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BGPãƒã‚¤ã‚¸ãƒ£ãƒƒã‚¯ã¨ã¯ï¼Ÿ BGPãƒã‚¤ã‚¸ãƒ£ãƒƒã‚¯ã¨ã¯ã€æ”»æ’ƒè€…ãŒæ‚ªæ„ã‚’æŒã£ã¦ã‚¤ãƒ³ã‚¿ãƒ¼ãƒãƒƒãƒˆãƒˆãƒ©ãƒ•ã‚£ãƒƒã‚¯ã‚’å†ãƒ«ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã™ã‚‹ã“ã¨ã§ã™ã€‚æ”»æ’ƒè€…ã¯ã€IPãƒ—ãƒ¬ãƒ•ã‚£ãƒƒã‚¯ã‚¹ã¨å‘¼ã°ã‚Œã‚‹ã€å®Ÿéš›ã«ã¯æ‰€æœ‰ã—ã¦ã„ãªã„ã€åˆ¶å¾¡ã—ã¦ã„ãªã„ã€ã¾ãŸã¯ãƒ«ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã—ã¦ã„ãªã„IPã‚¢ãƒ‰ãƒ¬ã‚¹ã‚°ãƒ«ãƒ¼ãƒ—ã®æ‰€æœ‰æ¨©ã‚’å½ã£ã¦çŸ¥ã‚‰ã›ã‚‹ã“ã¨ã§ã“ã‚Œã‚’å®Ÿç¾ã—ã¾ã™ã€‚BGPãƒã‚¤ã‚¸ãƒ£ãƒƒã‚¯ã¯ã€èª°ã‹ãŒé«˜é€Ÿé“è·¯ã®ã™ã¹ã¦ã®æ¨™è˜ã‚’å¤‰æ›´ã—ã€è‡ªå‹•è»Šã®ãƒˆãƒ©ãƒ•ã‚£ãƒƒã‚¯ã‚’èª¤ã£ãŸå‡ºå£ã«å†ãƒ«ãƒ¼ãƒ†ã‚£ãƒ³ã‚°ã™ã‚‹ã‚ˆã†ãªã‚‚ã®ã§ã™ã€‚ BGPã¯ç›¸äº’æŽ¥ç¶šã•ã‚ŒãŸãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ãŒæ‰€æœ‰ã™ã‚‹IPã‚¢ãƒ‰ãƒ¬ã‚¹ã«ã¤ã„ã¦çœŸå®Ÿã‚’ä¼ãˆã¦ã„ã‚‹ã¨ã„ã†å‰æã«åŸºã¥ã„ã¦æ§‹ç¯‰ã•ã‚Œã¦ã„ã‚‹ãŸã‚ã€BGPãƒã‚¤ã‚¸ãƒ£ãƒƒã‚¯ã‚’æ¢ã‚ã‚‹ã“ã¨ã¯ã»ã¼ä¸å¯èƒ½ã§ã™ã€‚é«˜é€Ÿé“è·¯æ¨™è˜ã‚’èª°ã‚‚ç®¡ç†ã—ã¦ã„ãªã„å ´åˆã‚’æƒ³åƒã—ã¦ãã ã•ã„ã€‚ãã‚Œã‚‰ãŒæ‚ªæ„ã‚’æŒã£ã¦å¤‰æ›´ã•ã‚ŒãŸã‹ã©ã†ã‹ã‚’çŸ¥ã‚‹å”¯ä¸€ã®æ–¹æ³•ã¯ã€å¤šãã®è‡ªå‹•è»ŠãŒé–“é•ã£ãŸåœ°åŸŸã«è¡Œãç€ãã®ã‚’è¦³å¯Ÿã™ã‚‹ã“ã¨ã ã‘ã§ã™ã€‚ãŸã ã—ã€ãƒã‚¤ã‚¸ãƒ£ãƒƒã‚¯ãŒç™º
xiangze 2022/10/04
security

network
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Microsoft researchers unlock the black box of network embedding - Microsoft Research
As humans, we have great capacity to understand knowledge, notice similarities and make connections and inferences. However, our capacity is limited by the amount of information we can process. Computers, on the other hand, can process tremendous amounts of information, but their ability to understand knowledge and make inferences is limited. Thatâ€™s because humans think in symbols and computers th
xiangze 2018/04/23
network
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On the networked architecture of genotype spaces and its critical effects on molecular evolution
xiangze 2018/04/20
network

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bnlearn - Bayesian network structure learning
Features bnlearn provides an open implementation of large parts of the literature on Bayesian networks: Classes of Bayesian networks: discrete (multinomial) Bayesian networks for discrete data, Gaussian Bayesian networks for continuous data and Conditional Gaussian networks for mixed data. Structure learning algorithms: constraint-based (PC Stable, Grow-Shrink, IAMB, Fast-IAMB, Inter-IAMB, IAMB-FD
xiangze 2017/09/10
network

Bayesian Network

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Robust Spatial Filtering with Graph Convolutional Neural Networks
xiangze 2017/07/16
cnn

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xiangze 2017/01/06
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xiangze 2016/11/30
Statistical ranking problems and Hodge decompositions of graphs and skew-symmetric matrices

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hodgeç†è«–
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Cloudera Blog
In an era where artificial intelligence (AI) is reshaping enterprises across the globeâ€”be it in healthcare, finance, or manufacturingâ€”itâ€™s hard to overstate the transf ormation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to [â€¦] Read blog post
xiangze 2016/11/28
spark

network
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xiangze 2016/10/27
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xiangze 2016/09/22
network

python

programming
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https://pubs.aip.org/aip/cha/article/26/6/065303/322481/Deep-graphs-A-general-framework-to-represent-and
xiangze 2016/08/26
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The Evolutionary Origins of Hierarchy
xiangze 2016/06/12
bioinformatics

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xiangze 2016/05/21
network

Database

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xiangze 2016/04/10
database

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VLDB'14 è«–æ–‡æŽ¡æŠžï¼šã‚°ãƒ©ãƒ•ã®æ§‹é€ ã‚’æ´»ç”¨ã—ãŸ Personalized PageRank ã®é«˜é€Ÿè¨ˆç®— - iwiwiã®æ—¥è¨˜
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xiangze 2016/03/31
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xiangze 2016/03/31
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xiangze 2016/03/26
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xiangze 2016/02/23
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xiangze 2016/01/18
gpu

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xiangze 2016/01/06
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