Basic Graph Search. Algorithms and Data Structures, Fall Rasmus Pagh. Based on slides by Kevin Wayne, Princeton

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1 Algorithms and Data Structures, Fall Basic Graph Search Rasmus Pagh Based on slides by Kevin Wayne, Princeton Algorithms, th Edition Robert Sedgewick and Kevin Wayne Copyright

2 Mid-term evaluation Thanks to those of you who provided feedback - Will try to choose case studies for lecture on November that allow us to rehearse algorithm analysis basics. - Will start posing previous exam problems for exercises (from next week). Also, there will be a trial exam on November 8.

3 Undirected graphs Graph. Set of vertices connected pairwise by edges. Why study graph algorithms? Interesting and broadly useful abstraction. Challenging branch of computer science and discrete math. Hundreds of graph algorithms known. Thousands of practical applications.

4 Protein-protein interaction network Reference: Jeong et al, Nature Review Genetics

5 The Internet as mapped by the Opte Project

6 million Facebook friends "Visualizing Friendships" by Paul Butler 6

7 Relationship graph Relationship graph at "Jefferson High" 7

8 Graph applications graph vertex edge communication telephone, computer fiber optic cable circuit gate, register, processor wire mechanical joint rod, beam, spring financial stock, currency transactions transportation street intersection, airport highway, airway route internet class C network connection game board position legal move social relationship person, actor friendship, movie cast neural network neuron synapse protein network protein protein-protein interaction chemical compound molecule bond 8

9 Graph terminology Path. Sequence of vertices connected by edges. Cycle. Path whose first and last vertices are the same. Two vertices are connected if there is a path between them. cycle of length edge vertex path of length vertex of degree connected components Anatomy of a graph 9

10 Some graph-processing problems Path. Is there a path between s and t? Shortest path. What is the shortest path between s and t? Cycle. Is there a cycle in the graph? Euler tour. Is there a cycle that uses each edge exactly once? Hamilton tour. Is there a cycle that uses each vertex exactly once? Connectivity. Is there a way to connect all of the vertices? MST. What is the best way to connect all of the vertices? Biconnectivity. Is there a vertex whose removal disconnects the graph? Planarity. Can you draw the graph in the plane with no crossing edges? Graph isomorphism. Do two adjacency lists represent the same graph? Challenge. Which of these problems are easy? difficult? intractable?

11 Graph representation Graph drawing. Provides intuition about the structure of the graph. Caveat. Intuition can be misleading. Two drawings of the same graph

12 Graph representation Vertex representation. This lecture: use integers between and V-. Applications: convert between names and integers with symbol table. A B C G 6 symbol table D E F Anomalies. self-loop parallel edges Anomalies

13 Graph API public class Graph Graph(int V) Graph(In in) create an empty graph with V vertices create a graph from input stream void addedge(int v, int w) add an edge v-w Iterable<Integer> adj(int v) vertices adjacent to v int V() int E() String tostring() number of vertices number of edges string representation

14 Adjacency-matrix graph representation Maintain a two-dimensional V-by-V boolean array; for each edge v-w in graph: adj[v][w] = adj[w][v] = true. two entries for each edge

15 Adjacency-list graph representation Maintain vertex-indexed array of lists. (use Bag abstraction) 6 Bag objects adj[] (alt: HashSet) representations of the same edge

16 Graph representations In practice. Use adjacency-lists representation. Algorithms based on iterating over vertices adjacent to v. Real-world graphs tend to be sparse. huge number of vertices, small average vertex degree representation space add edge edge between v and w? iterate over vertices adjacent to v? adjacency matrix V * V adjacency lists E + V degree(v) degree(v) * disallows parallel edges 6

17 Graph representations In practice. Use adjacency-lists representation. Algorithms based on iterating over vertices adjacent to v. Real-world graphs tend to be sparse. huge number of vertices, small average vertex degree sparse (E = ) dense (E = ) Two graphs (V = ) 7

18 Maze exploration: A graph search problem Maze graphs. Vertex = intersection. Edge = passage. intersection passage Goal. Explore every intersection in the maze. 8

19 Trémaux maze exploration Algorithm. Unroll a ball of string behind you. Mark each visited intersection and each visited passage. Retrace steps when no unvisited options. Theseus and the Minotaur 9

20 Maze exploration

21 Maze exploration

22 Depth-first search Goal. Systematically search through a graph. Idea. Mimic maze exploration. DFS (to visit a vertex v) Mark v as visited. Recursively visit all unmarked vertices w adjacent to v. Typical applications. [ahead] Find all vertices connected to a given source vertex. Find a path between two vertices.

23 Design pattern for graph processing Design pattern. Decouple graph data type from graph processing. public class Search Search(Graph G, int s) find vertices connected to s boolean marked(int v) is vertex v connected to s? int count() how many vertices connected to s? Typical client program. Create a Graph. Pass the Graph to a graph-processing routine, e.g., Search. Query the graph-processing routine for information. Search search = new Search(G, s); for (int v = ; v < G.V(); v++) if (search.marked(v)) StdOut.println(v); print all vertices connected to s

24 Depth-first search (reachability) public class DepthFirstSearch { private boolean[] marked; true if connected to s public DepthFirstSearch(Graph G, int s) { marked = new boolean[g.v()]; dfs(g, s); } constructor marks vertices connected to s private void dfs(graph G, int v) { marked[v] = true; for (int w : G.adj(v)) if (!marked[w]) dfs(g, w); } recursive DFS does the work } public boolean marked(int v) { return marked[v]; } client can ask whether vertex v is connected to s

25 Depth-first search properties Proposition. DFS marks all vertices connected to s in time proportional to the sum of their degrees. Pf. Correctness: - if w marked, then w connected to s (why?) - if w connected to s, then w marked source s set of marked vertices (if w unmarked, then consider last edge on a path from s to w that goes from a marked vertex to an unmarked one) set of unmarked vertices v x no such edge can exist Running time: each vertex connected to s is visited once. w

26 Depth-first search application: preparing for a date

27 Depth-first search application: flood fill Change color of entire blob of neighboring red pixels to blue. Build a grid graph. Vertex: pixel. Edge: between two adjacent red pixels. Blob: all pixels connected to given pixel. recolor red blob to blue 6

28 Depth-first search application: flood fill Change color of entire blob of neighboring red pixels to blue. Build a grid graph. Vertex: pixel. Edge: between two adjacent red pixels. Blob: all pixels connected to given pixel. recolor red blob to blue 7

29 Pathfinding in graphs Goal. Does there exist a path from s to t? If yes, find any such path. 8 amortized

30 Pathfinding in graphs Goal. Does there exist a path from s to t? If yes, find any such path. public class Paths Paths(Graph G, int s) find paths in G from source s boolean haspathto(int v) is there a path from s to v? Iterable<Integer> pathto(int v) path from s to v; null if no such path Depth-first search. After linear-time preprocessing, can recover path itself in time proportional to its length. Idea: Mark each visited vertex by keeping track of edge taken to visit it. 9

31 Depth-first search (pathfinding) public class DepthFirstPaths { private boolean[] marked; private int[] edgeto; private final int s; public DepthFirstPaths(Graph G, int s) { marked = new boolean[g.v()]; edgeto = new int[g.v()]; this.s = s; dfs(g, s); } private void dfs(graph G, int v) { marked[v] = true; for (int w : G.adj(v)) if (!marked[w]) { edgeto[w] = v; dfs(g, w); } } parent-link representation of DFS tree set parent link } public boolean haspathto(int v) public Iterable<Integer> pathto(int v) ahead

32 Depth-first search (pathfinding iterator) edgeto[] is a parent-link representation of a tree rooted at s. x path edgeto[] public boolean haspathto(int v) { return marked[v]; } public Iterable<Integer> pathto(int v) { if (!haspathto(v)) return null; Stack<Integer> path = new Stack<Integer>(); for (int x = v; x!= s; x = edgeto[x]) path.push(x); path.push(s); return path; }

33 Breadth-first search Depth-first search. Put unvisited vertices on a stack. Breadth-first search. Put unvisited vertices on a queue. Shortest path. Find path from s to t that uses fewest number of edges. BFS (from source vertex s) Put s onto a FIFO queue, and mark s as visited. Repeat until the queue is empty: - remove the least recently added vertex v - add each of v's unvisited neighbors to the queue, and mark them as visited. Intuition. BFS examines vertices in increasing distance from s.

34 Breadth-first search (pathfinding) q marked[] edgeto[] adj[] T private void bfs(graph G, int s) { Queue<Integer> q = new Queue<Integer>(); q.enqueue(s); marked[s] = true; while (!q.isempty()) { int v = q.dequeue(); for (int w : G.adj(v)) if (!marked[w]) { q.enqueue(w); marked[w] = true; edgeto[w] = v; } } } T T T T T T T T T T T T T T T T T T T T T T T T T T T T

35 Breadth-first search properties Proposition. BFS computes shortest path (number of edges) from s in a connected graph in time proportional to E + V. Pf. Correctness: queue always consists of zero or more vertices of distance k from s, followed by zero or more vertices of distance k +. Running time: each vertex connected to s is visited once. standard drawing dist = dist = dist =

36 DFS and BFS space In the worst case, need to store V vertices on the stack/queue. Possible to do this with less space? In, Omer Reingold showed that it is possible to determine the existence of a path using just a constant number of integers (in addition to the input). However, the running time of Reingold s algorithm is high, between V and V 8.

37 Breadth-first search application: routing Fewest number of hops in a communication network. ARPANET, July 977 6

38 Kevin Bacon graph Include a vertex for each performer and for each movie. Connect a movie to all performers that appear in that movie. Compute shortest path from s = Kevin Bacon. Caligola Patrick Allen Dial M for Murder Grace Kelly John Gielgud Glenn Close Portrait of a Lady The Stepford Wives Nicole Kidman The Eagle Has Landed To Catch a Thief High Noon Lloyd Bridges Murder on the Orient Express Cold Mountain Donald Sutherland Kathleen Quinlan Joe Versus the Volcano Hamlet movie vertex Enigma Eternal Sunshine of the Spotless Mind Vernon Dobtcheff Jude Kate Winslet An American Haunting The Woodsman Wild Things John Belushi Meryl Streep Animal House Kevin Bacon The River Wild Titanic Apollo Bill Paxton Paul Herbert Yves Aubert Tom Hanks The Da Vinci Code Shane Zaza performer vertex Serretta Wilson 7

39 Connectivity queries Def. Vertices v and w are connected if there is a path between them. Goal. Preprocess graph to answer queries: is v connected to w? in constant time. public class CC CC(Graph G) find connected components in G boolean connected(int v, int w) are v and w connected? int count() number of connected components int id(int v) component identifier for v 8

40 Connected components Def. A connected component is a maximal set of connected vertices. 9

41 Connected components Def. A connected component is a maximal set of connected vertices. 6 connected components 9

42 Connected components Goal. Partition vertices into connected components. Connected components Initialize all vertices v as unmarked. For each unmarked vertex v, run DFS to identify all vertices discovered as part of the same component. V tinyg.txt E

43 Graph-processing challenge Problem. Is a graph bipartite? How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. No one knows. Impossible

44 Graph-processing challenge Problem. Find a cycle. How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. No one knows. Impossible

45 Graph-processing challenge Problem. Find a cycle that visits every vertex. Assumption. Need to visit each vertex exactly once. How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. No one knows. Impossible

46 Graph-processing challenge Problem. Find a cycle that visits every vertex. Assumption. Need to visit each vertex exactly once. How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. Hamiltonian cycle No one knows. (classical NP-complete problem) Impossible Andreas Björklund

47 Graph-processing challenge Problem. Are two graphs identical except for vertex names? How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. No one knows. Impossible ,,, 6,,, 6

48 Graph-processing challenge Problem. Are two graphs identical except for vertex names? How difficult? Any COS 6 student could do it. Need to be a typical diligent COS 6 student. Hire an expert. Intractable. No one knows. Impossible. graph isomorphism is longstanding open problem ,,, 6,,, 6 6

49 Directed graphs Digraph. Set of vertices connected pairwise by directed edges. vertex of outdegree and indegree directed path from to directed cycle 7

50 Road network Vertex = intersection; edge = one-way street. 8 Google - Map data 8 Sanborn, NAVTEQ - Terms of Use 8

51 WordNet graph Vertex = synset; edge = hypernym relationship. 9

52 Digraph applications digraph vertex directed edge transportation street intersection one-way street web web page hyperlink food web species predator-prey relationship WordNet synset hypernym scheduling task precedence constraint financial bank transaction cell phone person placed call infectious disease person infection game board position legal move citation journal article citation object graph object pointer inheritance hierarchy class inherits from control flow code block jump

53 Some digraph problems Path. Is there a directed path from s to t? Shortest path. What is the shortest directed path from s to t? NEW Topological sort. Can you draw the digraph so that all edges point upwards? NEW Strong connectivity. Is there a directed path between all pairs of vertices? NEW Transitive closure. For which vertices v and w is there a path from v to w? NEW PageRank. What is the importance of a web page?

54 Digraph representations Same as for undirected graphs. In practice. Use adjacency-list representation. Algorithms based on iterating over vertices adjacent from v. Real-world digraphs tend to be sparse. representation space insert edge from v to w edge from v to w? iterate over vertices adjacent from v? adjacency matrix V V adjacency list E + V outdegree(v) outdegree(v) disallows parallel edges

55 Depth-first search in digraphs Same method as for undirected graphs. Every undirected graph is a digraph (with edges in both directions). DFS is a digraph algorithm. DFS (to visit a vertex v) Mark v as visited. Recursively visit all unmarked vertices w adjacent from v.

56 Breadth-first search in digraphs Same method as for undirected graphs. Every undirected graph is a digraph (with edges in both directions). BFS is a digraph algorithm. BFS (from source vertex s) Put s onto a FIFO queue, and mark s as visited. Repeat until the queue is empty: - remove the least recently added vertex v - for each unmarked vertex adjacent from v: add to queue and mark as visited.. s Proposition. BFS computes shortest paths (fewest number of edges).

57 Reachability application: mark-sweep garbage collector Every data structure is a digraph. Vertex = object. Edge = reference. Roots. Objects known to be directly accessible by program (e.g., stack). Reachable objects. Objects indirectly accessible by program (starting at a root and following a chain of pointers). PROBLEM SESSION Another approach to garbage collection is to maintain a count of the number of references to each object, and remove objects with count. - Argue that this will not always get rid of all unused objects. - How can we find cycles in a directed graph?

58 Precedence scheduling Goal. Given a set of tasks to be completed with precedence constraints, in which order should we schedule the tasks? Graph model. vertex = task; edge = precedence constraint.. Algorithms. Complexity Theory. Artificial Intelligence. Intro to CS. Cryptography. Scientific Computing 6 6. Advanced Programming tasks precedence constraint graph feasible schedule 6

59 Precedence scheduling Goal. Given a set of tasks to be completed with precedence constraints, in which order should we schedule the tasks? Graph model. vertex = task; edge = precedence constraint.. Algorithms. Complexity Theory. Artificial Intelligence. Intro to CS. Cryptography. Scientific Computing 6 6. Advanced Programming tasks precedence constraint graph feasible schedule 6

60 Topological sort DAG. Directed acyclic graph. Topological sort. Redraw DAG so all edges point up directed edges DAG Solution. DFS. What else? topological order 7

61 Depth-first search order public class DepthFirstOrder { private boolean[] marked; private Stack<Integer> reversepost; public DepthFirstOrder(Digraph G) { reversepost = new Stack<Integer>(); marked = new boolean[g.v()]; for (int v = ; v < G.V(); v++) if (!marked[v]) dfs(g, v); } private void dfs(digraph G, int v) { marked[v] = true; for (int w : G.adj(v)) if (!marked[w]) dfs(g, w); reversepost.push(v); } } public Iterable<Integer> reversepost() { return reversepost; } returns all vertices in reverse DFS postorder 8

62 Reverse DFS postorder in a DAG marked[] reversepost

63 Reverse DFS postorder in a DAG marked[] reversepost dfs()

64 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs()

65 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs()

66 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done

67 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done

68 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs()

69 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done

70 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs()

71 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check

72 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done

73 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done

74 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check

75 Reverse DFS postorder in a DAG marked[] reversepost 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check

76 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() 6 6 9

77 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check 6 6 9

78 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check 6 6 9

79 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check 6 6 9

80 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 6 9

81 Reverse DFS postorder in a DAG marked[] reversepost 6 6 dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done

82 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 6 9

83 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 check 6 9

84 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 check 6 check 6 9

85 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 check 6 check 6 check 6 6 9

86 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 check 6 check 6 check 6 6 done 6 9

87 Reverse DFS postorder in a DAG marked[] reversepost dfs() - dfs() - dfs() - done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done 6 done 6 check 6 check 6 check 6 6 done 6 reverse DFS postorder is a topological order! 9

88 Topological sort in a DAG: correctness proof Proposition. Reverse DFS postorder of a DAG is a topological order. Pf. Consider any edge v w. When dfs(g, v) is called: Case : dfs(g, w) has already been called and returned. Thus, w was put on the stack before v. Case : dfs(g, w) has not yet been called. It will get called directly or indirectly by dfs(g, v) and will finish before dfs(g, v). Thus, w will be put on the stack before v. Case : dfs(g, w) has already been called, but has not returned. Can t happen in a DAG: function call stack contains path from w to v, so v w would complete a cycle. Ex: case case dfs() dfs() dfs() done done dfs() done dfs() check done done check check dfs() check check check dfs(6) 6 done done check check check 6 done all vertices adjacent from are done before is done, so they appear after in topological order 6

89 Directed cycle detection application: spreadsheet recalculation Microsoft Excel does cycle detection (and has a circular reference toolbar!) 6

90 Conclusion Graphs can be used to model a large variety of computational problems. Efficient graph algorithms (more to come!) can often be used as building blocks for solving given problems. Releted course goals: Choose among and make use of the most important algorithms and data structures in libraries, based on knowledge of their complexity. Design algorithms for ad hoc problems by using and combining known algorithms and data structures. 6