PPT-Implementing a Graph Implement a graph in three ways:

Adjacency List AdjacencyMatrix Pointersmemory for each node actually a form of adjacency list Adjacency List List of pointers for each vertex Undirected Adjacency List Adjacency List The sum of the lengths of the adjacency lists is 2E in an undirected graph and E in a directed graph. Implement a graph in three ways:. Adjacency List. Adjacency-Matrix. Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. 1. Graph Algorithms. Many problems are naturally represented as graphs. Networks, Maps, Possible paths, Resource Flow, etc.. Ch. 3 focuses on algorithms to find connectivity in graphs. Ch. 4 focuses on algorithms to find paths within graphs. 1. Introduction to . NoSQL. databases and CS554 projects based on ZHT. Outlines. General terms. Overview to . NoSQL. . dabases. and key-value stores. Introduction to ZHT. CS554 projects . 2. Databases/. Algorithms:. Graph . coloring. Created by: . . . Avdeev. . Alex,. . . Blakey Paul.. Criteria for good graph coloring algorithm:. Coloring Quality: . Number of colors needed . Number of colors ( . Implement a graph in three ways:. Adjacency List. Adjacency-Matrix. Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. Hao Wei. 1. , . Jeffrey Xu Yu. 1. , Can L. u. 1. , . Xuemin. Lin. 2. . 1 . The . Chinese University of Hong Kong, Hong Kong. 2 . The . University of New South Wales. , . Sydney, Australia. Graph in Big Data . Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Xiaoming. Sun and David P. Woodruff. Chinese Academy of Sciences and IBM Research-. Almaden. Streaming Models. Long sequence of items appear one-by-one. numbers, points, edges, …. (usually) . adversarially. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Outline. Min s-t cut problem. Data Structures and Algorithms. CSE 373 19 SP - Kasey Champion. 1. Administrivia. We clarified Exercise 5 Problem [] to explicitly mention “worst-case”. CSE 373 SP 18 - Kasey Champion. 2. Administrivia. Data Structures and Algorithms. CSE 373 WI 19 - Kasey Champion. 1. Last Time. We described algorithms to find:. CSE 373 SP 18 - Kasey Champion. 2. An ordering of the vertices so all edges go from left to right. . Data Structures and Algorithms. CSE 373 19 . Sp. - Kasey Champion. 1. Administrivia. HW 7 Due Friday. Final exam review Wednesday 6/5 4-5:50. Final exam next Tuesday!. Double check all grades . Please fill out survey. Pretend like the only vertex visited so far is the starting vertex (0).. Look at all other vertices connected to the starting vertex. Add the one with the shortest path to an array of visited vertices . Outline . Definition and Data Structures of Graph. . Eulerian. & Hamiltonian Cycle . . DNA Sequencing . . Shortest Superstring Problem, SSP as. . Traveling. . Salesman Problem (TSP) . Sequencing by Hybridization (SBH), SBH as Hamiltonian .