Graph Algorithm Implementation for Network Analysis

1. Graph Algorithm Zhen Jiang West Chester University zjiang@wcupa.edu

2. Outline • Graph table, p362 • Depth first search, p399 • Width first search • Shortest path and Dijkstra’s algorithm, p366

3. Graph table for undirectional network Backbone list, to store all nodes NULL 1 2 3 4 head 1 2 1 1 2 3 3 2 3 2 4 Neighbor list, to store all neighbors 4

4. Graph table for directional network Backbone list, to store all nodes NULL 1 2 3 4 head 1 2 4 2 3 3 2 Neighbor list, to store all neighbors 4

5. public class Edge{private String p;private String v;private double weight;public Edge(String a, String b){ p = a; v = b; weight = 1;}public Edge(String a, String b, double w){ p = a; v = b; weight = w;}public String getDest(){ return v;}public double getWeight(){ return weight;}public String getSource() { return p;}}// end of Edge class

6. public class Node{private String source;private ArrayList<Edge> neighbors;public Node(String a){source = a;neighbors = new ArrayList<Edge>();}public Node(String a, ArrayList<Edge> b){source = a;neighbors = b;}public void addEdge(Edge x){neighbors.add(x);}public String getSource(){return source;}public int getNnum(){return neighbors.size();}public Edge getNeighbor(int i){return neighbors.get(i);}public boolean equalsV(String s){return source.equals(s);}public String toString(){… }}

7. public class Graph{private ArrayList<Node> head = new ArrayList<Node>(); public Graph(String filename, String delimiter){ … } public String toString(){String ret ="";for(int i = 0; i<head.size(); i++){ Node tmp = head.get(i); ret += tmp.toString();}return ret;} }

8. Depth First Search • Step 0: Push node 1 to SL, select node 1 as node u. • Step 1: If node u is not visited (not found in ST), save the value of node u in print-out string (if u is the destination, then the search stops), and push u to ST. • Step 2: Find a node v in link list of node u which is not visited yet (not in ST). • Step 3.1: If node v is found, push v into SL. Select v as u. • Step 3.2: Otherwise (in case not found), pop out (u from) SL. Select top node of SL as node u. • Step 4: Repeat the steps 1 to 4 until SL is empty.

9. Width First Search • Step 0: When node 1 <> destination, offer node 1 to QL (from the tail) and QT (Stack or ArrayList). • Step 1: Poll out the node in QL (from the head) as node u. • Step 2: Save the value of node u in printout String. • Step 3: Offer all the neighbors of node u which are not in QT to (the tail of) QL. Save them to QT as well. (if the destination is one of them, the search stops.) • Step 4: Repeat the steps 1 to 4 until QL is empty. • Suggest to use ArrayList for both Qs.

10. 103 104 public String width_search(String s){105 return width_search(s, null);106 }107 public String width_search(String s, String t){108 if(s==null||contains(s)<0) {109 throw new NoSuchElementException("Start vertex not found in width first search");110 } 111 // return spanning with search result attached.112 // if t != null, return the path (or entire spanning tree) 113 // to reach it.114 if(s.equals(t)) return "reached";115 ArrayList<Node> QL = new ArrayList<Node>(); 116 ArrayList<Node> QT = new ArrayList<Node>(); 117 String ret=""; //StringBuilder118 Node u = head.get(contains(s));119 QL.add(u);120 QT.add(u);121 while(QL.size()>0){122 Edge tmp;123 Node t2=null;124 u = QL.get(0);125 QL.remove(u);126 ret += u.getSource() + "=>";127 for(int i=0; i<u.getNnum(); i++){ 128 tmp = u.getNeighbor(i);129 t2 = head.get(contains(tmp.getDest()));130 if (!QT.contains(t2)){131 QT.add(t2);132 QL.add(t2);133 if(t2.equalsV(t)) return ret+tmp.getDest()+" (reached)"; 134 }135 }// end of for136 }137 return ret;138 }

11. Shortest path • Step 0: Suggest to use ArrayList to realize the following “Stack.” • Step 1: current node c= s, c.length = 0, stack.push (c, its length, and parent). If c is the source s then no need for parent. if s == d, return the current stack. • Step 2: min = 0, hop = , index = 0 // zero-based index • Step 3: pick up the index’th record in stack, say node u, its length u.length, and its parent w. • Step 4: find a neighbor of u in the table of neighbors, say v, such that v is not found in any item in stack and <u,v> +u.length< hop. • Step 5: if such a neighbor is found, hop=min=u.length + <u,v> and record_node = v (record_parent = u). • Step 6: go to step 4 until all the neighbors of u have been tried (all can be found in stack).

12. Step 7: index ++, go to step 3 until all the nodes have been tried (found in stack). • Step 8: c = record_node, c.length = min, stack_push(c, c.length, record_parent). If c = = d stop the entire process and goes to step 9 for data collection, otherwise go to step 2. • Step 9: (t, d.length, and t.parent) = (d, d.length, and d.parent) in stack, keep searching on (t.parent, t.parent.length, t.parent.parent), until t.parent = s.