# Contraction Hierarchies#

Genius algorithm to find shortest paths in a large graph. Most of the shortest path algorithms do search in a graph. But what if we could preprocess that graph to make it easier to search on it later ? This is the approach that was chosen by Contraction Hierarchies algorithm.

The algorithm has of 2 stages:

1. Preporessing stage to create indices.

2. Query stage that uses indices to find a shortest path.

## 1. Preprocess#

The goal is to develop an alternate Dijkstra’s algorithm that can ignore some node $$v$$ and stops when given max distance is reached.

### We use this algorithm to find shortcuts#

1. Start from choosing a random node.

2. Calculate the max distance between incident nodes:

• When contracting node $$v$$, we need to insert the shortcut $$u \to w$$, if and only if $$u \to v \in E$$ and $$v \to w \in E$$ and $$u \to v \to w$$ is the only shortest path from $$u$$ to $$w$$.

• When contracting node $$v$$, for any pair of edges $$(u, v)$$ and $$(v, w)$$ we want to check whether there is a witness path from u to $$w$$ bypassing $$v$$ with length at most $$l(u,v)+l(v,w)$$ — then there is no need to add a shortcut from $$u$$ to $$w$$.

3. Make 1 hop backward search for each predecessor of target node v

4. Last node does not need to be contracted

5. Compute the importance for the top node on the priority queue, then compare it with the other top node, if its importance is less, then put the node back into the queue.

6. Already contracted nodes can be distinguished by their ranks: already contracted nodes have lower rank than currently contracting node. So, instead of having an array for contracted nodes, use the rank array. Motherfucker!

7. Stop limits can be various:

• max_incoming + max_outgoing

• cost(v, x) + max_outgoing - min_outgoing

• cost(v, x) + max_outgoing - min(x, w)

### Node ordering#

It is important because query time and preprocessing heavily depends on it. Nodes must be measure my some importance criterias and the least important nodes must be contracted first. Further, several importance criterias will be covered:

#### Edge difference#

• Want to minimize the number of edges in the augmented graph

• Number of shortcuts have to be added if node $$v$$ is contracted $$s(v)$$, incoming degree $$in(v)$$ and outgoing degree $$out(v)$$

• Lets suppose that there are 3 incoding endges and 5 outgoing edges, what is the worst case count of shortcuts if we contract this node ?

• It is $$in(v) * out(v)$$

• Edge difference $$ed = s(v) - in(v) - out(v)$$

• Number of edges increases by $$ed$$ after contracting $$v$$

• Contract node with small edge difference

#### Number of contracted neighbors#

• Want to spread contracted nodes across the graph

• Contract a node with small number of already contracted neighbors $$cn(v)$$

#### Shortcut Cover#

• Want to contract important nodes late

• Shortcut cover $$sc(v)$$ - the number of neighbors $$w$$ of $$v$$ such that we have to shortcut to or from $$w$$ after contracting $$v$$.

• If shortcut cover is big, many nodes depend on $$v$$

• Contract a node with small $$sc(v)$$

#### Node level (?)#

• Node level $$L(v)$$ is an upper bound on the number of edges in the shortest path from any s to v in the augmented graph

• Initially, $$L(v)$$ = 0

• After contracting node $$v$$, for neighbors $$u$$ of v do L(u) = max(L(u), L(v) + 1)

• Contract a node with small L(v)

TBA

## Code#

import queue
import sys
import time

INF = sys.maxsize

class ContractionHierarchies:
def __init__(self, n, m, g, g_r, costs, costs_r):
self.n = n
self.m = m
self.g = g
self.g_r = g_r
self.costs = costs
self.costs_r = costs_r
self.node_level =  * n
self.max_outgoing = [max(w) if len(w) else 1 for w in costs]
self.min_outgoing = [min(w) if len(w) else INF for w in costs_r]
self.rank = [INF] * n

def witness_search(self, s, v, max_dist):
dist = dict()
dist[s] = 0

heap = queue.PriorityQueue()
heap.put((0, s))
max_hops = 3
hops = 0

while not heap.empty() and hops < max_hops:
hops += 1
d, u = heap.get()

if max_dist <= d:
break

for x, w in self.g[u]:
if self.rank[x] < self.rank[v] or x == v:
continue

if d + w < dist.get(x, INF):
dist[x] = d + w
heap.put((dist[x], x))

return dist

def contract(self, v, adding_shortcuts=False):
shortcuts = list()
shortcuts_cover = set()
shortcut_count = 0
delta = self.max_outgoing[v] - self.min_outgoing[v]

for u, u_d in self.g_r[v]:
if self.rank[u] < self.rank[v]:
continue

limit = u_d + delta
dist = self.witness_search(u, v, limit)

for w, _ in self.g[v]:
if self.rank[w] < self.rank[v]:
continue

for x, d in self.g_r[w]:
if self.rank[x] < self.rank[v] or x == v:
continue

cost = dist.get(x, INF)

if self.costs_r[v][u] + self.costs[v][w] >= cost + d:
break

shortcut_count += 1

shortcuts.append((u, w, self.costs_r[v][u] + self.costs[v][w]))

edge_difference = shortcut_count - len(self.g[v]) - len(self.g_r[v])
return edge_difference + self.compute_node_level(v) + len(shortcuts_cover), shortcuts

def compute_node_level(self, v):
n, level = 0, 0

for u, _ in self.g[v]:
if self.rank[u] != INF:
n += 1
level = max(self.node_level[u]+1, level)

for u, _ in self.g_r[v]:
if self.rank[u] != INF:
n += 1
level = max(self.node_level[u]+1, level)

return n + level/2

def update_node_level(self, v):
for u, _ in self.g[v]:
self.node_level[u] = max(self.node_level[u], self.node_level[v]+1)

for u, _ in self.g_r[v]:
self.node_level[u] = max(self.node_level[u], self.node_level[v]+1)

for shortcut in shortcuts:
u, v, w = shortcut
if self.max_outgoing[u] < w:
self.max_outgoing[u] = w

if w < self.min_outgoing[u]:
self.max_outgoing[v] = w

self.g[u].append((v, w))
self.g_r[v].append((u, w))

self.costs[u][v] = w
self.costs_r[v][u] = w

def remove_edges(self):
for i in range(self.n):
j, k = 0, len(self.g[i])
while j < k:
if self.rank[self.g[i][j]] < self.rank[i]:
self.costs[i][self.g[i][j]] = INF
del self.g[i][j]
k -= 1
continue
j += 1

j, k = 0, len(self.g_r[i])
while j < k:
if self.rank[self.g_r[i][j]] < self.rank[i]:
self.costs_r[i][self.g_r[i][j]] = INF
del self.g_r[i][j]
k -= 1
continue
j += 1

def preprocess(self):
counter = 0
rank_count = 0
pq = queue.PriorityQueue()

print("n:", self.n)
for i in range(self.n):
c, _ = self.contract(i)
pq.put((c, i))

while pq.qsize() > 1:
counter += 1

_, u = pq.get()
ved, v = pq.get()

ed, shortcuts = self.contract(u, adding_shortcuts=True)

if ed <= ved:
self.rank[u] = rank_count
self.update_node_level(u)
else:
pq.put((ed, u))

rank_count += 1
pq.put((ved, v))

print("counter:", counter)
self.remove_edges()

def query(self, s, t):
estimate = INF

pq = queue.PriorityQueue()
pq_r = queue.PriorityQueue()

pq.put((0, s))
pq_r.put((0, t))

dist = [INF] * self.n
dist_r = [INF] * self.n
dist[s] = 0
dist_r[t] = 0

visited = [INF] * self.n
visited_r = [INF] * self.n

while not pq.empty() or not pq_r.empty():
if not pq.empty():
_, u = pq.get()

if dist[u] <= estimate:
for v, w in self.g[u]:
if dist[v] > dist[u] + w:
dist[v] = dist[u] + w
pq.put((dist[v], v))

visited[u] = True
if visited_r[u] and dist[u] + dist_r[u] < estimate:
estimate = dist[u] + dist_r[u]

if not pq_r.empty():
_, u = pq_r.get()

if dist_r[u] < estimate:
for v, w in self.g_r[u]:
if dist_r[v] > dist_r[u] + w:
dist_r[v] = dist_r[u] + w
pq_r.put((dist[v], v))

visited_r[u] = True
if visited[u] and dist[u] + dist_r[u] < estimate:
estimate = dist[u] + dist_r[u]

return -1 if estimate == INF else estimate

def init():
n, m = readl()

g = [[] for _ in range(n)]
g_r = [[] for _ in range(n)]

costs = [[0 for _ in range(n)] for _ in range(n)]
costs_r = [[INF for _ in range(n)] for _ in range(n)]

for _ in range(m):
u, v, w = readl()
costs[u-1][v-1] = w
costs_r[v-1][u-1] = w

g[u-1].append((v-1, w))
g_r[v-1].append((u-1, w))

return ContractionHierarchies(n, m, g, g_r, costs, costs_r)

if __name__ == '__main__':
ch = init()

start_time = time.time()
ch.preprocess()