14 Graph Algorithms Hongfei Yan June 8, 2016.

14 Graph Algorithms Hongfei Yan June 8, 2016

Assignment #11 on chapter 14: Graph Algorithms
dfs and bfs topological ordering shortest path minimum spanning tree

Contents 01 Python Primer (P2-51)
02 Object-Oriented Programming (P57-103) 03 Algorithm Analysis (P ) 04 Recursion (P ) 05 Array-Based Sequences (P ) 06 Stacks, Queues, and Deques (P ) 07 Linked Lists (P ) 08 Trees (P ) 09 Priority Queues (P ) 10 Maps, Hash Tables, and Skip Lists (P ) 11 Search Trees (P ) 12 Sorting and Selection (P ) 13 Text Processing (P ) 14 Graph Algorithms (P ) 15 Memory Management and B-Trees (P ) ISLR_Print6

Contents 14.1 Graphs 14.2 Data Structures for Graphs 14.3 Graph Traversals 14.4 Tansitive Closure 14.5 Directed Acyclic Graphs 14.6 Shortest Paths 14.7 Minimum Spanning Trees

14.1 Graphs ORD SFO LAX DFW 1843 802 1743 337 1233 Graphs
12/7/2017 7:18 AM 14.1 Graphs 1843 ORD SFO 802 1743 337 LAX 1233 DFW

Graphs PVD ORD SFO LGA HNL LAX DFW MIA A graph is a pair (V, E), where
V is a set of nodes, called vertices E is a collection of pairs of vertices, called edges Vertices and edges are positions and store elements Example: A vertex represents an airport and stores the three-letter airport code An edge represents a flight route between two airports and stores the mileage of the route 849 PVD 1843 ORD 142 SFO 802 LGA 1743 337 1387 HNL 2555 1099 LAX 1233 DFW 1120 MIA

Edge Types flight AA 1206 ORD PVD 849 miles ORD PVD Directed edge
ordered pair of vertices (u,v) first vertex u is the origin second vertex v is the destination e.g., a flight Undirected edge unordered pair of vertices (u,v) e.g., a flight route Directed graph all the edges are directed e.g., route network Undirected graph all the edges are undirected e.g., flight network flight AA 1206 ORD PVD 849 miles ORD PVD

Applications Electronic circuits Transportation networks
Printed circuit board Integrated circuit Transportation networks Highway network Flight network Computer networks Local area network Internet Web Databases Entity-relationship diagram

Terminology X U V W Z Y a c b e d f g h i j
End vertices (or endpoints) of an edge U and V are the endpoints of a Edges incident on a vertex a, d, and b are incident on V Adjacent vertices U and V are adjacent Degree of a vertex X has degree 5 Parallel edges h and i are parallel edges Self-loop j is a self-loop X U V W Z Y a c b e d f g h i j

Terminology (cont.) Path Simple path Examples V a b P1 d U X Z P2 h c
sequence of alternating vertices and edges begins with a vertex ends with a vertex each edge is preceded and followed by its endpoints Simple path path such that all its vertices and edges are distinct Examples P1=(V,b,X,h,Z) is a simple path P2=(U,c,W,e,X,g,Y,f,W,d,V) is a path that is not simple V a b P1 d U X Z P2 h c e W g f Y

Terminology (cont.) V a b d U X Z C2 h e C1 c W g f Y Cycle
circular sequence of alternating vertices and edges each edge is preceded and followed by its endpoints Simple cycle cycle such that all its vertices and edges are distinct Examples C1=(V,b,X,g,Y,f,W,c,U,a,) is a simple cycle C2=(U,c,W,e,X,g,Y,f,W,d,V,a,) is a cycle that is not simple V a b d U X Z C2 h e C1 c W g f Y

Properties Sv deg(v) = 2m Property 1 Property 2 Notation Example n = 4
Proof: each edge is counted twice Property 2 In an undirected graph with no self-loops and no multiple edges m  n (n - 1)/2 Proof: each vertex has degree at most (n - 1) Notation n number of vertices m number of edges deg(v) degree of vertex v Example n = 4 m = 6 deg(v) = 3 What is the bound for a directed graph?

Vertices and Edges A graph is a collection of vertices and edges.
We model the abstraction as a combination of three data types: Vertex, Edge, and Graph. A Vertex is a lightweight object that stores an arbitrary element provided by the user (e.g., an airport code) We assume it supports a method, element(), to retrieve the stored element. An Edge stores an associated object (e.g., a flight number, travel distance, cost), retrieved with the element( ) method. In addition, we assume that an Edge supports the following methods:

Graph ADT

14.2 Data Structures for Graphs

Edge List Structure Vertex object Edge object Vertex sequence
element reference to position in vertex sequence Edge object origin vertex object destination vertex object reference to position in edge sequence Vertex sequence sequence of vertex objects Edge sequence sequence of edge objects In an edge list, we maintain an unordered list of all edges. This minimally suffices, but there is no efficient way to locate a particular edge (u,v), or the set of all edges incident to a vertex v.

Adjacency List Structure
Incidence sequence for each vertex sequence of references to edge objects of incident edges Augmented edge objects references to associated positions in incidence sequences of end vertices In an adjacency list, we maintain, for each vertex, a separate list containing those edges that are incident to the vertex. The complete set of edges can be determined by taking the union of the smaller sets, while the organization allows us to more efficiently find all edges incident to a given vertex.

Adjacency map structure
An adjacency map is very similar to an adjacency list, but the secondary container of all edges incident to a vertex is organized as a map, rather than as a list, with the adjacent vertex serving as a key. This allows for access to a specific edge (u,v) in O(1) expected time.

Adjacency Matrix Structure
Edge list structure Augmented vertex objects Integer key (index) associated with vertex 2D-array adjacency array Reference to edge object for adjacent vertices Null for non nonadjacent vertices The “old fashioned” version just has 0 for no edge and 1 for edge An adjacency matrix provides worst-case O(1) access to a specific edge (u,v) by maintaining an n×n matrix, for a graph with n vertices. Each entry is dedicated to storing a reference to the edge (u,v) for a particular pair of vertices u and v; if no such edge exists, the entry will be None.

Performance n vertices, m edges no parallel edges no self-loops

Python Graph Implementation
We use a variant of the adjacency map representation. For each vertex v, we use a Python dictionary to represent the secondary incidence map I(v). The list V is replaced by a top-level dictionary D that maps each vertex v to its incidence map I(v). Note that we can iterate through all vertices by generating the set of keys for dictionary D. A vertex does not need to explicitly maintain a reference to its position in D, because it can be determined in O(1) expected time. Running time bounds for the adjacency-list graph ADT operations, given above, become expected bounds.

Vertex Class

Edge Class

Graph, Part 1

Graph, end

14.3 Graph Traversals Depth-First Search DFS Implementation and Extensions Breadth-First Search 14.4 Transitive Closure 14.5 Directed Acyclic Graphs Topological Ordering

14.3.1 Depth-First Search D B A C E Depth-First Search
12/7/2017 7:18 AM Depth-First Search D B A C E

Subgraphs A subgraph S of a graph G is a graph such that
The vertices of S are a subset of the vertices of G The edges of S are a subset of the edges of G A spanning subgraph of G is a subgraph that contains all the vertices of G Subgraph Spanning subgraph

Non connected graph with two connected components
Connectivity A graph is connected if there is a path between every pair of vertices A connected component of a graph G is a maximal connected subgraph of G Connected graph Non connected graph with two connected components

Trees and Forests A (free) tree is an undirected graph T such that
T is connected T has no cycles This definition of tree is different from the one of a rooted tree A forest is an undirected graph without cycles The connected components of a forest are trees Tree Forest

Spanning Trees and Forests
A spanning tree of a connected graph is a spanning subgraph that is a tree A spanning tree is not unique unless the graph is a tree Spanning trees have applications to the design of communication networks A spanning forest of a graph is a spanning subgraph that is a forest Graph Spanning tree

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 what Euler tour is to binary trees

Python Implementation

Example unexplored vertex visited vertex unexplored edge
B A C E A A visited vertex unexplored edge discovery edge back edge D B A C E D B A C E Depth-First Search

Example (cont.) D B A C E D B A C E D B A C E D B A C E
Depth-First Search

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)

Properties of DFS 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 D B A C E

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 Method incidentEdges is called once for each vertex DFS runs in O(n + m) time provided the graph is represented by the adjacency list structure Recall that Sv deg(v) = 2m Depth-First Search

14.3.3 Breadth-First Search L0 L1 L2 C B A E D F Breadth-First Search
12/7/2017 7:18 AM Breadth-First Search C B A E D L0 L1 F L2

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

Example unexplored vertex visited vertex unexplored edge
C B A E D L0 L1 F A unexplored vertex A visited vertex unexplored edge discovery edge cross edge L0 L0 A A L1 L1 B C D B C D E F E F Breadth-First Search

Example (cont.) L0 L1 L0 L1 L2 L0 L1 L2 L0 L1 L2 C B A E D F C B A E D
Breadth-First Search

Example (cont.) L0 L1 L2 L0 L1 L2 L0 L1 L2 C B A E D F A B C D E F C B
Breadth-First Search

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

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 Li Method incidentEdges 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 Sv deg(v) = 2m

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

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 A B C D E F DFS BFS

14.5 Directed Graphs Shortest Path 12/7/2017 7:18 AM BOS ORD JFK SFO
DFW LAX MIA

Digraphs A digraph is a graph whose edges are all directed
Short for “directed graph” Applications one-way streets flights task scheduling A C E B D Directed Graphs

Digraph Properties A graph G=(V,E) such that
B D Digraph Properties A graph G=(V,E) such that Each edge goes in one direction: Edge (a,b) goes from a to b, but not b to a If G is simple, m < n(n - 1) If we keep in-edges and out-edges in separate adjacency lists, we can perform listing of incoming edges and outgoing edges in time proportional to their size

Digraph Application Scheduling: edge (a,b) means task a must be completed before b can be started ics21 ics22 ics23 ics51 ics53 ics52 ics161 ics131 ics141 ics121 ics171 The good life ics151 Directed Graphs

14.5.1 DAGs and Topological Ordering
A directed acyclic graph (DAG) is a digraph that has no directed cycles A topological ordering of a digraph is a numbering v1 , …, vn of the vertices such that for every edge (vi , vj), 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 C A DAG G v4 v5 D E v2 B v3 C v1 Topological ordering of G A Directed Graphs

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

Algorithm for Topological Sorting
Note: This algorithm is different than the one in the book Running time: O(n + m) Algorithm TopologicalSort(G) H  G // Temporary copy of G n  G.numVertices() while H is not empty do Let v be a vertex with no outgoing edges Label v  n n  n - 1 Remove v from H

Implementation with DFS
Simulate the algorithm by using depth-first search O(n+m) time. Algorithm topologicalDFS(G, v) Input graph G and a start vertex v of G Output labeling of the vertices of G in the connected component of v setLabel(v, VISITED) for all e  G.outEdges(v) { outgoing edges } w  opposite(v,e) if getLabel(w) = UNEXPLORED { e is a discovery edge } topologicalDFS(G, w) else { e is a forward or cross edge } Label v with topological number n n  n - 1 Algorithm topologicalDFS(G) Input dag G Output topological ordering of G n  G.numVertices() for all u  G.vertices() setLabel(u, UNEXPLORED) for all v  G.vertices() if getLabel(v) = UNEXPLORED topologicalDFS(G, v) Directed Graphs

Topological Sorting Example
Directed Graphs

9 Directed Graphs

8 9 Directed Graphs

7 8 9 Directed Graphs

6 7 8 9 Directed Graphs

6 5 7 8 9 Directed Graphs

4 6 5 7 8 9 Directed Graphs

3 4 6 5 7 8 9 Directed Graphs

2 3 4 6 5 7 8 9 Directed Graphs

2 1 3 4 6 5 7 8 9 Directed Graphs

14.6 Shortest Paths C B A E D F 3 2 8 5 4 7 1 9 Shortest Path
12/7/2017 7:18 AM 14.6 Shortest Paths C B A E D F 3 2 8 5 4 7 1 9

Weighted Graphs PVD ORD SFO LGA HNL LAX DFW MIA
In a weighted graph, each edge has an associated numerical value, called the weight of the edge Edge weights may represent, distances, costs, etc. Example: In a flight route graph, the weight of an edge represents the distance in miles between the endpoint airports 849 PVD 1843 ORD 142 SFO 802 LGA 1743 1205 337 1387 HNL 2555 1099 LAX 1233 DFW 1120 MIA Shortest Paths

Shortest Paths PVD ORD SFO LGA HNL LAX DFW MIA
Given a weighted graph and two vertices u and v, we want to find a path of minimum total weight between u and v. Length of a path is the sum of the weights of its edges. Example: Shortest path between Providence and Honolulu Applications Internet packet routing Flight reservations Driving directions 849 PVD 1843 ORD 142 SFO 802 LGA 1743 1205 337 1387 HNL 2555 1099 LAX 1233 DFW 1120 MIA Shortest Paths

Shortest Path Properties
Property 1: A subpath of a shortest path is itself a shortest path Property 2: There is a tree of shortest paths from a start vertex to all the other vertices Example: Tree of shortest paths from Providence 849 PVD 1843 ORD 142 SFO 802 LGA 1743 1205 337 1387 HNL 2555 1099 LAX 1233 DFW 1120 MIA Shortest Paths

Dijkstra’s Algorithm The distance of a vertex v from a vertex s is the length of a shortest path between s and v Dijkstra’s algorithm computes the distances of all the vertices from a given start vertex s Assumptions: the graph is connected the edges are undirected the edge weights are nonnegative We grow a “cloud” of vertices, beginning with s and eventually covering all the vertices We store with each vertex v a label d(v) representing the distance of v from s in the subgraph consisting of the cloud and its adjacent vertices At each step We add to the cloud the vertex u outside the cloud with the smallest distance label, d(u) We update the labels of the vertices adjacent to u

Edge Relaxation Consider an edge e = (u,z) such that d(z) = 75
u is the vertex most recently added to the cloud z is not in the cloud The relaxation of edge e updates distance d(z) as follows: d(z)  min{d(z),d(u) + weight(e)} d(u) = 50 10 d(z) = 75 e u s z d(u) = 50 10 d(z) = 60 u e s z

Example C B A E D F 3 2 8 5 4 7 1 9 A 4 8 2 8 2 4 7 1 B C D 3 9   2 5 E F A 4 A 8 8 4 2 2 8 2 3 7 2 3 7 1 7 1 B C D B C D 3 9 3 9 5 11 5 8 2 5 2 5 E F E F Shortest Paths

Example (cont.) A 4 8 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F A 8 4 2 7 2 3
A 4 8 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F A 8 4 2 7 2 3 7 1 B C D 3 9 5 8 2 5 E F Shortest Paths

Dijkstra’s Algorithm

Analysis of Dijkstra’s Algorithm
Graph operations We find all the incident edges once for each vertex Label operations We set/get the distance and locator labels of vertex z O(deg(z)) times Setting/getting a label takes O(1) time Priority queue operations Each vertex is inserted once into and removed once from the priority queue, where each insertion or removal takes O(log n) time The key of a vertex in the priority queue is modified at most deg(w) times, where each key change takes O(log n) time Dijkstra’s algorithm runs in O((n + m) log n) time provided the graph is represented by the adjacency list/map structure Recall that Sv deg(v) = 2m The running time can also be expressed as O(m log n) since the graph is connected

14.5 Minimum Spanning Trees
12/7/2017 7:18 AM 14.5 Minimum Spanning Trees

Minimum Spanning Trees
Spanning subgraph Subgraph of a graph G containing all the vertices of G Spanning tree Spanning subgraph that is itself a (free) tree Minimum spanning tree (MST) Spanning tree of a weighted graph with minimum total edge weight Applications Communications networks Transportation networks ORD 10 1 PIT DEN 6 7 9 3 DCA STL 4 8 5 2 DFW ATL

Prim-Jarnik’s Algorithm
Similar to Dijkstra’s algorithm We pick an arbitrary vertex s and we grow the MST as a cloud of vertices, starting from s We store with each vertex v label d(v) representing the smallest weight of an edge connecting v to a vertex in the cloud At each step: We add to the cloud the vertex u outside the cloud with the smallest distance label We update the labels of the vertices adjacent to u

Prim-Jarnik Pseudo-code

Example  7 7 D 7 D 2 2 B 4 B 4 8 9  5 9  5 5 2 F 2 F C C 8 8 3 3 8 8 E E A 7 A 7 7 7 7 7 7 D 2 7 D 2 B 4 B 4 5 9  5 5 9 4 2 F 5 C 2 F 8 C 8 3 8 3 8 E A E 7 7 A 7 7 Minimum Spanning Trees

Example (contd.) 7 7 D 2 B 4 9 4 5 5 2 F C 8 3 8 E A 3 7 7 7 D 2 B 4 5 9 4 5 2 F C 8 3 8 E A 3 7 Minimum Spanning Trees

Analysis Graph operations We cycle through the incident edges once for each vertex Label operations We set/get the distance, parent and locator labels of vertex z O(deg(z)) times Setting/getting a label takes O(1) time Priority queue operations Each vertex is inserted once into and removed once from the priority queue, where each insertion or removal takes O(log n) time The key of a vertex w in the priority queue is modified at most deg(w) times, where each key change takes O(log n) time Prim-Jarnik’s algorithm runs in O((n + m) log n) time provided the graph is represented by the adjacency list structure Recall that Sv deg(v) = 2m The running time is O(m log n) since the graph is connected Minimum Spanning Trees

Kruskal’s Approach Maintain a partition of the vertices into clusters
Initially, single-vertex clusters Keep an MST for each cluster Merge “closest” clusters and their MSTs A priority queue stores the edges outside clusters Key: weight Element: edge At the end of the algorithm One cluster and one MST

Kruskal’s Algorithm

Example G G 8 8 B 4 B 4 E 9 E 6 9 6 5 1 F 5 1 F C 3 11 C 3 11 2 2 7 H 7 H D D A 10 A 10 G G 8 8 B 4 B 4 9 E E 6 9 6 5 1 F 5 1 F C 3 3 11 C 11 2 2 7 H 7 H D D A A 10 10 Campus Tour

Example (contd.) four steps two steps G G 8 8 B 4 B 4 E 9 E 6 9 6 5 5
1 F 1 F C 3 C 3 11 11 2 2 7 H 7 H D D A 10 A 10 four steps two steps G G 8 8 B 4 B 4 E E 9 6 9 6 5 5 1 F 1 F C 3 11 C 3 11 2 2 7 H 7 H D D A A 10 10 Campus Tour

Data Structure for Kruskal’s Algorithm
The algorithm maintains a forest of trees A priority queue extracts the edges by increasing weight An edge is accepted it if connects distinct trees We need a data structure that maintains a partition, i.e., a collection of disjoint sets, with operations: makeSet(u): create a set consisting of u find(u): return the set storing u union(A, B): replace sets A and B with their union Minimum Spanning Trees

List-based Partition Each set is stored in a sequence Each element has a reference back to the set operation find(u) takes O(1) time, and returns the set of which u is a member. in operation union(A,B), we move the elements of the smaller set to the sequence of the larger set and update their references the time for operation union(A,B) is min(|A|, |B|) Whenever an element is processed, it goes into a set of size at least double, hence each element is processed at most log n times Minimum Spanning Trees

Partition-Based Implementation
Partition-based version of Kruskal’s Algorithm Cluster merges as unions Cluster locations as finds Running time O((n + m) log n) Priority Queue operations: O(m log n) Union-Find operations: O(n log n) Minimum Spanning Trees

Minimum Spanning Trees

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