Graph Search, Shortest Paths, and Data Structures

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/algorithms-graphs-data-structures

About Course

The primary topics in this part of the specialization are: data structures (heaps, balanced search trees, hash tables, bloom filters), graph primitives (applications of breadth-first and depth-first search, connectivity, shortest paths), and their applications (ranging from deduplication to social network analysis).

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Course Introduction

Graph Search, Shortest Paths, and Data Structures This is the third course in the Data Structures and Programming in C programming specialization. In this class, we will explore the basic elements of graph data structures such as trees, rectangles, and strings. We'll also consider basic data management techniques such as indexing, traversing, and shortest paths. We'll even cover the basics of algorithms that can shorten the path of programs by a certain amount. We'll implement a program that computes an average over all paths and their median values. This course assumes that you already know the basics of C programming and that you're comfortable with basic programming additions and optimizations.Graph Architecture and Data Structures Shortest Paths and Average) Compute an Average GIS as a Service (C) GIS is a service that enables you to obtain, manipulate, and visualize spatial data. This course will focus on the core elements and how to apply the GIS data warehouse system. We will explore the warehouse system in detail, covering its components, storage and load balancing, and the use of spatial indexes and bucket data models. We will also discuss the use of MIP maps, ROT maps, and static maps for mapping data. We will also discuss the concepts and use of REST for web access and client-server communication. You will also learn how to view and manipulate data using the WebSphere WL 4.0 framework and to visualize data using the

Course Tag

Graphs Data Structure Algorithms Hash Table

Related Wiki Topic

Article Example
Facebook Graph Search In late September 2013, Facebook announced that it would begin rolling out search for posts and comments as part of Graph Search. The rollout began in October 2013, but many people who had Graph Search were not given immediate access to this feature. A post on the Facebook Engineering blog explained that the huge amount of post and comment data, coming to a total of 700 TB, meant that developing Graph Search for posts was substantially more challenging than the original Graph Search.
Facebook Graph Search Facebook Graph Search was a semantic search engine that was introduced by Facebook in March 2013. It was designed to give answers to user natural language queries rather than a list of links. The Graph Search feature combined the big data acquired from its over one billion users and external data into a search engine providing user-specific search results. In a presentation headed by Facebook CEO Mark Zuckerberg, it was announced that the Graph Search algorithm finds information from within a user's network of friends. Additional results were provided by Microsoft's Bing search engine. In July it was made available to all users using the U.S. English version of Facebook. In December 2014, Facebook changed its search features, dropping partnership with Bing, and eliminating most of the search patterns.
Shortest path problem The all-pairs shortest path problem finds the shortest paths between every pair of vertices , in the graph. The all-pairs shortest paths problem for unweighted directed graphs was introduced by , who observed that it could be solved by a linear number of matrix multiplications that takes a total time of .
David Eppstein In computer science, Eppstein's research is focused mostly in computational geometry: minimum spanning trees, shortest paths, dynamic graph data structures, graph coloring, graph drawing and geometric optimization. He has published also in application areas such as finite element meshing, which is used in engineering design, and in computational statistics, particularly in robust, multivariate, nonparametric statistics.
Modular graph In graph theory, a branch of mathematics, the modular graphs are undirected graphs in which every three vertices , , and have at least one "median vertex" that belongs to shortest paths between each pair of , , and .
Planar separator theorem The separator based divide and conquer paradigm has also been used to design data structures for dynamic graph algorithms and point location, algorithms for polygon triangulation, shortest paths, and the construction of nearest neighbor graphs, and approximation algorithms for the maximum independent set of a planar graph.
Median graph In graph theory, a division of mathematics, a median graph is an undirected graph in which every three vertices "a", "b", and "c" have a unique "median": a vertex "m"("a","b","c") that belongs to shortest paths between each pair of "a", "b", and "c".
A* search algorithm A* is an informed search algorithm, or a best-first search, meaning that it solves problems by searching among all possible paths to the solution (goal) for the one that incurs the smallest cost (least distance travelled, shortest time, etc.), and among these paths it first considers the ones that "appear" to lead most quickly to the solution. It is formulated in terms of weighted graphs: starting from a specific node of a graph, it constructs a tree of paths starting from that node, expanding paths one step at a time, until one of its paths ends at the predetermined goal node.
Breadth-first search Breadth-first search (BFS) is an algorithm for traversing or searching tree or graph data structures. It starts at the tree root (or some arbitrary node of a graph, sometimes referred to as a 'search key') and explores the neighbor nodes first, before moving to the next level neighbors.
Distributed tree search One of the more important limits of DTS is the fact that it requires a tree as input. Trees are a sub-instance of a data structure known as Graphs, which means every Graph can be converted into a tree. Although there currently exists no better way to search through trees than Korf-Ferguson's algorithm, each task has different particularities and in most cases, there will exist more efficient data structures to represent the problem and solve it than through tree-search. And so there exist instances of tree structures with cycles that cannot possibly be faster than a graph-search on the same structure with the same processing power.
Graph (abstract data type) In computer science, a graph is an abstract data type that is meant to implement the undirected graph and directed graph concepts from mathematics.
Sparksee (graph database) DEX is based on a graph database model, that is basically characterized by three properties: data structures are graphs or any other structure similar to a graph; data manipulation and queries are based on graph-oriented operations; and there are data constraints to guarantee the integrity of the data and its relationships.
Search algorithm An important and extensively studied subclass are the graph algorithms, in particular graph traversal algorithms, for finding specific sub-structures in a given graph — such as subgraphs, paths, circuits, and so on. Examples include Dijkstra's algorithm, Kruskal's algorithm, the nearest neighbour algorithm, and Prim's algorithm.
List of data structures This is a list of data structures. For a wider list of terms, see list of terms relating to algorithms and data structures. For a comparison of running time of subset of this list see comparison of data structures.
List of Dutch inventions and discoveries Dijkstra's algorithm, conceived by Dutch computer scientist Edsger Dijkstra in 1956 and published in 1959, is a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge path costs, producing a shortest path tree. Dijkstra's algorithm is so powerful that it not only finds the shortest path from a chosen source to a given destination, it finds all of the shortest paths from the source to all destinations. This algorithm is often used in routing and as a subroutine in other graph algorithms.
Median graph No cycle graph of length other than four can be a median graph. Every such cycle has three vertices "a", "b", and "c" such that the three shortest paths wrap all the way around the cycle without having a common intersection. For such a triple of vertices, there can be no median.
K shortest path routing Eppstein's model finds the K shortest paths (allowing cycles) connecting a given pair of vertices in a digraph, in time O(m+ nlogn + K). This algorithm uses a graph transformation technique.
K shortest path routing This model can also find the "K" shortest paths from a given source s to each vertex in the graph, in total time O("m" + "n" log "n" + "kn").
Modular graph It is not possible for a modular graph to contain a cycle of odd length. For, if is a shortest odd cycle in a graph, is a vertex of , and is the edge of the cycle farthest from , there could be no median , for the only vertices on the shortest path are and themselves, but neither can belong to a shortest path from to the other without shortcutting and creating a shorter odd cycle. Therefore, every modular graph is a bipartite graph.
Facebook Graph Search The emergence of the Graph Search feature builds on this partnership. Facebook content remains on Bing.com. The focus of Graph Search is internal content, but Bing continues to issue search results of external content. The external search results are based on traditional keyword-match.