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NetSci雑感．
若い人の主役の会議．年上の大御所は参加しているけれど，オブザーバー的な感じで直接発表していない．(Kertesz, Stanley, Mantegna etc.) ニューヨーク州立大の佐山さんによると，世界中に散らばったバラバシの門下生が年に一度集まる同窓会みたいなもん，らしい．
こちらから見ると，普段読んでる論文の著者やエディターがうじゃうじゃいる訳で，結構目移りしながらの聴講．FujiRockとかSummerSonicとかもこんな感じなのかも．行ったこと無いけど．さらに良いことに，すぐ近くにそんな有名人が座っていたり，質問したり，されたり，なんでもできる感じで楽しい．最新の研究とかはちょっと「それで？」みたいなのも多くて，ピンとくる話は少ないが，分野全体の傾向を空気で感じられるのが貴重．
リンクが現れたり消えたりする，Temporal networkが流行中．物理学者が7割，コンピュータ科学者3割くらいな印象．物理学者もオリジンが物理で，今やっているのは計算機科学に近い人が多数で，属性が私に似ている．
データーの貴重さ等もあり，オープンか否かが１つのキーワード．bring home messageをキッチリ言う人が多いのも特徴的．
私自身の発表は成功と言えるが，質疑応答の質がNG.何を言っているかはほぼ，わかる(5/6)のだけれど，答えがone phraseで終わる．他の人はここぞとばかりに，付加情報をしたり，もっと知ってるんだぜ，やってるんだぜアピールをするのに，全然生かせない．この問題は英語というよりも，研究自体に関する問題なのでもっと，詰める研究をしてないと．物理学会での質疑応答もしかり．
あとはよくわからんが，googleのauthor検索みたいなものをみんな発表者に対して行っていた．私もやってみる．
以下はランダムなメモ書き．
1. Havlin
social network
1 How created Gallos et al. PRX(2012)
Motifs occur significantly
2 How indentify spreaders Kitsak et al. Nature Phys (2020)
K-shell better than degree or betweenes
3 Formed? Shao PRL(2009), Qian et al. J. Stat.Phys (2013)
Agent's own opinion should be considered
4 extreme opinion Makase et al. arXiv
Agents surrounding by less extreme people are more stubborn(頑固)
1 Interaction: 5 basic mechanism
change, balance, distant, collective attention, structure hole
-> distant most big
2 k-shell decomposition analysis can identify the spreader
3 J. Shao's idea -> majority rule + own opinion = Non concensus state (NCO)
4 extreme shows nonlinear increase with normal
NCO model can reproduce
2. Tumminello
statistically validated network
Bipartise network 1. suspect - crime 2. market members - transactions
crimes per suspect power law
types per crime power law
Bonferroni test -> restrict network
happy aging?
4. Adamic
Cascade two types of photo Obama Victory vs Million Like Me
age, gender, country, political difference
spreading is different
[exogenous effect exists, TV or web news ? vs exogenous]
5. Pedreschi
Orange challenge -> Ivory court mobile phone data
6. Toole
Mobile data and employment
Do layoff affect social networks?
Call data and layoff data
7 Yasseri
Revert network
10 Lamboitte
Online game network : structural balance
myPersonality -> facebook application : psychological mesaurment
5 dimentions OCEAN
30 -1000 facebook friends = typical person
(historical study is limited, biased but massive data )
Kertez: online <- -> offline = some similarity
11 Tredan
SOUK project
Tag + human -> position
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0. Leskovic
Collaborate with twitter : follow - follower network very temporal
Visited Tokyo for Kitsunegawa on March
15? students
1. Lamboitte
M. McPherson, L. Smith-Lawin J.M. Cook (2001)
Amm.Rev.Social 27.415
Need for new algorithms, New theoretical questions
2. Masuda
SocioPatterns Project
burst : CV = stad(tau)/<tau> CV=1 for poisson, CV=0 for periodic
Softky & Neurosc (i1993) classic
or
Goh & Barabasi B
Correlated inter event time by Takaguchi et al., PRX
-> Model : Queue model by OR = Exponential
+ Priority queue model by Barabasi =Power-law task has priority i.i.d. random value
reproduce power-law inter-event time distribution with exponent 1
but some result shows 1.5.
but not every task has priority
-> Model Two-state point process
idea is well known in neuroscience up/down state
+ Cascading Process process by Malgren, Amaral et al., PNAS 2008
circadian pattern and self-exciting nature
( Neuroscience seems to be useful...)
+ Two-state model by Karsai Sci. Rep. (2012)
normal <--> excited
Temporal walk
Synchronized triangles
Randomization distribution keep + order destroy
(1)Activity-driven model for temporal network Perra et al., Sci. Rep. 2012
-> most stable model for the moment
Holme
Data-driven vs hypothesis-driven
Leskovic
badge change behavior? -> around thsrehold, yes
2-dimention model
direction = preference
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Raj
World citation
How about population. Talking about Gravity raw, I naturally think that compare mass as population.
You put number of citation as mass term.
You can compare these two.
Is there any threshold, number of authors or countries ?
How about Japan cost efficiency?
I think Kweit is spend money is for other foregin countries.
Budgets can be normalized by DGP or currency rate or publication fee.
Global language network
Wikipedia, Twitter, Translation ->
Japanese is not central but second largest population in twitter.
but wikipedia is well connected
LINKS 2013 July 22-23 2013 Cambridge
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Leskovec
Community -> based on weak ties i.e., centrality, betweenness
Overlapping communities : confirm based on true data
Communities s "tiles" = Simmel(1955), ot Granovetter(1973)
Overlapping, Non-overlapping, nested
core = most overlapping area
Moon
snowball sampling: サンプルしたら，次のサンプルの人を紹介してもらう
Sano
Self-avoiding effect ? by Sayama
Why shuffled document.
Marton
Activity driven model with memory
Reinforced network
Temporal effect + memory
Rumor spreading Daley & Kendall (1964)
http://arxiv.org/pdf/1303.5966v1.pdf
Hon
Contextual analysis framework
Burst + Context
Context = friends
Barabasi, Watts, Strogatz, Takayasu(の本)が呼び戻してくれた．自由で刺激的な世界．ありがたい．この世界でどうか生き続けられますように．
