PUMA publications for /user/raphei/ComputerMon May 04 15:23:37 CEST 2020arXiv:1608.03251 [physics]augarXiv: 1608.03251Choosing {Collaboration} {Partners}. {How} {Scientific} {Success} in {Physics} {Depends} on {Network} {Positions}2016- Computer Digital Information Libraries Networks, Physics Science Social Society, and Physics is one of the most successful endeavors in science. Being a prototypic big science it also reflects the growing tendency for scientific collaborations. Utilizing 250,000 papers from ArXiv.org a prepublishing platform prevalent in Physics we construct large coauthorship networks to investigate how individual network positions influence scientific success. In this context, success is seen as getting a paper published in high impact journals of physical subdisciplines as compared to not getting it published at all or in rather peripheral journals only. To control the nested levels of authors and papers, and to consider the time elapsing between working paper and prominent journal publication we employ multilevel eventhistory models with various network measures as covariates. Our results show that the maintenance of even a moderate number of persistent ties is crucial for scientific success. Also, even with low volumes of social capital Physicists who occupy brokerage positions enhance their chances of articles in high impact journals significantly. Surprisingly, inter(sub)disciplinary collaborations decrease the probability of getting a paper published in specialized journals for almost all positions.Mon May 04 15:23:37 CEST 2020New YorkComputational {Social} {Networks}aug296--306Lecture {Notes} in {Computer} {Science}Shifts in {Collective} {Attention} and {Stock} {Networks}91972015(incl. Applications Artificial Collective Communication Computational Computer Data Database Discovery, Econophysics, Information Intelligence Internet), Knowledge Management, Mining Networks, Retrieval, Robotics), Stock Storage Systems Theory, and attention, crisis, financial networks science, In this paper, we combine network analytical methods to understand the structure of financial markets with recent research about collective attention shifts by utilizing massive social media data. Our main goal, hence, is to investigate whether changes in stock networks are connected with collective attention shifts. To examine the relationship between structural market properties and mass online behavior empirically, we merge company-level Google Trends data with stock network dynamics for all S\&P 100 corporations between 2004 and 2014. The interplay of massive online behavior and market activities reveals that collective attention shifts precede structural changes in stock market networks and that this connection is mostly carried by companies that already dominate the development of the S\&P 100.