检索结果-内蒙古大学图书馆

Uncovering simultaneous breakthroughs with a robust measure of disruptiveness

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kim, Munjung Kojaku, Sadamori Ahn, Yong-Yeol Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States School of Systems Science and Industrial Engineering Binghamton University State University of New York United States

Progress in science and technology is punctuated by disruptive innovation and breakthroughs. researchers have characterized these disruptions to explore the factors that spark such innovations and to assess their long-term trends. However, although understanding disruptive breakthroughs and their drivers hinges upon accurately quantifying disruptiveness, the core metric used in previous studies—the disruption index—remains insufficiently understood and tested. Here, after demonstrating the critical shortcomings of the disruption index, including its conflicting evaluations for simultaneous discoveries, we propose a new, continuous measure of disruptiveness based on a neural embedding framework that addresses these limitations. Our measure not only better distinguishes disruptive works, such as Nobel Prize-winning papers, from others, but also reveals simultaneous disruptions by allowing us to identify the "twins" that have the most similar future context. By offering a more robust and precise lens for identifying disruptive innovations and simultaneous discoveries, our study provides a foundation for deepening insights into the mechanisms driving scientific breakthroughs while establishing a more equitable basis for evaluating transformative contributions. © 2025, CC BY.

关键词： Patents and inventions

Modeling resource consumption in the US air transportation system via minimum-cost percolation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kim, Minsuk Diggans, C. Tyler Radicchi, Filippo Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Air Force Research Laboratory’s Information Directorate RomeNY13441 United States

We introduce a dynamic percolation model aimed at describing the consumption, and eventual exhaustion, of resources in transportation networks. In the model, rational agents progressively consume the edges of a network along demanded minimum-cost paths. As a result, the network undergoes a transition between a percolating phase where it can properly serve demand to a non-percolating phase where demand can no longer be supplied. We apply the model to a weighted, directed, temporal, multi-layer network representation of the air transportation system that can be generated using real schedules of commercial flights operated by US carriers. We study how cooperation among different carriers could improve the ability of the overall air transportation system in serving the demand of passengers, finding that unrestricted cooperation could lead to a 30% efficiency increase compared to the non-cooperative scenario. Cooperation would require major airlines to share a significant portion of their market, but it would allow also for an increased robustness of the system against perturbations causing flight cancellations. Our findings underscore some key benefits that could emerge by simply promoting code-share arrangements among US airlines without altering their current cost of operation. © 2025, CC BY.

关键词： Air transportation

Predicting percolation thresholds in networks

学校读者我要写书评

暂无评论

Physical Review E 2015年第1期91卷 010801(R)-010801(R)页

作者： Filippo Radicchi []Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University Bloomington Indiana 47405 USA

We consider different methods, which do not rely on numerical simulations of the percolation process, to approximate percolation thresholds in networks. We perform a systematic analysis on synthetic graphs and a collection of 109 real networks to quantify their effectiveness and reliability as prediction tools. Our study reveals that the inverse of the largest eigenvalue of the nonbacktracking matrix of the graph often provides a tight lower bound for true percolation threshold. However, in more than 40% of the cases, this indicator is less predictive than the naive expectation value based solely on the moments of the degree distribution. We find that the performance of all indicators becomes worse as the value of the true percolation threshold grows. Thus, none of them represents a good proxy for the robustness of extremely fragile networks.

关键词： PERCOLATION (Statistical physics) ROBUST control MONTE Carlo method PERCOLATION MATRIX functions

Community-centric modeling of citation dynamics explains collective citation patterns in science, law, and patents

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Kojaku, Sadamori Mahari, Robert Lera, Sandro Claudio Moro, Esteban Pentland, Alex Ahn, Yong-Yeol Department of Systems Science and Industrial Engineering Binghamton University New York United States Media Lab Massachusetts Institute of Technology Cambridge United States Harvard Law School Cambridge United States Connection Science Massachusetts Institute of Technology Cambridge United States Southern University of Science and Technology Shenzhen China Network Science Institute Department of Physics Northeastern University Boston United States Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

Many human knowledge systems, such as science, law, and invention, are built on documents and the citations that link them. Citations, while serving multiple purposes [1–3], primarily function as a way to explicitly document the use of prior work and thus have become central to the study of knowledge systems [4–6]. Analyzing citation dynamics has revealed statistical patterns that shed light on knowledge production, recognition, and formalization [5, 7–14], and has helped identify key mechanisms driving these patterns [5, 15, 16]. However, most quantitative findings are confined to scientific citations, raising the question of universality of these findings. Moreover, existing models of individual citation trajectories fail to explain phenomena such as delayed recognition, calling for a unifying framework. Here, we analyze a newly available corpus of U.S. case law, in addition to scientific and patent citation networks, to show that they share remarkably similar citation patterns, including a heavy-tailed distribution of sleeping beauties. We propose a holistic model that captures the three core mechanisms driving collective dynamics and replicates the elusive phenomenon of delayed recognition. We demonstrate that the model not only replicates observed citation patterns, but also better predicts future successes by considering the whole system. Our work offers insights into key mechanisms that govern large-scale patterns of collective human knowledge systems and may provide generalizable perspectives on discovery and innovation across domains. © 2025, CC BY-NC-SA.

关键词： Patents and inventions

Truthy: Enabling the study of online social networks 13

学校读者我要写书评

暂无评论

Truthy: Enabling the study of online social networks

2013 2nd ACM Conference on Computer Supported Cooperative Work Companion, CSCW 2013

作者： McKelvey, Karissa Menczer, Filippo Center for Complex Networks and Systems Research Indiana University Bloomington IN United States

ISBN: (纸本)9781450313322

The broad adoption of online social networking platforms has made it possible to study communication networks at an unprecedented scale. Digital trace data can be compiled into large data sets of online discourse. However, it is a challenge to collect, store, filter, and analyze large amounts of data, even by experts in the computational sciences. Here we describe our recent extensions to Truthy, a system that collects Twitter data to analyze discourse in near real-time. We introduce several interactive visualizations and analytical tools with the goal of enabling citizens, journalists, and researchers to understand and study online social networks at multiple scales. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).

关键词： Social networking (online)

Scholarometer: A system for crowdsourcing scholarly impact metrics 14

学校读者我要写书评

暂无评论

Scholarometer: A system for crowdsourcing scholarly impact m...

6th ACM Web Science Conference, WebSci 2014

作者： Kaur, Jasleen Asbagh, Mohsen Jafari Radicchi, Filippo Menczer, Filippo Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University Bloomington IN United States

ISBN: (纸本)9781450326223

Scholarometer (***) is a social tool developed to facilitate citation analysis and help evalu- ate the impact of authors. The Scholarometer service allows scholars to compute various citation-based impact measures. In exchange, users provide disciplinary annotations of au- thors, which allow for the computation of discipline-specific statistics and discipline-neutral impact metrics. We present here two improvements of our system. First, we integrated a new universal impact metric hs that uses crowdsourced data to calculate the global rank of a scholar across disci- plinary boundaries. Second, improvements made in ambigu- ous name classification have increased the accuracy from 80% to 87%. Copyright © 2014 ACM.

关键词： discipline-bias crowdsourcing ambiguous name detection universal impact metrics citation analysis bibliometrics

Suppression of heading errors in Bell-Bloom optically pumped free-induction-decay alkali-metal atomic magnetometers

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Liu, Siqi Wang, Xueke Zhang, Xiangdong Xiao, Wei Sheng, Dong Department of Precision Machinery and Precision Instrumentation Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes University of Science and Technology of China Hefei230027 China College of Physics and Optoelectronic Engineering Shenzhen University Shenzhen518060 China Beijing Computational Science Research Center Beijing100193 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Center for Quantum Information Technology Peking University Beijing100871 China MIIT Key Laboratory of Complex-field Intelligent Sensing Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China

Heading errors of atomic magnetometers refer to the dependence of measurement results on the sensor orientation with respect to the external magnetic field. There are three main sources of such errors: the light shift effect, the linear nuclear-spin Zeeman effect, and the nonlinear Zeeman effect. In this work, we suppress the former two effects by using the Bell-Bloom optical pumping method and probe the atomic signals while the pumping beam is off, and focus on the heading error induced by nonlinear Zeeman effect while the sensor operates in the geomagnetic field range. We demonstrate several schemes to suppress this remaining heading error within 1 nT using a single magnetometer or a comagnetometer. In the magnetometer system, two schemes are developed to average out the horizontal atomic polarization in space or in time, respectively. In the comagnetometer system, we combine the simultaneously measured Larmor frequencies of two different kinds of alkali atoms to either suppress the heading error or extract the orientation of the pumping beam relative to the bias field. © 2025, CC BY.

关键词： Blooms (metal)

The diffusion of political memes in social media

学校读者我要写书评

暂无评论

The diffusion of political memes in social media

2012 1st ACM Workshop on Politics, Elections and Data, PLEAD 2012 - Co-located with CIKM 2012

作者： Menczer, Filippo Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University Bloomington IN United States

ISBN: (纸本)9781450317139

This talk presents ongoing work on the study of information diffusion in social media, focusing in particular on political communication in the Twitter microblogging network. Social media platforms play an important role in shaping political discourse in the US and around the world. The *** infrastructure [6] allows us to mine and visualize a large stream of social media data related to political themes [5]. The analyses in this keynote address polarization and cross-ideological communication, and partisan asymmetries in the online political activities of social media users [3]. Machine learning efforts can successfully leverage the structure of meme diffusion networks to detect orchestrated astroturf attacks that simulate grassroots campaigns [7, 4], and to predict the political affiliation of active users [1]. The retweet network segregates individuals into two distinct, homogenous communities of left- and right-leaning users. The mention network does not exhibit this kind of segregation, instead forming a communication bridge across which information flows between these two partisan communities. We propose a mechanism of action to explain these divergent topologies and provide statistical evidence in support of this hypothesis [2]. Related to political communication are questions about the birth of online social movements. Social media data provides an opportunity to look for signatures that capture these seminal events. Finally, I will introduce a model of the competition for attention in social media [8]. A dynamic of information diffusion emerges from this process, where a few ideas go viral while most do not. I will show that the relative popularity of different topics, the diversity of information to which we are exposed, and the fading of our collective interests for specific memes, can all be explained as deriving from a combination between the competition for limited attention and the structure of social networks. Surprisingly, one can repr

关键词： Learning systems

Traffic in social media I: Paths through information networks

学校读者我要写书评

暂无评论

Traffic in social media I: Paths through information network...

IEEE International Conference on Social Computing

作者： Ratkiewicz, Jacob Flammini, Alessandro Menczer, Filippo Center for Complex Networks and Systems Research School of Informatics and Computing Indiana University Bloomington IN United States

ISBN: (纸本)9780769542119

Wikipedia is used every day by people all around the world, to satisfy a variety of information needs. We cross-correlate multiple Wikipedia traffic data sets to infer various behavioral features of its users: their usage patterns (e.g., as a reference or a source);their motivations (e.g., routine tasks such as student homework vs. information needs dictated by news events);their search strategies (how and to what extent accessing an article leads to further related readings inside or outside Wikipedia);and what determines their choice of Wikipedia as an information resource. We primarily study article hit counts to determine how the popularity of articles (and article categories) changes over time, and in response to news events in the English-speaking world. We further leverage logs of actual navigational patterns from a very large sample of Indiana University users over a period of one year, allowing us unprecedented ability to study how users traverse an online encyclopedia. This data allows us to make quantitative claims about how users choose links when navigating Wikipedia. From this same source of data we are further able to extract analogous navigation networks representing other large sites, including Facebook, to compare and contrast the use of these sites with Wikipedia. Finally we present a possible application of traffic analysis to page categorization. © 2010 IEEE.

关键词： Information services