ฑจธๆศหฃบศ๐สฟธฅภ๏ฑคด๓ัง Matus Medo
ฑจธๆสฑผไฃบ2018ฤ๊7ิย7ศีษฯฮ็11:05
ฑจธๆตุตใฃบปดฐฒษ๑อ๚ด๓พฦต๊ฐฤฟอืใฒสอ๘ฑจธๆฬ�
ฑจธๆีชาชฃบComplex networks theory is a rapidly developing interdisciplinary research field. In this talk, we will focus on growing networks where time plays a crucial role, and which are relevant in diverse fields such as bibliometrics (to model citations among scientific papers) and e-commerce (to model users and their purchases on Amazon.com, for example). We will show that usual network metrics and algorithms that ignore the time information produce inferior or even misleading results. To resolve this problem, we will introduce time-aware methods for classical problems such as ranking the network nodes and community detection.ฃจธดิำอ๘ย็ภํย�สวาปธ๖ฟ์หูทขีนตฤฟ็ังฟฦัะพฟม์ำ๒กฃิฺีโดฮั�ฝฒึะฃฌฮารวฝซพ�ฝนฒปถฯิ๖ณคตฤอ๘ย็ฃฌิฺีโะฉอ๘ย็ึะฃฌสฑผไฐ็ั�ืลึมนุึุาชตฤฝวษซฃฌำ๋ฒปอฌตฤม์ำ๒ฯเนุฃฌฑศศ็ฮฤฯืผฦมฟัง(ถิฟฦังย�ฮฤตฤา�ำรฝ๘ะะฝจฤฃ)บอต็ืำษฬฮ๑(ภ�ศ็ฃฌถิำรปงผฐฦไิฺัวยํัทอ๘ีพษฯตฤนบย๒ะะฮชฝ๘ะะฝจฤฃ)กฃอจณฃตฤอ๘ย็ถศมฟบอหใทจำษำฺบ๖ยิมหสฑผไะลฯขฃฌฝซตผึยมำึสษ๕ึมสวฮ๓ตผะิตฤฝแน๛กฃฮชมหฝโพ๖ีโาปฮสฬโฃฌฮารวฝ้ษ�าปะฉพญตไฮสฬโตฤสฑผไธะึชทฝทจฃฌศ็ถิอ๘ย็ฝฺตใฝ๘ะะลละ๒บอษ็ว๘ผ์ฒโกฃฃฉ
ฑจธๆศหผ๒ฝ้ฃบData scientist at the Department of Clinical Research, University of Bern. Lecturer at the Department of Physics, University of Fribourg. Research fellow at the Institute of Fundamental and Frontier Sciences, UESTC Chengdu. Research interests: complex networks, information filtering, data analysis. Published multiple research papers on top journal, PRL, PNAS, Physics Report etc, Received over 2000 citations. ฃจฒฎถ๛ฤแด๓ังมูดฒัะพฟฯตส�พ�ฟฦังผากฃ ธฅภ๏ฑคด๓ังฮ๏ภํฯตฝฒสฆกฃ ณษถผต็ืำฟฦผผด๓ังป๙ดกำ๋วฐัุฟฦังัะพฟห๙ัะพฟิฑกฃ ัะพฟะหศคฃบธดิำอ๘ย็ฃฌะลฯขน�ยหฃฌส�พ�ทึฮ๖กฃ ิฺPRLฃฌPNASฃฌPhysics Reportตศถฅผถฦฺฟฏทขฑํถเฦชัะพฟย�ฮฤฃฌื�า�ำรณฌน�2000ดฮกฃฃฉ
