I/O-Efficient Graph Algorithms

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I/O-Efficient Graph Algorithms Norbert Zeh Duke University EEF Summer School on Massive Data Sets Århus, Denmark June 26 – July 1, 2002

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I/O-Efficient Graph Algorithms. Norbert Zeh Duke University EEF Summer School on Massive Data Sets Århus, Denmark June 26 – July 1, 2002. Motivation. For theoreticians: Graph problems are neat, often difficult, hence interesting For practitioners: - PowerPoint PPT Presentation

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Page 1: I/O-Efficient Graph Algorithms

I/O-Efficient Graph Algorithms

Norbert ZehDuke University

EEF Summer School on Massive Data SetsÅrhus, Denmark

June 26 – July 1, 2002

Page 2: I/O-Efficient Graph Algorithms

Motivation

For theoreticians:• Graph problems are neat, often difficult, hence

interesting

For practitioners:• Massive graphs arise in GIS, web modelling, ...• Problems in computational geometry can be

expressed as graph problems• Many abstract problems best viewed as graph

problems• Extreme: Pointer-based data structures =

graphs with extra information at their nodes

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Outline

Fundamental graph problems• List ranking• Algorithms for trees

• Euler tour• Tree labelling

• Graph searching• BFS/DFS

• Connectivity• Connected components• Minimum spanning tree

• Single source shortest paths

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Outline

• Techniques and data structures• Graph contraction• Time-forward processing• Tournament tree• Buffered repository tree

• Lower bounds• List ranking• Connectivity

• Planar graphs

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Introduction and “Simple” Problems

• List ranking• Euler tour• Tree labelling• Evaluating directed acyclic graphs• Greedy graph algorithms

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List Ranking

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Why Is List Ranking Non-Trivial?

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The internal memory algorithm spends (N) I/Os in the worst case.

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An Efficient List Ranking Algorithm

• Assume an independent set of size at least N/3 can be found efficiently (in O(sort(N)) I/Os).

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An Efficient List Ranking Algorithm

• Compressing L:• Sort elements in L \ I• Sort elements in I by their successor

pointers• Scan the two lists to update the label of

succ(v), for every element v I

• The I/O-complexity of this procedure is

Theorem: A list of size N can be ranked in O(sort(N)) I/Os.

NsortONsortOINI 3N2

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The Euler Tour Technique

Goal: Given a tree T, represent it by a list L so that certain computations on T can be performed by ranking L.

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The Euler Tour Technique

Theorem: Given the adjacency lists of the vertices in T, an Euler tour can be constructed in O(scan(N)) I/Os.

• Let {v,w1},…,{v,wr} be the edgesincident to v

• Then succ((wi,v)) = (v,wi+1)) v

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Rooting a Tree

• Choosing a vertex r as the root of a tree T defines parent-child relationships between adjacent nodes

• Rooting tree T =computing for every edge{v,w} who is the parentand who is the child

• v = p(w) if and only ifrank((v,w)) < rank((w,v))

Theorem: A tree can be rooted in O(sort(N)) I/Os.

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Computing a Preorder Numbering

Theorem: A preorder numbering of a rooted tree T can be computed in O(sort(N)) I/Os.

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Computing Subtree Sizes

Theorem: The nodes of T can be labelled with their subtree sizes in O(sort(N)) I/Os.

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Evaluating a Directed Acyclic Graph

• More general: Given a labelling , compute a labelling so that (v) is computed from (v) and (u1),…,(ur), where u1,…,ur are v’s in-neighbors

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Q:

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Time-Forward Processing

• Assume nodes are given in topologically sorted order.

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Use priority queue Q to send data along the edges.

(6,1,0)(4,2,1) (5,2,1) (6,1,0)(4,2,1) (4,3,0) (5,2,1) (5,3,0) (6,1,0)(4,2,1) (4,3,0) (5,2,1) (5,3,0) (6,1,0)(5,2,1) (5,3,0) (6,1,0)(5,2,1) (5,3,0) (6,1,0) (7,4,0) (8,4,0)(5,2,1) (5,3,0) (6,1,0) (7,4,0) (8,4,0)(6,1,0) (7,4,0) (8,4,0)(6,1,0) (6,5,1) (7,4,0) (7,5,1) (8,4,0) (8,5,1)(6,1,0) (6,5,1) (7,4,0) (7,5,1) (8,4,0) (8,5,1)(7,4,0) (7,5,1) (8,4,0) (8,5,1)(7,4,0) (7,5,1) (8,4,0) (8,5,1) (10,6,0)(7,4,0) (7,5,1) (8,4,0) (8,5,1) (10,6,0)(8,4,0) (8,5,1) (10,6,0)(8,4,0) (8,5,1) (9,7,1) (10,6,0) (10,7,1)(8,4,0) (8,5,1) (9,7,1) (10,6,0) (10,7,1)(9,7,1) (10,6,0) (10,7,1)(9,7,1) (9,8,0) (10,6,0) (10,7,1)(9,7,1) (9,8,0) (10,6,0) (10,7,1)(10,6,0) (10,7,1)(10,6,0) (10,7,1) (11,9,1) (12,9,1)(10,6,0) (10,7,1) (11,9,1) (12,9,1)(11,9,1) (12,9,1)(11,9,1) (11,10,0) (12,9,1) (12,10,0)(11,9,1) (11,10,0) (12,9,1) (12,10,0)(12,9,1) (12,10,0)

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Time-Forward Processing

Analysis:• Vertex set + adjacency lists scanned O(scan(|V| + |E|)) I/Os• Priority queue:

• Every edge inserted into and deleted from Qexactly once

O(|E|) priority queue operations O(sort(|E|)) I/Os

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Time-Forward Processing

Analysis:• Vertex set + adjacency lists scanned O(scan(|V| + |E|)) I/Os• Priority queue:

• Every edge inserted into and deleted from Qexactly once

O(|E|) priority queue operations O(sort(|E|)) I/Os

Theorem: A directed acyclic graph G = (V,E) can be evaluated in O(sort(|V| + |E|)) I/Os.

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Maximal Independent Set (MIS)

Algorithm GREEDYMIS:1. I 02. for every vertex v G do3. if no neighbor of v is in I then4. Add v to I5. end if6. end for

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Maximal Independent Set (MIS)

Algorithm GREEDYMIS:1. I 02. for every vertex v G do3. if no neighbor of v is in I then4. Add v to I5. end if6. end for

Observation: It suffices to consider all neighbors of v which have been visited in a previous iteration.

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Maximal Independent Set (MIS)

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Maximal Independent Set (MIS)

Theorem: A maximal independent set of a graphG = (V,E) can be computed in O(sort(|V|+|E|)) I/Os.

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Large Independent Set of a List

Corollary: An independent set of size at least N/3 for a list L of size N can be found in O(sort(N)) I/Os.

• Every vertex in an MIS I prevents two other vertices from being in I:

Every MIS has size at least N/3.

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Graph Connectivity

• Connected components• Minimum spanning tree

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ConnectivityA Semi-External Algorithm

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ConnectivityA Semi-External Algorithm

Analysis:• Scan vertex set to load vertices into main

memory• Scan edge set to carry out algorithm• O(scan(|V| + |E|)) I/Os

Theorem: The connected components of a graph can be computed in O(scan(|V| + |E|)) I/Os, provided that |V| M.

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ConnectivityThe General Case

Idea:• If |V| M

• Use semi-external algorithm• If |V| > M

• Identify simple connected subgraphs of G• Contract these subgraphs to obtain graph

G’ = (V’,E’) with |V’| c|V|, c < 1• Recursively compute connected

components of G’• Obtain labelling of connected components

of G from labelling of components of G’

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ConnectivityThe General Case

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ConnectivityThe General Case

Main steps:• Find smallest neighbors (easy)• Compute connected components of graph

H induced by selected edges• Contract each component into a single vertex

(easy)• Call the procedure recursively• Copy label of every vertex v G’ to all vertices

in G represented by v (easy)

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ConnectivityThe General Case

• Every connected component of H has size at least 2 |V’| |V|/2 recursive calls

Theorem: The connected components of a graphG = (V,E) can be computed in I/Os. logEsortO M

V

MVlog

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ConnectivityThe General Case

• Later: BFS in O(|V| + sort(|E|)) I/Os Can be used to identify connected components

• When |V| = |E|/B, algorithm takes O(sort(|E|)) I/Os

• Can stop recursion after recursive calls

Theorem: The connected components of a graphG = (V,E) can be computed in I/Os.

EBVlog

EBVlogEsortO

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Minimum Spanning Tree (MST)

Observation: Connectivity algorithm can be augmented to produce a spanning tree of G.

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Minimum Spanning Tree (MST)

To obtain a minimum spanning tree:• Choose edge of minimum weight incident to v

• Some book-keeping:• The weight of an edge e in the compressed

graph = the min weight of all edges represented by e

• When “e is added” to T, add in fact this minimum edge

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Minimum Spanning Tree (MST)

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Theorem: A MST of a graph G = (V,E) can be computed in I/Os. logEsortO M

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A Fast MST Algorithm

• Idea:• Assume MST can be computed in

O(|V| + sort(|E|)) I/Os• Again recursion can be stopped after

iterations

• Prim’s algorithm:

EBVlog

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A Fast MST Algorithm

• Maintain superset of blue edges in priority queue Q

• When edge {v,w} of minimum weight is retrieved, test whether v,w are both in T• Yes discard edge• No Add edge to MST and add all edges

incident to w to Q, except {v,w}(assuming that w T)

Problem: How to testwhether v,w T.

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A Fast MST Algorithm

• If v,w T, but {v,w} T, then both v and w have inserted edge {v,w} into Q

There are two copies of {v,w} in Q• They are consecutive Perform two DELETEMIN operations

• If {v,w} = {y,z}, discard both• Otherwise, add {v,w} to T and re-insert {y,z}

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A Fast MST Algorithm

Analysis:• O(|V| + scan(|E|)) I/Os for retrieving adjacency

lists• O(sort(|E|)) I/Os for priority queue operations

Theorem: A MST of a graph G = (V,E) can be found in O(|V| + sort(|E|)) I/Os.

Corollary: A MST of a graph G = (V,E) can be found in I/Os.

EBVlogEsortO

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Graph Contraction and Sparse Graphs

• A graph G = (V,E) is sparse if for any graph H obtainable from G through a series of edge contractions, |E(H)| = O(|V(H)|).

• For a sparse graph, the number of vertices and edges in G reduces by a constant factor in each iteration of the connectivity and MST algorithms.

Theorem: The connected components or a MST of a sparse graph with N vertices can be computed in O(sort(N)) I/Os.

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Three Techniques for Graph Algorithms

• Time-forward processing:• Express graph problems as evaluation

problems of DAGs• Graph contraction:

• Reduce the size of G while maintaining the properties of interest

• Solve problem recursively on compressed graph

• Construct solution for G from solution for compressed graph

• Bootstrapping:• Switch to generally less efficient algorithm as

soon as (part of the) input is small enough