Instructor: Lilian de Greef Quarter: Summer 2017

Instructor: Lilian de Greef Quarter: Summer 2017

CSE 373: Data Structures and Algorithms Lecture 15: Graph Data Structures, Topological Sort, and Traversals (DFS, BFS) Instructor: Lilian de Greef Quarter: Summer 2017 Today: Announcements Graph data structures Topological Sort Graph Traversals Depth First Search (DFS) Breadth First Search (BFS) Announcement: Received Course Feedback Whats working well: Walking through in-class examples Posted, printed, and annotated slides Interactive questions & in-class partner discussion Things to address: Amount to write on printed slides Why using polling system for in-class exercises Concern about not getting through entire slide deck Year in Program this Fall Wide range of student

backgrounds! Hence, using a range of teaching styles, pauses, etc. 20 10 0 hm s e Fr Represented majors: Engineering Math 40 Informatics 30 Geology 20 Spanish

10 Pre-major And more! om h p o S e or or i n Ju or i n Se h 5t

r a ye ua d a Gr te s t n e d tu r e h ot Last Time Programmed / Taken CS Course Science

Asian Language an 0 17 17 16 16 15 15 14 16 16 15 15 0 0 0 0 0 0 0 0 0 0

0 2 2 2 r2 r2 g 2 ter 2 g 2 te r 2 g 2 te r 2 ll ll ll e e a a a n n n F F F ri ri ri m

m n n i in i p p p m m S S S W W W Su Su Graph Data Structures A couple of different ways to store adjacencies What is the Data Structure? So graphs are really useful for lots of data and questions For example, whats the lowest-cost path from x to y

But we need a data structure that represents graphs The best one can depend on: Properties of the graph (e.g., dense versus sparse) The common queries (e.g., is (u,v) an edge? versus what are the neighbors of node u?) So well discuss the two standard graph representations Adjacency Matrix and Adjacency List Different trade-offs, particularly time versus space Adjacency Matrix Assign each node a number from 0 to |V|-1 A |V| x |V| matrix (i.e., 2-D array) of Booleans (or 1 vs. 0) If M is the matrix, then M[u][v] == true means there is an edge from u to v B (0) E S (1) M (3) 0

0 (2) 1 2 3 1 2 3 Adjacency Matrix Properties Running time to: Get a vertexs out-edges: Get a vertexs in-edges: Decide if some edge exists: Insert an edge: Delete an edge: 0 0 F

1 F 2 F 3 F 1 T F T T 2 F F T

T 3 F T T F Space requirements: B Best for sparse or dense graphs? (0) E S (1) M (3)

(2) Adjacency Matrix Properties How will the adjacency matrix vary for an undirected graph? Undirected will be symmetric around the diagonal How can we adapt the representation for weighted graphs? Instead of a Boolean, store a number in each cell Need some value to represent not an edge In some situations, 0 or -1 works Adjacency List Assign each node a number from 0 to |V|-1 An array of length |V| in which each entry stores a list of all adjacent vertices (e.g., linked list) B (0) E S (1) M (3)

0 (2) 1 2 3 Adjacency List Properties 0 1 0 Running time to: 2 3 / 3 1 Get all of a vertexs out-edges: where d is out-degree of vertex Get all of a vertexs in-edges:

(but could keep a second adjacency list for this!) Decide if some edge exists: where d is out-degree of source Insert an edge: (unless you need to check if its there) Delete an edge: where d is out-degree of source Space requirements: / B (0) 2 3 / 2 / E S (1) M (3)

Best for sparse or dense graphs? (2) Algorithms Okay, we can represent graphs Now well implement some useful and non-trivial algorithms! Topological Sort Shortest Paths Related: Determining if such a path exists Depth First Search Breadth First Search Graphs: Topological Sort Ordering vertices in a DAG Topological Sort Topological sort: Given a DAG, order all the vertices so that every vertex comes before all of its neighbors XYZ CSE 374 CSE 410 CSE 142 MATH 126

One example output: CSE 143 CSE 373 CSE 413 CSE 415 CSE 417 Questions and comments Why do we perform topological sorts only on DAGs? 2 0 Is there always a unique answer? 4 3 1 Do some DAGs have exactly 1 answer? Terminology: A DAG represents a partial order and a topological sort produces a total order that is consistent with it

A few of its uses Figuring out how to graduate Computing an order in which to recompute cells in a spreadsheet Determining an order to compile files using a Makefile In general, taking a dependency graph and finding an order of execution A First Algorithm for Topological Sort 1. Label (mark) each vertex with its in-degree Could write in a field in the vertex Could also do this via a data structure (e.g., array) on the side 2. While there are vertices not yet output: a) Choose a vertex v with in-degree of 0 b) Output v and conceptually remove it from the graph c) For each vertex u adjacent to v (i.e. u such that (v,u) in E), decrement the in-degree of u Example Output: XYZ

CSE 374 CSE 410 CSE 142 CSE 143 CSE 373 CSE 413 CSE 415 MATH 126 CSE 417 Node: 126 Removed? In-degree: 0 1 3 142 143 0

374 2 373 1 410 413 1 415 1 417 1 XYZ 1 Notice Needed a vertex with in-degree 0 to start Will always have at least 1 because Ties among vertices with in-degrees of 0 can be broken arbitrarily Can be more than one correct answer, by definition, depending on the graph

Running time? labelEachVertexWithItsInDegree(); for(i = 0; i < numVertices; i++){ v = findNewVertexOfDegreeZero(); put v next in output for each u adjacent to v u.indegree--; } What is the worst-case running time? Initialization (assuming adjacency list) Sum of all find-new-vertex (because each O(|V|)) Sum of all decrements (assuming adjacency list) So total is not good for a sparse graph! Doing better The trick is to avoid searching for a zero-degree node every time! Keep the pending zero-degree nodes in a list, stack, queue, bag, table, or something Order we process them affects output but not correctness or efficiency, provided that add/remove are both O(1) Using a queue:

1. Label each vertex with its in-degree, enqueue 0-degree nodes 2. While queue is not empty a) v = dequeue() b) Output v and remove it from the graph c) For each vertex u adjacent to v (i.e. u such that (v,u) in E), decrement the in-degree of u, if new degree is 0, enqueue it Example: Topological Sort Using Queues Node B C A A B C D 0 1

1 2 Removed? In-degree D Queue: Output: The trick is to avoid searching for a zero-degree node every time! 1. Label each vertex with its in-degree, enqueue 0-degree nodes 2. While queue is not empty a) v = dequeue() b)

Output v and remove it from the graph c) For each vertex u adjacent to v (i.e. u such that (v,u) in E), decrement the in-degree of u, if new degree is 0, enqueue it Running time? labelAllAndEnqueueZeros(); for(i=0; ctr < numVertices; ctr++){ v = dequeue(); put v next in output for each u adjacent to v { u.indegree--; if(u.indegree==0) enqueue(u); } } What is the worst-case running time? Initialization: (assuming adjacency list) Sum of all enqueues and dequeues: Sum of all decrements: (assuming adjacency list) Total: much better for sparse graph!

Graph Traversals Depth- and Breadth- First Searches! Introductory Example: Graph Traversals How would a computer systematically find a path through the maze? Source A B C D E F G H I

J K L M N O Destination Note: under the hood, were using a graph to represent the maze In graph terminology: find a path (if any) from one vertex to another. Source Source A B C D

E A B C D E F G H I J F G H

I J K L M N O K L M N O Destination

Destination Find a path (if any) from one vertex to another. Let s try kee ping track recur sively Idea: Repeatedly explore and keep track of adjacent vertices. Mark each vertex we visit, so we dont process each more than once. itional d d a s a e r o t S ertices v in le b

ia r a v Source A B C D E F G H I J

K L M N O Destination Depth First Search (DFS) Depth First Search (DFS): Explore as far as possible along each branch before backtracking Repeatedly explore adjacent vertices using or Mark each vertex we visit, so we dont process each more than once. Example pseudocode: DFS(Node start) { mark and process start for each node u adjacent to start if u is not marked DFS(u) }

Find a path (if any) from one vertex to another. Now let s tr y using a que ue! Idea: Repeatedly explore and keep track of adjacent vertices. Mark each vertex we visit, so we dont process each more than once. itional d d a s a e r o t S ertices v in le b ia r

a v Source A B C D E F G H I J K

L M N O Destination Breadth First Search (BFS) Breadth First Search (BFS): Explore neighbors first, before moving to the next level of neighbors. Repeatedly explore adjacent vertices using Mark each vertex we visit, so we dont process each more than once. Example pseudocode: BFS(Node start) { initialize queue q and enqueue start mark start as visited while(q is not empty) { next = q.dequeue() // and process for each node u adjacent to next if(u is not marked) mark u and enqueue onto q } }

Practice time! What is one possible order of visiting the nodes of the following graph when using Breadth First Search (BFS)? N M R Q O P A) MNOPQR C) QMNPRO B) NQMPOR D) QMNPOR (space for scratch work / notes) Running Time and Traversal Order

Assuming add and remove are O(1), entire traversal is Use an adjacency list representation The order we traverse depends entirely on add and remove For DFS: For BFS: Comparison (useful for Design Decisions!) Which one finds shortest paths? i.e. which is better for what is the shortest path from x to y when theres more than one possible path? Which one can use less space in finding a path? A third approach: Iterative deepening (IDFS): Try DFS but disallow recursion more than K levels deep If that fails, increment K and start the entire search over Like BFS, finds shortest paths. Like DFS, less space. Graph Traversal Uses In addition to finding paths, we can use graph traversals to answer: What are all the vertices reachable from a starting vertex? Is an undirected graph connected? Is a directed graph strongly connected?

But what if we want to actually output the path? How to do it: Instead of just marking a node, store the previous node along the path When you reach the goal, follow path fields back to where you started (and then reverse the answer) If just wanted path length, could put the integer distance at each node instead once Saving the Path Our graph traversals can answer the reachability question: Is there a path from node x to node y? But what if we want to actually output the path? How to do it: Instead of just marking a node, store the previous node along the path When you reach the goal, follow path fields back to where you started (and then reverse the answer) If just wanted path length, could put the integer distance at each node instead Source: https://xkcd.com/761/

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