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A C# plug-and-play class-library project of standard Data Structures and Algorithms. It contains 35+ Data Structures and 30+ Algorithms designed as Object-Oriented isolated components. Even though this project started for educational purposes, the implemented Data Structures and Algorithms are standard, efficient, stable and tested.This project originally started out as an interview preparation project. However, after receiving a great amount of positive responses on reddit, and noticing excitement from a few GitHubers to contribute furthermore to it, the project took on a different meaning. So, I decided to keep maintaining it as a reference for data structures and algorithm implementations in C# as well as my own research side-project under these topics.

algorithms data-structures graph graph-algorithms tree binary-trees algorithmSource code of Learning JavaScript Data Structures and Algorithms book.

javascript-algorithms algorithm data-structures typescript typescript-algorithms stack queue deque priority-queue linked-list set tree sorting-algorithms dictionary binary-tree avl-tree quicksort graph graph-algorithms dijkstra-algorithmNetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. For additional details, please see INSTALL.rst.

complex-networks graph-theory graph-algorithms graph-analysis graph-generation graph-visualizationPython implementation of TextRank, based on the Mihalcea 2004 paper. The results produced by this implementation are intended more for use as feature vectors in machine learning, not as academic paper summaries.

textrank summarization natural-language-processing text-analytics nlp nlp-parsing machine-learning graph-algorithmsVivaGraphJS is designed to be extensible and to support different rendering engines and layout algorithms. Underlying algorithms have been broken out into ngraph. The larger family of modules can be found by querying npm for "ngraph".

graph-drawing layout-algorithm visualization ngraph graph graph-algorithms webgl vivagraphNeo4j 3.2 has increased security for procedures and functions (aka sandboxing). Procedures that use internal APIs have to be allowed in $NEO4J_HOME/conf/neoj4.conf with, e.g. dbms.security.procedures.unrestricted=apoc.trigger.*,apoc.meta.* for security reasons (or apoc.* for all). If you want to use this via docker, you need to amend -e NEO4J_dbms_security_procedures_unrestricted=apoc.\\\* to your docker run … command. The three backslashes are necessary to prevent wildcard expansions.

graph-database graph-algorithms stored-procedures neo4j neo4j-pluginIn this course you will learn how to Analysis algorithms like Sorting, Searching, and Graph algorithms. And how to reduce the code complexity from one Big-O level to another level. Furthermore, you will learn different type of Data Structure for your code. Also you will learn how to find Big-O for every data structure, and how to apply correct Data Structure to your problem in Java. By the end you will be able to write code that run faster and use low memory. You Also will learn how to analysis problems using Dynamic programming.

algorithm algorithm-analysis analysis-algorithms complexity job-interviews graph-algorithms sortDoxygen documentation can be found here. We have walkthroughs for a few different parts of MeTA on the MeTA homepage.

nlp nlp-parsing search-engine inverted-index pos-tag text-analysis text-analytics text-classification language-modeling graph-algorithms c-plus-plus word-embeddingsGraph data structure library. Requires Rust 1.12. Dual-licensed to be compatible with the Rust project.

graph-algorithmsSwiftGraph is a pure Swift (no Cocoa) implementation of a graph data structure, appropriate for use on all platforms Swift supports (iOS, macOS, Linux, etc.). It includes support for weighted, unweighted, directed, and undirected graphs. It uses generics to abstract away both the type of the vertices, and the type of the weights. It includes copious in-source documentation, unit tests, as well as search functions for doing things like breadth-first search, depth-first search, and Dijkstra's algorithm. Further, it includes utility functions for topological sort, Jarnik's algorithm to find a minimum-spanning tree, detecting a DAG (directed-acyclic-graph), and enumerating all cycles.

graph data-structure graph-algorithms dijkstra-algorithm topological-sort breadth-first-search depth-first-search prims-algorithmEfficient Graph Algorithms for Neo4j

graph-algorithms neo4j cypher graph-database graph-analyticsUntil an issue with one of our dependencies is resolved, LightGraphs will not work with any Julia 0.7 or 1.0 version that has been built from source on OSX or other systems with a compiler more modern than GCC7. If you use LightGraphs with Julia 0.7 or 1.0, please download a Julia binary. LightGraphs offers both (a) a set of simple, concrete graph implementations -- Graph (for undirected graphs) and DiGraph (for directed graphs), and (b) an API for the development of more sophisticated graph implementations under the AbstractGraph type.

julia graph lightgraphs graph-theory graph-generation graph-analytics graph-algorithmsTopological ordering, image by David Eppstein, CC0 1.0.The algorithms can be applied to any graph data structure implementing the two Iterator methods: Order, which returns the number of vertices, and Visit, which iterates over the neighbors of a vertex.

graph-algorithms graph-theory data-structuresTarjan's algorithm takes as input a directed (possibly cyclic!) graph and returns as output its strongly connected components in a topological order. In various cases, dependencies might be cyclic and a group of interdependant actions must be executed simultaneously. It is not uncommon that the simulataneous execution is costly. With Tarjan's algorithm, one can determine an efficient order in which to execute the groups of interdependant actions.

graph-algorithms tarjan transitive-closure scc dependancy-managerThis work consists of a study of a set of techniques and strategies related with algorithm's design, whose purpose is the resolution of problems on massive data sets, in an efficient way. This field is known as Algorithms for Big Data. In particular, this work has studied the Streaming Algorithms, which represents the basis of the data structures of sublinear order o(n) in space, known as Sketches. In addition, it has deepened in the study of problems applied to Graphs on the Semi-Streaming model. Next, the PageRank algorithm was analyzed as a concrete case study. Finally, the development of a library for the resolution of graph problems, implemented on the top of the intensive mathematical computation platform known as TensorFlow has been started.

final-degree-project big-data algorithms university-of-valladolid research graph-algorithms pagerank graphA web app for finding a rating scale between two arbitrary things. Ever wanted to rate something on a scale of David Hasselhoff to the Eiffel Tower? Well now you can.Dave Pagurek and I made this for the TerribleHack Winter 2015 hackathon. I wrote the backend, Dave wrote the frontend. I later rewrote the backend in Nim and then D to make it cleaner and easier to deploy as well as learn new languages.

wikipedia terrible-hack web-app graph-algorithms hackathonExtracts link graphs in a variety of formats from Wikipedia data dumps. This includes a highly compact binary graph format designed for very efficient graph searches.It can compress a recent 10GB compressed Wikipedia dump into a 630MB binary link graph and a 550MB sqlite database for translating article names into binary graph offsets.

wikipedia graph-algorithms data-collectorIn this repository you find the code for a graph pattern learner. Given a list of source-target-pairs and a SPARQL endpoint, it will try to learn SPARQL patterns. Given a source, the learned patterns will try to lead you to the right target. As you can immediately see, associations don't only follow a single pattern. Our algorithm is designed to be able to deal with this. It will try to learn several patterns, which in combination model your input list of source-target-pairs. If your list of source-target-pairs is less complicated, the algorithm will happily terminate earlier.

graph-algorithms rdf sparql data-mining knowledge-graph associations machine-learning graph-queries knowledge-mining pattern-learning embeddings end-to-end-learning graph-pattern-learner learners algorithm human-associations prediction linked-data semantic-webThis is an implementation of PageRank in Spark, using Spark's standard RDD API. You can use this as a library and call it from within your own drivers. You will want to do this when you have some data preparation to do that does not conform with the built-in driver data interfaces.

spark pagerank graph graph-algorithmsNodes is a general purpose graph library. It focuses on simple creation and traversal of graphs with general objects on their nodes and links. Nodes is a work in progress, it should not be considered production-ready in general, though certain aspects may be well-tested. These are all subinterfaces of the basic interface org.nodes.Graph.

graphs graph traversal graph-algorithms
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