		}
		h.heap[i], h.heap[parent] = h.heap[parent], h.heap[i]
		i = parent
	}
}

func (h *Heap) extractMax() Path {
	if h.size <= 0 {
		panic("cannot extract from empty heap")
	}

	max := h.heap[1]
	h.heap[1] = h.heap[h.size]
	h.size -= 1
	h.maxHeapify(1)

	return max
}

func (h *Heap) maxHeapify(idx int) {
	left := idx * 2
	right := left + 1
	largest := idx

	if left <= h.size {
		if h.isHigh(h.heap[left], h.heap[idx]) {
			largest = left
		}
		if right <= h.size && h.isHigh(h.heap[right], h.heap[largest]) {
			largest = right
		}
	}

	if largest != idx {
		h.heap[idx], h.heap[largest] = h.heap[largest], h.heap[idx]
		h.maxHeapify(largest)
	}
}

C++: //
//  main.cpp
//  单元最短路径
//
//  Created by 陆昱珉 on 2018/12/26.
//  Copyright © 2018 Lu. All rights reserved.
//

//dijkstra algorithm

#include <stdio.h>
#define INF 999999

int n, k;
int color[3000], d[3000];
int map[3000][3000];

int dijkstra(int s)
{
    for(int i = 0; i < n; i ++)
    {
        d[i] = INF;
    }
    d[s] = 0;
    while(1)
    {
        int min = INF;
        int u = -1;
        for(int i = 0; i < n; i ++)
        {
            if(min > d[i] && color[i] != 1)
            {
                u = i;
                min = d[i];
            }
        }
        if(u == -1)
            break;
        color[u] = 1;//染色，以防后面重复使用
        for(int i = 0; i < n; i ++)
        {
            if(color[i] != 1 && map[u][i] != INF)
                if(d[i] > d[u] + map[u][i])
                    d[i] = d[u] + map[u][i];
        }
    }
    return 0;
}

int main(void)
{
    int x, y, v;
    scanf("%d", &n);
    for(int i = 0; i < n; i ++)
        for(int j = 0; j < n; j ++)
            map[i][j] = INF;
    for(int i = 0; i < n; i ++)
    {
        scanf("%d%d", &x, &k);
        for(int j = 0; j < k; j ++)
        {
            scanf("%d%d", &y, &v);
            map[x][y] = v;
        }
    }
    dijkstra(0);
    
    for(int i = 0; i < n; i ++)
    {
        if(d[i] != INF)
            printf("%d %d", i, d[i]);
        printf("\n");
    }
    return 0;
}

C++: #include <vector>
#include <iostream>
#include <limits>
#include <queue>
#include <map>

using namespace std;


struct edge {
    int src;
    int dst;
    int weight;

    edge(int src, int dst, int w) : src(src), dst(dst), weight(w) {}
};

bool operator < (const edge &a, const edge &b) {
  return a.weight > b.weight;
}


int main() {
    int n;
    cin >> n;

    vector<map<int, int>> G(n);
    for (int i = 0; i < n; ++i) {
        int u, k;
        cin >> u >> k;

        for (int j = 0; j < k; ++j) {
            int v, c;
            cin >> v >> c;
            G[u][v] = c;
        }
    }

    // Dijkstra's algorithm (with pritority queue)
    vector<int> d(n, -1);
    vector<bool> visited(n, false);
    priority_queue<edge> Q;

    Q.push(edge(-1, 0, 0));
    while (!Q.empty()) {
        auto e = Q.top(); Q.pop();
        if (visited[e.dst])
            continue;
        d[e.dst] = e.weight;
        visited[e.dst] = true;

        int i = e.dst;
        int minw = numeric_limits<int>::max();
        for (auto& x : G[i]) {
            if (visited[x.first])
                continue;
            int w = x.second + d[i];
            Q.push(edge(i, x.first, w));
        }
    }

    for (int i = 0; i < n; ++i) {
        cout << i << " " << d[i] << endl;
    }

    return 0;
}

C++: #include <cassert>
#include <climits>
#include <algorithm>
#include <iostream>
#include <vector>
#include <string>


class TVertex;
struct TPath
{
    int Cost;
    TVertex* Target;
    TPath(TVertex* ATarget, int ACost)
     : Target(ATarget), Cost(ACost) {}
};
class TVertex
{
    int FIndex;
    std::vector<TPath> FList;
    int FDistance;

public:
    TVertex(int Index)
     : FIndex(Index), FDistance(INT_MAX)
    {}
    void AddPath(const TPath& Path)
    {
        FList.push_back(Path);
    }
    void PrintDistance(std::ostream& out)const
    {
        out << FIndex << " " << FDistance << std::endl;
    }
    void BreadthFirstSearch(int Distance)
    {
        if(FDistance <= Distance) return;
        FDistance = Distance;
        for(std::vector<TPath>::iterator It = FList.begin(); It != FList.end(); ++It){
            It->Target->BreadthFirstSearch(Distance + It->Cost);
        }
    }
};
class TGraph
{
    std::vector<TVertex*> FTable;
    void Init(int n)
    {
        assert(n > 0);
        FTable.resize(n);
        for(int i = 0; i < n; ++i){
            FTable[i] = new TVertex(i);
        }
    }
public:
    TGraph(int n)
    {
        Init(n);
    }
    
    void SetupVertex(std::istream& in)
    {
        int Index;
        in >> Index;
        assert(Index >= 0);
        assert(Index < FTable.size());
        int k;
        in >> k;
        for(int i = 0; i < k; ++i){
            int v, c;
            in >> v >> c;
            FTable[Index]->AddPath(TPath(FTable[v], c));
        }
    }
    void BreadthFirstSearch()
    {
        FTable[0]->BreadthFirstSearch(0);
    }
    void PrintDistance(std::ostream& out)
    {
        for(int i = 0; i < FTable.size(); ++i){
            FTable[i]->PrintDistance(out);
        }
    }
};

int main()
{
    int n;
    std::cin >> n;
    TGraph Graph(n);
    for(int i = 0; i < n; ++i){
        Graph.SetupVertex(std::cin);
    }
    Graph.BreadthFirstSearch();
    Graph.PrintDistance(std::cout);
    return 0;
    
}