by Nathan Pinsker

First, consider any path between two vertices of different colors. There must exist an edge on this path whose endpoints are different colors (otherwise, the entire path would be the same color). If the path comprises two or more edges, then considering just this edge as our path is strictly shorter than the path we started with. It follows that the shortest path must always be a single edge in the graph.

The next observation is to notice that, if vertices i and j are different colors, then ANY path from i to j must have an edge with two differently-colored vertices at some point. How do we know that the edge from i to j is actually the shortest path in the graph? At the very least, we need to know that there isn't any path from i to j comprised only of edges with lower weight -- if there was, then we could take some edge on that path and obtain a better solution. The set of edges in the graph that have this property is equal to the minimum spanning tree of the graph. Since any shortest path must be one of these edges, it is an edge of the minimum spanning tree of the graph.

However, each edge in the minimum spanning tree may or may not be usable at each step, depending on whether its vertices are two different colors. We must still test each edge in the minimum spanning tree at each step to determine whether it is usable, and of the usable edges, which is currently the smallest. In order to facilitate this, we need to track, for each vertex, each of the edges that connect to that vertex. We also need to be able to sort them by their weight, and filter by those that share the same color as the color we're considering.

We do this by keeping several heaps at each vertex -- for each possible color, we maintain a heap containing all adjacent vertices of that color. A little care is required when initializing these heaps, so as not to waste too much memory at each vertex. When we update a vertex's color, we remove it from its neighbors' heaps that correspond to that color, and insert it into its neighbors' heaps corresponding to the new color. In order to query a vertex's minimum-length edge, we take the minimum value over all heaps that don't match the vertex's current color (which can be maintained by another heap).

However, this raises a problem: a vertex may have many neighbors, so updating its color may take a long time. We can solve this by rooting our MST, so that each vertex only needs to compute its minimum-weight edge to one of its children. Thus, changing the color of a vertex will only require updates to the heaps located at that vertex and its parent.

Finally, we keep a global segment tree that contains the minimum value from each vertex-local heap, updated when we update each vertex-local heap. This does not change our overall runtime at all, since the cost of each update update is $O(\log n)$ per vertex-local heap update. At each step, we need to update $O(1)$ heaps at a cost of $O(\log n)$ per heap, so the total runtime is $O(n \log n)$.

Here is the solution written by Eric Zhang:

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

#define MAXN 200013
#define MAXM 200013
#define INF 1123456

int N, M, K, Q;
int ufd[MAXN];
pair<int, pair<int, int>> edges[MAXM];
vector<int> orderbyrow[MAXN];
vector<int> order;
int i2pos[MAXN], il[MAXN], ir[MAXN];
int parent[MAXN];
int depth[MAXN];
int C[MAXN];
int st2[4 * MAXN];

map<int, set<int> > colors[MAXN];
set<int> best[MAXN];

// Union-Find data structure
void ufd_init() {
for (int i = 0; i < N; i++) {
ufd[i] = i;
}
}

int ufd_find(int x) {
if (ufd[x] == x) return x;
return (ufd[x] = ufd_find(ufd[x]));
}

void ufd_union(int x, int y) {
x = ufd_find(x);
y = ufd_find(y);
ufd[y] = x;
}

void dfs(int n, int p=0) {
parent[n] = p;
depth[n] = depth[p] + 1;
vector<pair<int, int> > v;
for (auto c : adj[n]) {
if (c.first != p) {
v.push_back({c.second, c.first});
}
}
sort(v.begin(), v.end());
for (auto c : v) {
orderbyrow[depth[n]].push_back(c.second);
dfs(c.second, n);
}
}

// Min-SegTree for tracking the global minimum from each vertex-local heap.
void update2(int i, int x, int lo=0, int hi=-1, int node=0) {
if (hi == -1) hi = N - 1;
if (i > hi || i < lo) return;
if (lo == hi) { st2[node] = x; return; }
int mid = (lo + hi) / 2;
update2(i, x, lo, mid, 2 * node + 1);
update2(i, x, mid + 1, hi, 2 * node + 2);
st2[node] = min(st2[2 * node + 1], st2[2 * node + 2]);
}

int query2(int s, int e, int lo=0, int hi=-1, int node=0) {
if (hi == -1) hi = N - 1;
if (hi < s || lo > e) return INF;
if (lo >= s && hi <= e) return st2[node];
int mid = (lo + hi) / 2;
auto p1 = query2(s, e, lo, mid, 2 * node + 1);
auto p2 = query2(s, e, mid + 1, hi, 2 * node + 2);
return min(p1, p2);
}

int main() {
ios_base::sync_with_stdio(false);
cin.tie(0);

freopen("grass.in", "r", stdin);
freopen("grass.out", "w", stdout);

cin >> N >> M >> K >> Q;
for (int i = 0; i < M; i++) {
cin >> edges[i].second.first >> edges[i].second.second >> edges[i].first;
edges[i].second.first--;
edges[i].second.second--;
}

// Lemma: In a cycle, the highest-length edge will never be the answer.
// Proof: If that edge changes color, then another edge in the same cycle must change color ==> it is better.
// ---
// If we keep removing highest-length edges until we get a tree, then we can get to any MST just fine.
// (We definitely can't get anything besides an MST, as that leads to a contradiction).
// THUS: We use Kruskal and only keep the MST edges.
ufd_init();
sort(edges, edges + M);
for (auto p : edges) {
int u = p.second.first;
int v = p.second.second;
if (ufd_find(u) != ufd_find(v)) {
ufd_union(u, v);
}
}

// Now we reorder/relabel the nodes from 0..N-1 maintaining the following property:
// For every node n, the new label of n is i2pos[n]. Furthermore, the labels
// of n's children form a contiguous interval from [il[n]..ir[n]], and they satisfy
// the property that the children with the highest-weight edge to n have the highest label.
dfs(0);
orderbyrow.push_back(0);
for (int row = 0; row <= N; row++) {
order.insert(order.end(), orderbyrow[row].begin(), orderbyrow[row].end());
}
assert(order.size() == N);

memset(il, -1, sizeof il);
memset(ir, -1, sizeof ir);
for (int i = 0; i < order.size(); i++) {
i2pos[order[i]] = i;
if (order[i]) {
if (il[parent[order[i]]] == -1) il[parent[order[i]]] = order[i];
ir[parent[order[i]]] = order[i];
}
}

// We're done with initial preprocessing; now we will start working with the nodes' colors.
// At every vertex v, we will maintain an array of sets colors[v][c] that stores the weights
// of the edges to all the children of v of color c. We also maintain a set best[v] that
// stores all the minimum values of colors[v][c] for all values of c.
//
// Lastly, we keep a Min-Segment Tree of size N that stores the lowest-weight edge from
// v to a differently colored child of v for each node v. We can get the final answer by
// querying the segment tree from [0..N-1].
for (int i = 0; i < N; i++) {
cin >> C[i];
}
for (int n = 0; n < N; n++) {
// Initialize the data structures using the initial types of grass (colors).
if (il[n] == -1) {
update2(i2pos[n], INF);
continue;
}

for (int c = i2pos[il[n]]; c <= i2pos[ir[n]]; c++) {
colors[n][C[order[c]]].insert(c);
}

for (auto& p : colors[n]) {
best[n].insert(*p.second.begin());
}

auto it = best[n].begin();
if (C[order[*it]] == C[n]) it++;
if (it == best[n].end()) {
update2(i2pos[n], INF);
}
else {
}
}
for (int q = 0; q < Q; q++) {
int A, B;
cin >> A >> B;
A--;
int PB = C[A];
C[A] = B;

if (A) {
// If A is not the root, then we update A's parent to reflect A's change in color.
int n = parent[A];
if (best[n].count(i2pos[A])) {
best[n].erase(i2pos[A]);
if (colors[n][PB].size() > 1) {
best[n].insert(*(++colors[n][PB].begin()));
}
}

if (colors[n][B].size() && i2pos[A] < *colors[n][B].begin()) {
best[n].erase(*colors[n][B].begin());
best[n].insert(i2pos[A]);
}
else if (colors[n][B].empty()) {
best[n].insert(i2pos[A]);
}
colors[n][PB].erase(i2pos[A]);
colors[n][B].insert(i2pos[A]);

auto it = best[n].begin();
if (C[order[*it]] == C[n]) {
it++;
}

// Finally, update the segment tree at A's parent.
if (it == best[n].end()) {
update2(i2pos[n], INF);
}
else {
}
}

if (~il[A]) {
// If A has children (il[A] != -1), then we update A as well.
auto it = best[A].begin();
if (C[order[*it]] == C[A]) it++;

// Update the segment tree at A.
if (it == best[A].end()) {
update2(i2pos[A], INF);
}
else {