Transcription

Basic Graph AlgorithmsJaehyun ParkCS 97SIStanford UniversityJune 29, 2015

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Graphs2

Graphs An abstract way of representing connectivity using nodes (alsocalled vertices) and edgesWe will label the nodes from 1 to nm edges connect some pairs of nodes– Edges can be either one-directional (directed) or bidirectional GraphsNodes and edges can have some auxiliary information3

Why Study Graphs? Lots of problems formulated and solved in terms of graphs–––––––GraphsShortest path problemsNetwork flow problemsMatching problems2-SAT problemGraph coloring problemTraveling Salesman Problem (TSP): still unsolved!and many more.4

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Adjacency Matrix and Adjacency List5

Storing Graphs Need to store both the set of nodes V and the set of edges E– Nodes can be stored in an array– Edges must be stored in some other way Want to support operations such as:– Retrieving all edges incident to a particular node– Testing if given two nodes are directly connected Use either adjacency matrix or adjacency list to store theedgesAdjacency Matrix and Adjacency List6

Adjacency Matrix An easy way to store connectivity information– Checking if two nodes are directly connected: O(1) time Make an n n matrix A– aij 1 if there is an edge from i to j– aij 0 otherwise Uses Θ(n2 ) memory– Only use when n is less than a few thousands,– and when the graph is denseAdjacency Matrix and Adjacency List7

Adjacency List Each node has a list of outgoing edges from it– Easy to iterate over edges incident to a certain node– The lists have variable lengths– Space usage: Θ(n m)Adjacency Matrix and Adjacency List8

Implementing Adjacency List Solution 1. Using linked lists– Too much memory/time overhead– Using dynamic allocated memory or pointers is bad Solution 2. Using an array of vectors– Easier to code, no bad memory issues– But very slow Solution 3. Using arrays (!)– Assuming the total number of edges is known– Very fast and memory-efficientAdjacency Matrix and Adjacency List9

Implementation Using ArraysAdjacency Matrix and Adjacency List10

Implementation Using Arrays Have two arrays E of size m and LE of size n– E contains the edges– LE contains the starting pointers of the edge lists Initialize LE[i] -1 for all i– LE[i] 0 is also fine if the arrays are 1-indexed Inserting a new edge from u to v with ID kE[k].to vE[k].nextID LE[u]LE[u] kAdjacency Matrix and Adjacency List11

Implementation Using Arrays Iterating over all edges starting at u:for(ID LE[u]; ID ! -1; ID E[ID].nextID)// E[ID] is an edge starting from u Once built, it’s hard to modify the edges– The graph better be static!– But adding more edges is easyAdjacency Matrix and Adjacency List12

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Special Graphs13

Tree A connected acyclic graphMost important type of special graphs Alternate equivalent definitions: – Many problems are easier to solve on trees–––––A connected graph with n 1 edgesAn acyclic graph with n 1 edgesThere is exactly one path between every pair of nodesAn acyclic graph but adding any edge results in a cycleA connected graph but removing any edge disconnects itSpecial Graphs14

Other Special Graphs Directed Acyclic Graph (DAG): the name says what it is– Equivalent to a partial ordering of nodes Bipartite Graph: Nodes can be separated into two groups Sand T such that edges exist between S and T only (no edgeswithin S or within T )Special Graphs15

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Depth-First and Breadth-First Search16

Graph Traversal The most basic graph algorithm that visits nodes of a graphin certain order Used as a subroutine in many other algorithms We will cover two algorithms– Depth-First Search (DFS): uses recursion (stack)– Breadth-First Search (BFS): uses queueDepth-First and Breadth-First Search17

Depth-First SearchDFS(v): visits all the nodes reachable from v in depth-first order Mark v as visitedFor each edge v u:– If u is not visited, call DFS(u) Use non-recursive version if recursion depth is too big (over afew thousands)– Replace recursive calls with a stackDepth-First and Breadth-First Search18

Breadth-First SearchBFS(v): visits all the nodes reachable from v in breadth-first order Initialize a queue Q Mark v as visited and push it to QWhile Q is not empty: – Take the front element of Q and call it w– For each edge w u: If u is not visited, mark it as visited and push it to QDepth-First and Breadth-First Search19

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Topological Sort20

Topological Sort Input: a DAG G (V, E) Output: an ordering of nodes such that for each edge u v,u comes before vThere can be many answers – e.g., both {6, 1, 3, 2, 7, 4, 5, 8} and {1, 6, 2, 3, 4, 5, 7, 8} arevalid orderings for the graph belowTopological Sort21

Topological Sort Any node without an incoming edge can be the first element After deciding the first node, remove outgoing edges from it Repeat! Time complexity: O(n2 m)– Too slow.Topological Sort22

Topological Sort (faster version) Precompute the number of incoming edges deg(v) for eachnode v Put all nodes v with deg(v) 0 into a queue QRepeat until Q becomes empty: – Take v from Q– For each edge v u: Decrement deg(u) (essentially removing the edge v u)If deg(u) 0, push u to QTime complexity: Θ(n m)Topological Sort23

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Eulerian Circuit24

Eulerian Circuit Given an undirected graph G Want to find a sequence of nodes that visits every edgeexactly once and comes back to the starting point Eulerian circuits exist if and only if– G is connected– and each node has an even degreeEulerian Circuit25

Constructive Proof of Existence Pick any node in G and walk randomly without using thesame edge more than onceEach node is of even degree, so when you enter a node, therewill be an unused edge you exit through– Except at the starting point, at which you can get stuck When you get stuck, what you have is a cycle– Remove the cycle and repeat the process in each connectedcomponent– Glue the cycles together to finish!Eulerian Circuit26

Related Problems Eulerian path: exists if and only if the graph is connected andthe number of nodes with odd degree is 0 or 2. Hamiltonian path/cycle: a path/cycle that visits every node inthe graph exactly once. Looks similar but very hard (stillunsolved)!Eulerian Circuit27

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Minimum Spanning Tree (MST)28

Minimum Spanning Tree (MST) Given an undirected weighted graph G (V, E) Want to find a subset of E with the minimum total weightthat connects all the nodes into a tree We will cover two algorithms:– Kruskal’s algorithm– Prim’s algorithmMinimum Spanning Tree (MST)29

Kruskal’s Algorithm Main idea: the edge e with the smallest weight has to be inthe MSTSimple proof:– Assume not. Take the MST T that doesn’t contain e .– Add e to T , which results in a cycle.– Remove the edge with the highest weight from the cycle. The removed edge cannot be e since it has the smallestweight.– Now we have a better spanning tree than T– Contradiction!Minimum Spanning Tree (MST)30

Kruskal’s Algorithm Another main idea: after an edge is chosen, the two nodes atthe ends can be merged and considered as a single node(supernode)Pseudocode:– Sort the edges in increasing order of weight– Repeat until there is one supernode left: Take the minimum weight edge e If e connects two different supernodes, then connect themand merge the supernodes (use union-find)– Otherwise, ignore e and try the next edgeMinimum Spanning Tree (MST)31

Prim’s Algorithm Main idea:– Maintain a set S that starts out with a single node s– Find the smallest weighted edge e (u, v) that connectsu S and v /S– Add e to the MST, add v to S– Repeat until S V Differs from Kruskal’s in that we grow a single supernode Sinstead of growing multiple ones at the same timeMinimum Spanning Tree (MST)32

Prim’s Algorithm Pseudocode Initialize S : {s}, Dv : cost(s, v) for every v– If there is no edge between s and v, cost(s, v) Repeat until S V :– Find v / S with smallest Dv Use a priority queue or a simple linear search– Add v to S, add Dv to the total weight of the MST– For each edge (v, w): Update Dw : min(Dw , cost(v, w))Can be modified to compute the actual MST along with thetotal weightMinimum Spanning Tree (MST)33

Kruskal’s vs Prim’s Kruskal’s Algorithm– Takes O(m log m) time– Pretty easy to code– Generally slower than Prim’s Prim’s Algorithm– Time complexity depends on the implementation: Can be O(n2 m), O(m log n), or O(m n log n)– A bit trickier to code– Generally faster than Kruskal’sMinimum Spanning Tree (MST)34

OutlineGraphsAdjacency Matrix and Adjacency ListSpecial GraphsDepth-First and Breadth-First SearchTopological SortEulerian CircuitMinimum Spanning Tree (MST)Strongly Connected Components (SCC)Strongly Connected Components (SCC)35

Strongly Connected Components (SCC) Given a directed graph G (V, E) A graph is strongly connected if all nodes are reachable fromevery single node in V Strongly connected components of G are maximal stronglyconnected subgraphs of G The graph below has 3 SCCs: {a, b, e}, {c, d, h}, {f, g}Strongly Connected Components (SCC)36

Kosaraju’s Algorithm Initialize counter c : 0While not all nodes are labeled:– Choose an arbitrary unlabeled node v– Start DFS from v Check the current node x as visitedRecurse on all unvisited neighborsAfter the DFS calls are finished, increment c and set the labelof x as cReverse the direction of all the edgesFor node v with label n, n 1, . . . , 1:– Find all reachable nodes from v and group them as an SCCStrongly Connected Components (SCC)37

Kosaraju’s Algorithm We won’t prove why this worksTwo graph traversals are performed– Running time: Θ(n m) Other SCC algorithms exist but this one is particularly easy tocode– and asymptotically optimalStrongly Connected Components (SCC)38