グラフアルゴリズム実践活用術
æƒ…å ±ã‚ªãƒªãƒ³ãƒ”ãƒƒã‚¯å¤å£ã‚»ãƒŸãƒŠãƒ¼ 2023: https://jcioi-summer-seminar-2023.peatix.com/ ã§ã®è¬›æ¼”スライドã§ã™ã€‚ 講義概è¦: アルゴリズムを勉強ã—ã¦ã„ã‚‹ã¨ï¼Œã‚°ãƒ©ãƒ•ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã«ãŸãã•ã‚“出会ã„ã¾ã™ï¼Žã—ã‹ã—，グラフアルゴリズムãŒç¾å®Ÿä¸–ç•Œã§ã©ã®ã‚ˆã†ã«æ´»èºã—ã¦ã„…
ラベルä¼æ’法・拡散法ã«ã‚ˆã‚‹åŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã§å°‘é‡ã®ãƒ©ãƒ™ãƒ«ä»˜ãデータã‹ã‚‰åˆ†é¡žãƒ¢ãƒ‡ãƒ«ã‚’作る - Qiita
ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ï¼Œ(æ ª)日立製作所 ç ”ç©¶é–‹ç™ºã‚°ãƒ«ãƒ¼ãƒ— ã‚µãƒ¼ãƒ“ã‚¹ã‚³ãƒ³ãƒ”ãƒ¥ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ç ”ç©¶éƒ¨ã®éœ²æœ¨ã§ã™ã€‚ 機械å¦ç¿’ã§ä¸€èˆ¬çš„ãªæ•™å¸«ã‚ã‚Šå¦ç¿’ã®å•é¡Œã¨ã—ã¦ï¼Œæ£è§£ãƒ©ãƒ™ãƒ«ã®ä»˜ã„ã¦ã„るデータを大é‡ã«æº–å‚™ã—ãªã‘ã‚Œã°ãªã‚‰ãªã„ã“ã¨ãŒæŒ™ã’られã¾ã™ã€‚ã“ã®å•é¡Œã‚’解決ã™ã‚‹ãŸã‚ã«ï¼Œãƒ©ãƒ™ãƒ«ãªã—データを活用ã™ã‚‹ã“ã¨ã§å¿…è¦ãªãƒ©ãƒ™ãƒ«ä»˜ãデータã®æ•°ã‚’削減ã™ã‚‹åŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã®åˆ†é‡ŽãŒã‚ã‚Šã¾ã™ã€‚ãã—ã¦ï¼ŒåŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã®ä¸€ç¨®ã«ã‚°ãƒ©ãƒ•ãƒ™ãƒ¼ã‚¹ã®ãƒ©ãƒ™ãƒ«ä¼æ’法ãŒã‚ã‚Šã¾ã™ã€‚ ラベルä¼æ’法ã¯ï¼Œã‚るデータã®ãƒ©ãƒ™ãƒ«ã‚’ãã®è¿‘å‚ã«ã‚るラベルã®ãªã„データã«ã‚³ãƒ”ーã™ã‚‹ (ä¼æ’ã•ã›ã‚‹) ã“ã¨ã§ï¼Œå°‘é‡ã®ãƒ©ãƒ™ãƒ«ä»˜ãデータã‹ã‚‰ãƒ¢ãƒ‡ãƒ«ã‚’å¦ç¿’ã—ã¾ã™ã€‚ã¾ãŸï¼Œã€Œã‚°ãƒ©ãƒ•ãƒ™ãƒ¼ã‚¹ã€ã¨å‘¼ã°ã‚Œã‚‹ç†ç”±ã¯ï¼Œã²ã¨ã¤ã²ã¨ã¤ã®ãƒ‡ãƒ¼ã‚¿ã‚’ノード，データã®é¡žä¼¼åº¦ã‚’エッジ (ã®é‡ã¿) ã¨ã—ãŸã‚°ãƒ©ãƒ•ã‚’構æˆã—ã¦ï¼Œã“ã®ã‚°ãƒ©ãƒ•ä¸Šã§ãƒ©ãƒ™ãƒ«ã‚’ä¼æ’ã™ã‚‹ã‹ã‚‰ã§ã™ã€‚ã“ã®ã¨ã，類似度ã®è¨ˆç®—ã«ã‚«ãƒ¼ãƒãƒ«æ³•ã‚’組ã¿åˆã‚ã›ã‚‹ã“ã¨ã§éž
Label propagationã¨Label spreading - ã§ã‹ã„ãƒãƒ¼ã‚ºã‚’ベーグルã™ã‚‹
グラフベースåŠæ•™å¸«ã‚ã‚Šå¦ç¿’ (SSL) ã®Label propagation (LP) ã¨Label spreading (LS) ã®é•ã„を説明ã—ã¦ã„る文献ãŒã‚ã¾ã‚Šãªã‹ã£ãŸã®ã§ãã‚Œã«ã¤ã„ã¦ã¡ã‚‡ã£ã¨æ›¸ã„ã¦ã¿ã‚‹ã€‚SSL自体ã¨ã‹LPã€LSã«ã¤ã„ã¦ã¯ä»¥ä¸‹ã®è¨˜äº‹ã«ã¾ã¨ã‚ãŸæ–‡çŒ®ãŒã„ã„æ„Ÿã˜ãªã®ã§ãã¡ã‚‰ã‚’å‚照。 åŠæ•™å¸«ã‚ã‚Šå¦ç¿’ã®ãƒ¢ãƒ‡ãƒ«ä»®å®š - ã§ã‹ã„ãƒãƒ¼ã‚ºã‚’ベーグルã™ã‚‹ LPã®å…ƒè«–æ–‡ã¯ã“ã‚Œ (PDF) "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions", ICML2003 LSã®å…ƒè«–æ–‡ã¯ã“ã‚Œ (PDF) "Learning with Local and Global Consistency", NIPS2003 ã¾ã¨ã‚ LPã¨LSã®è¶…概è¦ã€ãƒ©ãƒ³ãƒ€ãƒ ウォークã¨ã—ã¦ã®è§£é‡ˆã€æœ€é©åŒ–å•é¡Œã¨ã—ã¦ã®è§£é‡ˆã‚’書ã„㟠軽ã„実験をã—ãŸ
Balanced Label Propagation for Partitioning Massive Graphs - Meta Research | Meta Research
Balanced Label Propagation for Partitioning Massive GraphsACM International Conference on Web Search and Data Mining (WSDM) Partitioning graphs at scale is a key challenge for any application that involves distributing a graph across disks, machines, or data centers. Graph partitioning is a very well studied problem with a rich literature, but existing algorithms typically can not scale to billion
トãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆ - Wikipedia
トãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆï¼ˆè‹±: topological sort）ã¯ã€ã‚°ãƒ©ãƒ•ç†è«–ã«ãŠã„ã¦ã€æœ‰å‘éžå·¡å›žã‚°ãƒ©ãƒ•ï¼ˆè‹±: directed acyclic graph, DAG）ã®å„ãƒŽãƒ¼ãƒ‰ã‚’é †åºä»˜ã‘ã—ã¦ã€ã©ã®ãƒŽãƒ¼ãƒ‰ã‚‚ãã®å‡ºåŠ›è¾ºã®å…ˆã®ãƒŽãƒ¼ãƒ‰ã‚ˆã‚Šå‰ã«ãるよã†ã«ä¸¦ã¹ã‚‹ã“ã¨ã§ã‚る。有å‘éžå·¡å›žã‚°ãƒ©ãƒ•ã¯å¿…ãšãƒˆãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆã™ã‚‹ã“ã¨ãŒã§ãる。 有å‘éžå·¡å›žã‚°ãƒ©ãƒ•ã®ãƒŽãƒ¼ãƒ‰ã®é›†åˆã«åˆ°é”å¯èƒ½æ€§é–¢ä¿‚ R (ノード x ã‹ã‚‰ y ã¸ã®(å„辺ã®å‘ãã«é€†è¡Œã—ãªã„)経路ãŒå˜åœ¨ã™ã‚‹ã¨ãã€ã¾ãŸãã®ã¨ãã«é™ã‚Š xRy ã¨ã™ã‚‹)を定ã‚ã‚‹ã¨ã€R ã¯åŠé †åºé–¢ä¿‚ã¨ãªã‚‹ã€‚トãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆã¨ã¯ã€ã“ã® R ã‚’å…¨é †åºã«ãªã‚‹ã‚ˆã†ã«æ‹¡å¼µã—ãŸã‚‚ã®ã¨ã¿ãªã›ã‚‹ã€‚ トãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆã®å…¸åž‹çš„ãªåˆ©ç”¨ä¾‹ã¯ã‚¸ãƒ§ãƒ–ã®ã‚¹ã‚±ã‚¸ãƒ¥ãƒ¼ãƒªãƒ³ã‚°ã§ã‚る。トãƒãƒã‚¸ã‚«ãƒ«ã‚½ãƒ¼ãƒˆã®ã‚¢ãƒ«ã‚´ãƒªã‚ºãƒ ã¯PERTã¨ã„ã†ãƒ—ãƒã‚¸ã‚§ã‚¯ãƒˆç®¡ç†æ‰‹æ³•[1]ã®ã‚¹ã‚±ã‚¸ãƒ¥ãƒ¼ãƒªãƒ³ã‚°ã®ãŸã‚ã«1960年代åˆé ã«ç ”究ãŒé–‹å§‹ã•ã‚ŒãŸ
Neo4j 㨠Google Cloud Vertex AI ã§ã‚ˆã‚Šã‚¹ãƒžãƒ¼ãƒˆãª AI å‘ã‘ã®ã‚°ãƒ©ãƒ•ã‚’使用ã™ã‚‹ | Google Cloud å…¬å¼ãƒ–ãƒã‚°
※ã“ã®æŠ•ç¨¿ã¯ç±³å›½æ™‚é–“ 2022 å¹´ 1 月 13 æ—¥ã«ã€Google Cloud blog ã«æŠ•ç¨¿ã•ã‚ŒãŸã‚‚ã®ã®æŠ„訳ã§ã™ã€‚ ã“ã®ãƒ–ãƒã‚°æŠ•ç¨¿ã§ã¯ã€ML 開発プラットフォームGoogle Cloud Vertex AI ã¨ã‚°ãƒ©ãƒ• データベース Neo4j ã® 2 ã¤ã®ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼ã‚’一緒ã«ä½¿ç”¨ã™ã‚‹æ–¹æ³•ã«ã¤ã„ã¦èª¬æ˜Žã—ã¾ã™ã€‚ã“れらã®ãƒ†ã‚¯ãƒŽãƒã‚¸ãƒ¼ã‚’組ã¿åˆã‚ã›ã¦ä½¿ç”¨ã™ã‚‹ã“ã¨ã§ã€ã‚°ãƒ©ãƒ•ãƒ™ãƒ¼ã‚¹ã®æ©Ÿæ¢°å¦ç¿’モデルを構築ã—ã€ãƒ‡ãƒ—ãƒã‚¤ã§ãã¾ã™ã€‚ ã“ã®ãƒ–ãƒã‚°æŠ•ç¨¿ã®åŸºç›¤ã¨ãªã‚‹ã‚³ãƒ¼ãƒ‰ã¯ã€ã“ã¡ã‚‰ã®ãƒŽãƒ¼ãƒˆãƒ–ックã§å‚ç…§ã§ãã¾ã™ã€‚ データ サイエンスã«ã‚°ãƒ©ãƒ•ã‚’使用ã™ã‚‹ã¹ãç†ç”±ãƒ“ジãƒã‚¹ä¸Šã®é‡å¤§ãªå•é¡Œã®å¤šãã§ã¯ã€ã‚°ãƒ©ãƒ•ã¨ã—ã¦è¡¨ç¾ã§ãるデータãŒä½¿ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚グラフã¯ãƒ‡ãƒ¼ã‚¿æ§‹é€ ã§ã€ãƒ‡ãƒ¼ã‚¿ãã®ã‚‚ã®ã¨åŒæ§˜ã«ãƒ‡ãƒ¼ã‚¿ãƒã‚¤ãƒ³ãƒˆé–“ã®é–¢ä¿‚を記述ã—ã¾ã™ã€‚ ç°¡å˜ã«è¨€ã†ã¨ã€ã‚°ãƒ©ãƒ•ã¯åè©žã¨å‹•è©žã®é–¢ä¿‚ã«ä¼¼ã¦ã„ã¾ã™ã€‚ノード（å詞）ã¯äººã€…ã€å ´æ‰€ã€å“物ãªã©ã®ã€Œã‚‚
gql.today
In this SpyMonster review, we will take a comprehensive look at the app, a powerful monitoring tool that helps parents and guardians keep track of their children’s online activities. We will explore its features, how it works, the platforms it supports, its advantages and disadvantages, the installation process, pricing, and subscription model, and some frequently
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Chris Preimesberger （Special to ZDNET.comï¼‰Â ç¿»è¨³æ ¡æ£ï¼šÂ æ‘ä¸Šé›…ç« Â é‡Žå´Žè£•å 2022-01-14 06:30 データ管ç†åˆ†é‡Žã®æ€æƒ³çš„リーダーらã«ã‚ˆã‚‹ã¨ã€2022å¹´ã«ã‚¨ãƒ³ã‚¿ãƒ¼ãƒ—ライズデータã«ã¤ã„ã¦èªžã‚‹éš›ã€ã‚ã‚Œã‚ã‚Œã¯æ–°ãŸãªç”¨èªžã‚’使ã†ã‚ˆã†ã«ãªã£ã¦ã„ã‚‹ã¨ã„ã†ã€‚ ã¾ãŸã€æœ€é«˜æŠ€è¡“責任者（CTO）やITマãƒãƒ¼ã‚¸ãƒ£ãƒ¼ã‚‚ãã®ãƒ¬ãƒ™ãƒ«ã«ã‹ã‹ã‚らãšã€è‡ªç¤¾ã®æœ¬ç•ªãƒ¦ãƒ¼ã‚¹ã‚±ãƒ¼ã‚¹ã‚’語る際ã«ã€ã€Œã‚³ãƒ¼ãƒ‰ã¨ã—ã¦ã®ãƒ‡ãƒ¼ã‚¿ã€ã‚„ã€ã€Œã‚¸ãƒ£ã‚¹ãƒˆã‚¤ãƒ³ã‚¿ã‚¤ãƒ ã€ãƒ‡ãƒ¼ã‚¿ã‚¢ãƒŠãƒªãƒ†ã‚£ã‚¯ã‚¹ã¨ã„ã£ãŸç”¨èªžã‚’定義ã—ãŸã‚Šã€è©¦ç”¨ã—ã¦ã¿ã‚‹ã‚ˆã†ã«ãªã‚‹ã¨ã„ã†ã€‚ ã•ã‚‰ã«ã€ãƒ‡ãƒ¼ã‚¿ç®¡ç†åˆ†é‡Žã§ã¯äººå·¥çŸ¥èƒ½ï¼ˆAI）ã®æ´»ç”¨ãŒé€²ã¿ã€ã‚³ãƒ¼ãƒ«ã‚»ãƒ³ã‚¿ãƒ¼ãŒé¡§å®¢ã‹ã‚‰ã‚ˆã‚Šé©åˆ‡ãªæƒ…å ±ã‚’å¼•ã出ã›ã‚‹ã‚ˆã†ã«ãªã£ãŸã‚Šã€ã‚µãƒ—ライãƒã‚§ãƒ¼ãƒ³ã®ã‚®ãƒ£ãƒƒãƒ—を埋ã‚られるよã†ã«ãªã£ãŸã‚Šã€ãƒ˜ãƒ«ã‚¹ã‚±ã‚¢ã‚µãƒ¼ãƒ“スをãƒãƒ¼ã‚«ãƒ«ç’°å¢ƒã¨ã‚¯ãƒ©ã‚¦ãƒ‰ä¸Šã®åŒæ–¹ã§å¼·åŒ–ã§ãるよã†ã«ãªã‚‹ã€‚ 以下ã§ã¯ã€202
Introducing TensorFlow Graph Neural Networks
Posted by Sibon Li, Jan Pfeifer and Bryan Perozzi and Douglas Yarrington Today, we are excited to release TensorFlow Graph Neural Networks (GNNs), a library designed to make it easy to work with graph structured data using TensorFlow. We have used an earlier version of this library in production at Google in a variety of contexts (for example, spam and anomaly detection, traffic estimation, YouTub
How to Create Representations of Entities in a Knowledge Graph using pyRDF2Vec
Table of contentsRepresenting data with Knowledge GraphsRunning example: countries in DBpediaCreating entity embeddings with RDF2VecIntroducing pyRDF2VecBiased walking or sampling strategiesWalk modifications and transformationsLoading KGs with pyRDF2VecCreating our first embeddingsTuning the hyper-parametersTrying different walking strategiesSampling Deeper WalksShortcomings of RDF2Vec and resear
igraph – Network analysis software
C/igraph 0.10.15, the thirteenth bugfix release of the 0.10 series, has arrived, with several new additions, bug fixes and performance improvements. As usual, the source can be obtained from the GitHub releases page. A summary of changes in 0.10.15 is below. More → python-igraph 0.11.8, the seventh bugfix release of the 0.11 series, has arrived. The primary reason for this release is to update the
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Graph Theory
Google Graph Mining and Learning @ NeurIPS 2020: Label Propagation -- Allan Heydon
Balanced Partitioning and Hierarchical Clustering at Scale
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PyTorch-BigGraph: Faster embeddings of extremely large graphs
GitHub - georgian-io-archive/Diffbot-Graph-Learning: Heterogenous Graph Representation learning on Diffbot's Knowledge Graph. Includes asynchronous BFS traversal on Diffbot's Knowledge Graph, Diffbot's enhance API to build custom graph dataset and models
From Louvain to Leiden: guaranteeing well-connected communities
Communications of the ACM — Communications of the ACM
Graph-powered Machine Learning at Google
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