Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
With all that scope for reasonable disagreement, is there anything we can all agree on? How much of the hierarchy in the medal table is indisputable, and how much depends on your point of view? So we want to say that one country has done strictly better than another if the medal score of the latter can be transformed into the former by a sequence of medal additions and medal upgrades. A bit of thought shows that this is exactly equivalent to defining a partial order on triples of medals, in which a triple (G,S,B) is considered at least as good as another triple (g,s,b) if and only if it satisfies the three conditions * G ≥ g * G + S ≥ g + s * G + S + B ≥ g + s + b
Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
Version 2 der freien Graph-Datenbank GraphDB haben die Entwickler modularisiert, auch haben sie die Verteilung von Graphen auf mehrere Knoten ermöglicht. Hinzugekommen sind außerdem neue Schnittstellen, unter anderem für PHP.
A simple particle system physics engine for processing. I've designed this to be application / domain agnostic. All this is supposed to do is let you make particles, apply forces and calculate the positions of particles over time in real-time. Anything else you need to handle yourself.
Flare is an ActionScript library for creating visualizations that run in the Adobe Flash Player. From basic charts and graphs to complex interactive graphics, the toolkit supports data management, visual encoding, animation, and interaction techniques. Even better, flare features a modular design that lets developers create customized visualization techniques without having to reinvent the wheel.
This is an abstractive summarization demo program. It was mainly used to summarize opinions, but since it does not rely on any domain information, it can be used to summarize any highly redundant text.
This paper presents a flexible framework for generating very short abstractive summaries. The key idea is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, we repeatedly find paths through this graph to produce concise summaries. We consider Opinosis a "shallow" abstractive summarizer as it uses the original text itself to generate summaries. This is unlike a true abstractive summarizer that would need a deeper level of natural language understanding.
While the evaluation is on an opinion dataset, the approach itself is general in that, it can be applied to any corpus containing high amounts of redundancies, for example, Twitter comments or user comments on blog/news articles. A very similar work to ours (published at the same time and at the same conference) is the following:
Multi-sentence compression: Finding shortest paths in word graphs
Proceedings of the 23rd International Conference on Computaional Linguistics (COLING 10). Beijing, China, August 23-27, 2010. Katja Filippova
Katja's work was evaluated on a news dataset (google news) for both English and Spanish while ours was evaluated on user reviews from various sources (English only). She studies the informativeness and grammaticality of sentences and in a similar way we evaluate these aspects by studying how close the Opinosis summaries are compared to the human composed summaries in terms of information overlap and readability (using a human assessor).
// create a graph and 3 nodes Graph g = new Graph(); Node n1 = new Node(); Node n2 = new Node(); Node n3 = new Node(); // create 2 edges and add them manually Edge e1 = new Edge(n1, n2); Edge e2 = new Edge(n2, n3); n1.getEdges().add(e1); n2.getEdges().add(e2); // connect n3 to n1 n3.connectTo(n1); // add all 3 nodes to the graph g.addNode(n1); g.addNode(n2); g.addNode(n3); // create a graphWriter and file output stream GraphWriter gw = new GraphWriter(); File f = new File("textout.gexf"); FileOutputStream fos = new FileOutputStream(f); // write the file and close the stream - no XML worries! gw.write(g, fos); fos.close();
SciDAVis is a free application for Scientific Data Analysis and Visualization. SciDAVis is a free interactive application aimed at data analysis and publication-quality plotting. It combines a shallow learning curve and an intuitive, easy-to-use graphical user interface with powerful features such as scriptability and extensibility. SciDAVis is similar in its field of application to proprietary Windows applications like Origin and SigmaPlot as well as free applications like QtiPlot, Labplot and Gnuplot. What sets SciDAVis apart from the above is its emphasis on providing a friendly and open environment (in the software as well as the project) for new and experienced users alike. Particularly, this means that we will try to provide good documentation on all levels, ranging from user’s manual over tutorials down to and including documentation of the internal APIs We encourage users to share their experiences on our forums and on our mailing lists.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning. We introduce the notion of MapReduce design patterns, which represent general reusable solutions to commonly occurring problems across a variety of problem domains. This book not only intends to help the reader "think in MapReduce", but also discusses limitations of the programming model as well.
Great reference with many open-source useful plotting and visualization tools Over the years many different plotting modules and packages have been developed for Python. For most of that time there was no clear favorite package, but recently matplotlib has become the most widely used. Nevertheless, many of the others are still available and may suit your tastes or needs better. Some of these are interfaces to existing plotting libraries while others are Python-centered new implementations.
P. Heim, J. Ziegler, and S. Lohmann. Proceedings of the International Workshop on Interacting with Multimedia Content in the Social Semantic Web (IMC-SSW 2008), volume 417 of CEUR Workshop Proceedings, page 49--58. Aachen, (2008)
Y. Yang, C. Huang, L. Xia, and C. Li. Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, page 1434--1443. (2022)
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)
D. Yang, P. Rosso, B. Li, and P. Cudre-Mauroux. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 1162–1172. New York, NY, USA, Association for Computing Machinery, (2019)