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はじめに ネットワーク解析やグラフアルゴリズムの研究者がアルゴリズムを実装した際，動作確認のために最初に実行する toy example をどうするかというのは意外と悩ましい．パスグラフやグリッドグラフのような高い対称性を持つグラフや小さすぎるグラフではいまいち動作に確証が持てない．一方，公開されている実データは最も小規模な Karate Club や Dolphin Social Network 等でも目視には大きすぎる．調度良いサイズの，ある程度非自明な形をしており，アルゴリズムによる出力の意味の解釈がある程度可能であり，できれば愛着が持てるグラフデータが必要とされている． そこで，本研究ではそのような用途に適切なグラフデータとして，「ラブライブ！ School idol project」のキャラクター間のグラフを構築する．データの構築には，pixiv に投稿されている二次創作作品のタ
Currently, we have visualizations for the following data structures and algorithms: Basics Stack: Array Implementation Stack: Linked List Implementation Queues: Array Implementation Queues: Linked List Implementation Lists: Array Implementation (available in java version) Lists: Linked List Implementation (available in java version) Recursion Factorial Reversing a String N-Queens Problem Indexing
Algorithms that are the main driver behind a system are, in my opinion, easier to find in non-algorithms courses for the same reason theorems with immediate applications are easier to find in applied mathematics rather than pure mathematics courses. It is rare for a practical problem to have the exact structure of the abstract problem in a lecture. To be argumentative, I see no reason why fashiona
If you use a 50% confidence level, and people are expecting 95%, it is going to make your data look a lot more impressive! 4. Use lots of graphs Humans love pattern matching. When you present data in a visual way, it allows people to quickly start jumping to conclusions, whether or not they are true. The above graph is just random values assigned to 10 cities in the US, then the columns are sorted
I was just doing some late night reading and came across this article: http://gigaom.com/data/why-becoming-a-data-scientist-might-be-easier-than-you... TL;DR - You can take the ML course on Coursera and you're magically a data scientist, because three really intelligent people did it. I disagree. I'm not claiming the people referenced in this article are not data scientists who score high in Kaggl
Telling climate stories through a solutions and data lens How solutions storytelling can engage local communities on mitigating the climate crisis The time is now for journalists to halt the doom and gloom narrative around the climate crisis and instead reframe the narrative with a solutions approach. Journalist Sherry Ricchiardi examines how to tell such solutions stories with data by highlightin
Online diagramming tool for collaborating on wireframes, flowcharts, and more
Common Crawl maintains a free, open repository of web crawl data that can be used by anyone.Common Crawl is a 501(c)(3) non–profit founded in 2007. We make wholesale extraction, transformation and analysis of open web data accessible to researchers.Overview Over 250 billion pages spanning 15 years.Free and open corpus since 2007.Cited in over 10,000 research papers.3–5 billion new pages added ea
Welcome to the Pure Data site! This site is a contribution of the IEM to the Pure Data community. Every Pure Data user who wants to contribute is welcome to join this portal and write/contribute some documentation, reports, news, comments and announcing events. The site is run on a Linux server with Zope / plone and administrated and driven by the Pure Data community. About Pure Data (aka "Pd" / "
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