1. Compact Representations of Separable Graphs From a paper of the same title submitted to SODA by: Dan Blandford and Guy Blelloch and Ian Kash

2. Standard Graph Representations Standard Adjacency List - |V| + 2|E| words = O(|E|lg|V|) bits - |V| + |E| words if stored as an array For arbitrary graphs, lower bound is O(|E| lg (|V| 2 /|E|)) bits, which adjacency list meets for sparse graphs, i.e. |E| = O(|V|)

3. Representations for Planar Graphs that use O(|V|) bits No Query Support - Turan [1984], First to come up with an O(|V|) bit compression scheme - Keeler and Westbrook [1995], Improved constants - Deo and Litow [1998], First use of separators - He, Kao and Lu [2000], Optimal high order term Query Support - Jacobson [1989], O( lg |V|) adjacency queries - Munro and Raman [1997], O(1) adjacency queries - Based on page embeddings and balanced parentheses, so not generally applicable

4. Graph Separators A Vertex SeparatorAn Edge Separator Formally, a class of Graphs is separable if: 1)Every member has a cut set of size βf(|V|) for some β > 0 such that 2)Each component has at most αn vertices for some α < 1 We concern ourselves with the case f(|V|) = |V| c, 0 < c < 1

5. Classes of Graphs With Small Separators Many Interesting and Useful Classes - Planar (and all bounded genus) - Telephone / Power / Road networks - 3D Meshes (Miller et al. [1997]) - Link Structure of Web

6. Results Theory for both edge and vertex separators Compress Graphs to O(|V|) bits Meets Lower Bound for Query Times - Degree Queries – O(1) - Adjacency Queries – O(1) - Neighbor Queries – O(D), D = Degree Implementation for edge separators

7. Main Ideas 1) Build separator tree 2) Order based on tree 3) Compress using difference encoded adjacency table 4) Use root find tree* * Vertex separators only

8. A Graph and Its Separator Tree 1 24 3 1 Final Numbering: 12 3 4 11 1113 2 1 4 41 2 3 32 4

9. Difference Coded Adjacency Table Instead of storing absolute numbers, store offsets i.e.: 1,1,3,2,1 instead of 1,2,5,7,8 Makes numbers stored smaller By itself, it offers no advantage

10. Gamma Code Variable length encoding with 2 parts 1 st Part: Unary code of length of 2 nd part 2 nd Part: Number in binary Encoding n takes 2 └ lg(n+1) ┘ =O(lg(n)) bits There are other codes with similar properties (Ex: Delta Code) NumberEncoding 100 201 31000 41001 51010 61011 7110000 8110001 9110010

11. Theorem: Using a difference coded adjacency table this ordering requires O(|V|) bits u uv v

12. Sketch of Proof The maximum distance between the numbers of u and v is the number of vertices in their Least Common Ancestor in the separator tree (n) With Gamma Code this takes O(lg(n)) bits Recurrence: S(n) ≤ 2S(.5n) + O(n c lg(n)) As a geometrically decreasing recurrence, this solves to S(|V|) = O(|V|)

13. Decoding Vertex i in O(1) time We can decode lg(|V|) bits at a time through table lookup An Edge Takes O(lg(|V|)) bits, and the degree of a vertex for a graph with good edge separators is constant We decode the entire vertex using a constant number of table lookups Vertex i-1DegreeEdge 2Edge 3Edge 1Vertex i+1 Pointer to Vertex i

14. Problem! This assumes we have a pointer to vertex i Requires |V| pointers of lg |V| bits each = O(|V| lg|V|), but the graph uses only O(|V|) Can afford a pointer to a BLOCK of lg |V| vertices O(lg |V| * |V| / lg |V|) = O(|V|) Can afford a pointer to a SUBBLOCK of at least k lg |V| bits Can afford extra 2 lg |V| bits per block

15. Getting a Pointer to Vertex i in O(1) Next BlockPrevious Block100110Pointer to BlockPointer to Offsets Prev. Subblocks Next SubblocksOffset for 3 rd SubblockOffset for 2 nd Subblock Pointer to Block (calculate as i / lg (|V|) Flags to determine subblocks. A 1 is the start of a subblock The Process: 1) Calculate block number 2) Identify subblock number 3) Get subblock offset 4) Add offset to pointer 5) Decode any Previous vertices (at most k lg |V| bits)

16. Graphs Used For Testing |V||E|Max DDesc. 3dGrid262144204410220 Large 3d Grid Airfoil2356046193620 3d Mesh of an Airfoil Crack10240606288 2d Triangle Mesh of a Cracked Plate Pitt18234495666 Map of Pittsburgh In Link24742811661463205 Links to WebPages (from TREC) Out Link2474281166146800 Links from WebPages (from TREC)

17. Experimental Space Results All #s in bits Used per edge Overhead per edge Used per vertex Overhead per Vertex 3dGrid9.380.9473.137.35 Airfoil5.340.46104.668.98 Crack5.900.9234.935.42 Pitt6.451.4217.543.87 In Link5.641.0126.584.75 Out Link8.781.2341.385.79

18. Experimental Time Results Results on 3D Grid Depth First Search time (seconds) Breadth First Search time (seconds) Our Implementation 0.890.86 Standard Adjacency List 0.330.31 Array Adjacency List 0.170.12

19. Conclusions Our Method uses O(|V|) bits to compress a graph with good separators On test graphs with good separators, it used less than 12 bits per edge, including overhead It meets the lower bounds for queries With unoptimized table lookup it takes 3 times as long to search as a standard adjacency list representation and 6 times as long as an array based adjacency list

20. Future Work Test decoding speed on more graphs Optimize decoding Allow addition / deletion of edges More Efficient Separator Generator Implement Vertex Separators

21. Planar Embeddings and Parentheses Create a Page Embedding Represent vertices and edges as parentheses - () for a vertex - ( for start of an edge - ) close for end - Figure on left: ()((())(()))((())(())) No good page embedding algorithm for arbitrary graphs