1. Chapter 14 Graph Algorithms
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Acknowledgement: These slides are adapted from slides provided with Data Structures and Algorithms in Java, Goodrich, Tamassia and Goldwasser (Wiley 2016) 1 1

2. Graphs
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Depth-First Search D B A C E

3. Depth-First Search
Depth-first search (DFS) is a general technique for traversing a graph
A DFS traversal of a graph 𝐺
Visits all the vertices and edges of 𝐺
Determines whether 𝐺 is connected
Computes the connected components of 𝐺
Computes a spanning forest of 𝐺
DFS on a graph with 𝑛 vertices and 𝑚 edges takes 𝑂(𝑛+𝑚) time
DFS can be further extended to solve other graph problems
Find and report a path between two given vertices
Find a cycle in the graph
Depth-first search is to graphs as what Euler tour is to binary trees

4. DFS Algorithm from a vertex
Algorithm DFS(𝐺, 𝑢)
Input: A graph 𝐺 and a vertex 𝑢 of 𝐺
Output: A collection of vertices reachable from 𝑢, with their discovery edges
Mark 𝑢 as visited
for each edge 𝑒= 𝑢, 𝑣 ∈𝐺.outgoingEdges(𝑢) do
if 𝑣 has not been visited then
Record 𝑒 as a discovery edge for 𝑣
DFS(𝐺, 𝑣)

5. Example unexplored vertex visited vertex unexplored edge
Graphs
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Example
𝐼(𝐵) = {𝑨, 𝐶, 𝐹} 𝐼(𝐵) = {𝐴, 𝑪, 𝐹} D B A C E F G A
unexplored vertex A
visited vertex
unexplored edge
discovery edge
back edge
𝐼(𝐴) = {𝑩, 𝐶, 𝐷, 𝐸}
𝐼(𝐶) = {𝑨, 𝐵, 𝐷, 𝐸} D B A C E F G D B A C E F G
Explore incident edges in order, go along the unexplored edge
1. Seeing a neighbor already explored -> check next edge
2. Reaching dead end (no more edges to explore) -> go back to parent
𝐼(𝐶) = {𝐴, 𝑩, 𝐷, 𝐸} 𝐼(𝐶) = {𝐴, 𝐵, 𝑫, 𝐸}

6. Example 𝐼(𝐶) = {𝐴, 𝐵, 𝑫, 𝐸} 𝐼(𝐶) = {𝐴, 𝐵, 𝐷, 𝑬} 𝐼(𝐷) = {𝑨, 𝐶}D B A C E F G D B A C E F G
𝐼(𝐷) = {𝑨, 𝐶}
𝐼(𝐸) = {𝑨, 𝐶} D B A C E F G D B A C E F G
𝐼(𝐷) = {𝐴, 𝑪} 𝐼(𝐷) = {𝐴, 𝐶}
𝐼(𝐸) = {𝐴, 𝑪} 𝐼(𝐸) = {𝐴, 𝐶}

7. Example 𝐼(𝐶) = {𝐴, 𝐵, 𝐷, 𝐸} 𝐼(𝐵) = {𝐴, 𝐶, 𝑭} 𝐼(𝐺) =∅ 𝐼(𝐹) = {𝐵}D B A C E F G D B A C E F G
𝐼(𝐹) = {𝐵} D B A C E F G
𝐼(𝐵) = {𝐴, 𝐶, 𝐹} 𝐼(𝐴) = {𝐴, 𝐵, 𝐶, 𝐷}

8. Exercise DFS Algorithm
Perform DFS of the following graph, start from vertex A
Assume adjacent edges are processed in alphabetical order
Number vertices in the order they are visited
Label edges as discovery or back edges C B A E D F

9. Graphs
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DFS and Maze Traversal
The DFS algorithm is similar to a classic strategy for exploring a maze
We mark each intersection, corner and dead end (vertex) visited
We mark each corridor (edge) traversed
We keep track of the path back to the entrance (start vertex) by means of a rope (recursion stack)
Use a rope to mark the visited route. Choose an unexplored way at the intersection
1. Seeing the rope -> choose another way
2. Reaching dead end (no more ways to explore) -> go back to the last intersection

10. Graphs
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DFS Algorithm
The algorithm uses a mechanism for setting and getting “labels” of vertices and edges
Algorithm DFS(𝐺)
Input: Graph 𝐺
Output: Labeling of the edges of 𝐺 as discovery edges and back edges
for each 𝑣∈𝐺.vertices do
setLabel 𝑣, 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷
for each 𝑒∈𝐺.edges do
setLabel(𝑒, 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷)
if getLabel 𝑣 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
DFS(𝐺, 𝑣)
Algorithm DFS(𝐺, 𝑣)
Input: Graph 𝐺 and a start vertex 𝑣
Output: Labeling of the edges of 𝐺 in
the connected component of 𝑣 as
discovery edges and back edges
setLabel(𝑣, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷)
for each 𝑒∈𝐺.outgoingEdges 𝑣 do
if getLabel 𝑒 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷)
𝑤←𝐺.opposite(𝑣,𝑒)
if getLabel 𝑤 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
setLabel(𝑒, 𝐷𝐼𝑆𝐶𝑂𝑉𝐸𝑅𝑌)
DFS(𝐺, 𝑤)
else
setLabel(𝑒, 𝐵𝐴𝐶𝐾)

11. Properties of DFS v1 v2 Property 1 Property 2
DFS(𝐺, 𝑣) visits all the vertices and edges in the connected component of 𝑣
Property 2
The discovery edges labeled by DFS(𝐺, 𝑣) form a spanning tree of the connected component of 𝑣 v1 D B A C E F v2 G

12. Analysis of DFS Setting/getting a vertex/edge label takes 𝑂(1) time
Each vertex is labeled twice
once as 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷
once as 𝑉𝐼𝑆𝐼𝑇𝐸𝐷
Each edge is labeled twice
once as 𝐷𝐼𝑆𝐶𝑂𝑉𝐸𝑅𝑌 or 𝐵𝐴𝐶𝐾
Function DFS(𝐺, 𝑣) and the method outgoingEdges are called once for each vertex
DFS runs in 𝑂(𝑛+𝑚) time provided the graph is represented by the adjacency list structure
Recall that Σ 𝑣 deg 𝑣 =2𝑚 D B A C E F G

13. Application Path Finding
We can specialize the DFS algorithm to find a path between two given vertices 𝑢 and 𝑧 using the template method pattern
We call DFS(𝐺, 𝑢) with 𝑢 as the start vertex
We use a stack 𝑆 to keep track of the path between the start vertex and the current vertex
As soon as destination vertex 𝑧 is encountered, we return the path as the contents of the stack
Algorithm pathDFS(𝐺, 𝑣, 𝑧)
setLabel(𝑣, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷)
𝑆.push(𝑣)
if 𝑣=𝑧
return 𝑆.elements( )
for each 𝑒∈𝐺.outgoingEdges 𝑣 do
if getLabel 𝑒 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷) then
𝑤←𝐺.opposite(𝑣, 𝑒)
if getLabel 𝑤 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
setLabel(𝑒, 𝐷𝐼𝑆𝐶𝑂𝑉𝐸𝑅𝑌)
𝑆.push(𝑒)
pathDFS 𝐺, 𝑤
𝑆.pop( )
else
setLabel(𝑒, 𝐵𝐴𝐶𝐾)

14. Application Cycle Finding
Algorithm cycleDFS(𝐺, 𝑣)
setLabel(𝑣, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷)
𝑆.push(𝑣)
for each 𝑒∈𝐺.outgoingEdges 𝑣 do
if getLabel 𝑒 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷) then
𝑤←𝐺.opposite(𝑣,𝑒)
𝑆.push 𝑒
if getLabel 𝑤 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
setLabel(𝑒, 𝐷𝐼𝑆𝐶𝑂𝑉𝐸𝑅𝑌)
cycleDFS 𝐺, 𝑤
𝑆.pop( )
else
𝑇← empty stack
repeat
𝑇.push(S.pop())
until 𝑇.top =𝑤
return 𝑇.elements
We can specialize the DFS algorithm to find a simple cycle using the template method pattern
We use a stack 𝑆 to keep track of the path between the start vertex and the current vertex
As soon as a back edge 𝑣,𝑤 is encountered, we return the cycle as the portion of the stack from the top to vertex 𝑤

15. Directed DFS A C E B D
We can specialize the traversal algorithms (DFS and BFS) to digraphs by traversing edges only along their direction
In the directed DFS algorithm, we have four types of edges
discovery edges
back edges
forward edges
cross edges
A directed DFS starting at a vertex 𝑠 determines the vertices reachable from 𝑠

16. Reachability
DFS tree rooted at 𝑣: vertices reachable from 𝑣 via directed paths A C E D A C E B D F A C E B D F

17. Strong Connectivity Each vertex can reach all other vertices a g c d eb e f g

18. Strong Connectivity Algorithm
Pick a vertex 𝑣 in 𝐺
Perform a DFS from 𝑣 in 𝐺
If there’s a 𝑤 not visited, print “no”
Let 𝐺’ be 𝐺 with edges reversed
Perform a DFS from 𝑣 in 𝐺’
Else, print “yes”
Running time: 𝑂(𝑛+𝑚) c d e b f a
G’: g c d e b f

19. Strongly Connected Components
Maximal subgraphs such that each vertex can reach all other vertices in the subgraph
Can also be done in 𝑂(𝑛+𝑚) time using DFS, but is more complicated (similar to biconnectivity). a d c b e f g
Biconnectivity – removing any single vertex breaks connectedness
{ a , c , g }
{ f , d , e , b }

20. Graphs
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Breadth-First Search C B A E D L0 L1 F L2

21. Breadth-First Search
Breadth-first search (BFS) is a general technique for traversing a graph
A BFS traversal of a graph 𝐺
Visits all the vertices and edges of 𝐺
Determines whether 𝐺 is connected
Computes the connected components of 𝐺
Computes a spanning forest of 𝐺
BFS on a graph with n vertices and m edges takes 𝑂(𝑛+𝑚) time
BFS can be further extended to solve other graph problems
Find and report a path with the minimum number of edges between two given vertices
Find a simple cycle, if there is one

22. BFS Algorithm Algorithm BFS(𝐺, 𝑠) 𝐿 0 ← 𝑠 setLabel(𝑠, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷) 𝑖←0
while ¬ 𝐿 𝑖 .isEmpty( ) do
𝐿 𝑖+1 ←∅
for each 𝑣∈ 𝐿 𝑖 do
for each 𝑒∈𝐺.outgoingEdges(𝑣) do
if getLabel 𝑒 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
𝑤←𝐺.opposite 𝑣, 𝑒
if getLabel 𝑤 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
setLabel(𝑒, 𝐷𝐼𝑆𝐶𝑂𝑉𝐸𝑅𝑌)
setLabel(𝑤, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷)
𝐿 𝑖+1 ← 𝐿 𝑖+1 ∪ 𝑤
else
setLabel(𝑒, 𝐶𝑅𝑂𝑆𝑆)
𝑖←𝑖+1
The algorithm uses a mechanism for setting and getting “labels” of vertices and edges
Algorithm BFS(𝐺)
Input: Graph 𝐺
Output: Labeling of the edges and
partition of the vertices of 𝐺
for each 𝑣∈𝐺.vertices do
setLabel(𝑣, 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷)
for each 𝑒∈𝐺.edges( ) do
setLabel(𝑒, 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷)
for each 𝑣∈𝐺.vertices( ) do
if getLabel 𝑣 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
BFS 𝐺, 𝑣

23. Example unexplored vertex visited vertex unexplored edgeC B A E D L0 L1 F A
unexplored vertex A
visited vertex
unexplored edge
discovery edge
cross edge C B A E D L0 L1 F C B A E D L0 L1 F

24. Example L0 L1 L0 L1 L2 L0 L1 L2 L0 L1 L2 A C B A E D F C B A E D F C B
discovery edge
cross edge
visited vertex A
unexplored vertex
unexplored edge
Example C B A E D L0 L1 F C B A E D L0 L1 F L2 C B A E D L0 L1 F L2 C B A E D L0 L1 F L2

25. Example L0 L1 L2 L0 L1 L2 L0 L1 L2 A C B A E D F C B A E D F C B A E D
discovery edge
cross edge
visited vertex A
unexplored vertex
unexplored edge
Example C B A E D L0 L1 F L2 C B A E D L0 L1 F L2 C B A E D L0 L1 F L2

26. Exercise BFS Algorithm
Perform BFS of the following graph, start from vertex A
Assume adjacent edges are processed in alphabetical order
Number vertices in the order they are visited and note the level they are in
Label edges as discovery or cross edges C B A E D F

27. Properties Notation Property 1 Property 2 Property 3 L0 L1 L2 C B A ED F
Notation
𝐺 𝑠 : connected component of 𝑠
Property 1
BFS(𝐺, 𝑠) visits all the vertices and edges of 𝐺 𝑠
Property 2
The discovery edges labeled by BFS 𝐺, 𝑠 form a spanning tree 𝑇 𝑠 of 𝐺 𝑠
Property 3
For each vertex 𝑣∈ 𝐿 𝑖
The path of 𝑇 𝑠 from 𝑠 to 𝑣 has 𝑖 edges
Every path from 𝑠 to 𝑣 in 𝐺 𝑠 has at least 𝑖 edges C B A E D L0 L1 F L2

28. Analysis Setting/getting a vertex/edge label takes O(1) time
Each vertex is labeled twice
once as UNEXPLORED
once as VISITED
Each edge is labeled twice
once as DISCOVERY or CROSS
Each vertex is inserted once into a sequence 𝐿 𝑖
Method outgoingEdges( ) is called once for each vertex
BFS runs in 𝑂 𝑛+𝑚 time provided the graph is represented by the adjacency list structure
Recall that Σ 𝑣 deg 𝑣 =2𝑚

29. Applications
Using the template method pattern, we can specialize the BFS traversal of a graph 𝐺 to solve the following problems in 𝑂 𝑛+𝑚 time
Compute the connected components of 𝐺
Compute a spanning forest of 𝐺
Find a simple cycle in 𝐺, or report that 𝐺 is a forest
Given two vertices of 𝐺, find a path in 𝐺 between them with the minimum number of edges, or report that no such path exists

30. DFS vs. BFS Applications DFS BFS DFS BFS
Spanning forest, connected components, paths, cycles 
Shortest paths
Biconnected components C B A E D L0 L1 F L2 C B A E D F
Biconnected component – the component remains connected if any vertex is removed
DFS
BFS

31. DFS vs. BFS Back edge 𝑣, 𝑤 Cross edge 𝑣, 𝑤 DFS BFS
𝑤 is an ancestor of 𝑣 in the tree of discovery edges
Cross edge 𝑣, 𝑤
𝑤 is in the same level as 𝑣 or in the next level in the tree of discovery edges C B A E D L0 L1 F L2 C B A E D F
DFS
BFS

32. Topological Ordering Shortest Path 10/28/2017 12:38 AM BOS ORD JFK SFO
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33. DAGs and Topological OrderingB A D C E
DAG G
A directed acyclic graph (DAG) is a digraph that has no directed cycles
A topological ordering of a digraph is a numbering
𝑣 1 ,…, 𝑣 𝑛
Of the vertices such that for every edge 𝑣 𝑖 , 𝑣 𝑗 , we have 𝑖<𝑗
Example: in a task scheduling digraph, a topological ordering a task sequence that satisfies the precedence constraints
Theorem - A digraph admits a topological ordering if and only if it is a DAG B A D C E
Topological ordering of G v1 v2 v3 v4 v5

34. Application
Scheduling: edge (𝑎,𝑏) means task 𝑎 must be completed before 𝑏 can be started
The good life
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35. Exercise Topological Sorting
more c.s.
write c.s. program
dream about graphs
play
wake up
eat
nap
study computer sci.
work out
sleep
A typical student day
bake cookies
Number vertices, so that 𝑢,𝑣 in 𝐸 implies 𝑢<𝑣

36. Exercise Topological Sorting
more c.s.
write c.s. program
bake cookies
dream about graphs
play
wake up
eat
nap
study computer sci.
work out
sleep
A typical student day 1 2 3 4 5 6 7 8 9 10 11
Number vertices, so that 𝑢,𝑣 in 𝐸 implies 𝑢<𝑣

37. Algorithm for Topological Sorting
Algorithm TopologicalSort 𝐺
𝐻←𝐺
𝑛←𝐺.numVertices
while ¬𝐻.isEmpty do
Let 𝑣 be a vertex with no outgoing edges
Label 𝑣←𝑛
𝑛←𝑛−1
𝐻.removeVertex 𝑣

38. Implementation with DFS
Simulate the algorithm by using depth-first search
𝑂(𝑛+𝑚) time.
Algorithm topologicalDFS 𝐺
Input: DAG 𝐺
Output: Topological ordering of 𝑔
𝑛←𝐺.numVertices
Initialize all vertices as 𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷
for each vertex 𝑣∈𝐺.vertices do
if getLabel 𝑣 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
topologicalDFS 𝐺, 𝑣
Algorithm topologicalDFS 𝐺,𝑣
Input: DAG 𝐺, start vertex 𝑣
Output: Labeling of the vertices of 𝐺 in the connected component of 𝑣
setLabel(𝑣, 𝑉𝐼𝑆𝐼𝑇𝐸𝐷)
for each 𝑒∈𝐺.outgoingEdges 𝑣 do
𝑤←𝐺.opposite(𝑣, 𝑒)
if getLabel 𝑤 =𝑈𝑁𝐸𝑋𝑃𝐿𝑂𝑅𝐸𝐷 then
//𝑒 is a discovery edge
topologicalDFS 𝐺, 𝑤
else
//𝑒 is a forward, cross, or back edge
Label 𝑣 with topological number 𝑛
𝑛←𝑛−1

39. Topological Sorting Example

40. Topological Sorting Example9

41. Topological Sorting Example8 9

42. Topological Sorting Example7 8 9

43. Topological Sorting Example7 8 6 9

44. Topological Sorting Example7 8 5 6 9

45. Topological Sorting Example7 4 8 5 6 9

46. Topological Sorting Example7 4 8 5 6 3 9

47. Topological Sorting Example2 7 4 8 5 6 3 9

48. Topological Sorting Example2 7 4 8 5 6 1 3 9