Competition is an essential mechanism for evolution and dynamics of various complex systems found in ecological, biological, social, information, and economic systems. Competition is also a quintessential network problem; citations and sports schedules are examples where understanding the mechanisms of competition under heterogeneous network topology and dynamics is paramount for predicting success and understanding long-term stability. The starting point for predicting winners in a network is centrality, a fundamental concept in network science that has been fruitfully used for information retrieval and relevance detection. This workshop is intended to foster exchange of ideas and collaboration in competition and centrality applicable to a diverse field of network science: social science, citations, sports, and ecology.

Organizers (e-mail: competenet-2016@googlegroups.com): please contact us with any questions

Time and Venue

  • Time: 2:30-6:30 PM on May 30 (Monday), 2016
  • Venue: The-K Hotel, Seoul, South Korea

Registration and Attendance

Per NetSci policy, we are able to serve refreshments only to registered participants. Attendance is free for attendees of NetSci 2016, although you need to register for our event. Please e-mail the organizers by clicking here with the following information:

  • Name
  • E-mail
  • Affiliation


CLOSED Submissions for Contributed Talks Deadline: Apr 15 

We have 4-5 slots for contributed talks. Please use Easychair (link here) to send in an abstract of 200-300 words. Alternatively, you may e-mail the organizers by clicking here.

Detailed Abstracts

  1. Aaron Clauset (University of Colorado-Boulder, USA)
    • Title: Systematic inequality and hierarchy in faculty hiring networks
    • Abstract: The faculty job market plays a fundamental role in shaping research priorities, educational outcomes, and career trajectories among scientists and institutions. However, a quantitative understanding of faculty hiring as a system, and the competitive processes that underlie it, is lacking. Using a simple technique to extract the institutional prestige ranking that best explains an observed faculty hiring network — who hires whose graduates as faculty — we present and analyze comprehensive placement data on nearly 19,000 regular faculty in three disparate disciplines. Across disciplines, we find that faculty hiring follows a common and steeply hierarchical structure that reflects profound social inequality. Furthermore, doctoral prestige alone better predicts ultimate placement than a U.S. News & World Report rank, women generally place worse than men, and increased institutional prestige leads to increased faculty production, better faculty placement, and a more influential position within the discipline. These results advance our ability to quantify the influence of prestige in academia and shed new light on the academic system.
    • Remark: This is joint work with Samuel Arbesman and Daniel B. Larremore.
  2. Filippo Radicchi (Indiana University, Bloomington, USA)
    • Title: Sleeping Beauties in Science
    • Abstract: Scientific papers typically have a finite lifetime: their rate to attract citations achieves its maximum a few years after publication, and then steadily declines. Previous studies pointed out the existence of a few blatant exceptions: papers whose relevance has not been recognized for decades, but then suddenly become highly influential and cited. The Einstein, Podolsky, and Rosen “paradox” paper is an exemplar Sleeping Beauty. We study how common Sleeping Beauties are in science. We introduce a quantity that captures both the recognition intensity and the duration of the “sleeping” period, and show that Sleeping Beauties are far from exceptional. The distribution of such quantity is continuous and has power-law behavior, suggesting a common mechanism behind delayed but intense recognition at all scales.
  3. Muhamma Hakeem (Tohoku University, Japan)
    • Title: Transformation within Eurozone’s Investment Network
    • Abstract: The European Union and Eurozone present a curious case of strongly interconnected network with high degree of dependence among nodes. This research focuses on investment network of European Union and its major trading partners for specific time period. The results explain the level of interconnectedness among member states with respect to private investment in equity markets. The timeframe (2001-14) considered for analysis elucidates the direction of liquidity flow and prominence of few nodes with the passage of time. The introduction of Euro and expansion of Eurozone are, among the fundamental changes, EU’s investment network incorporates. The changing patterns and characteristics of investment network are explained by growing degree distribution, weighted degree and higher number of edges among same nodes. The network measures entail the shifting behavior by revealing the more concentrated and intricate investment designs among Eurozone nations. Investment network visualization strengthens the opinion based on network’s statistical measures, such as increasing graph density and countries with higher and lower centrality measures with respect to liquidity inflows and outflows. Recent issues of network measures and relevance with financial network transformations are discussed by Joseph & Chen (2014) and Tonzer (2015) in their respective work.
  4. Sebastian Ahnert (University of Cambridge, UK)
    • Title: Competition Networks From Partial Rankings
    • Abstract: When compiling a ranking based on ratings by individuals one has to typically rely on incomplete information. Not all of those who rate a set of entities (movies, restaurants, etc.) have sampled all the entities that are to be rated. We explore the application of a network-based approach to this problem, by using competition networks based on partial rankings. A comparison with other ranking approaches, for example based on average ratings, highlights the relative advantages and disadvantages of a network-based approach in this context.
  5. Naoki Masuda (University of Bristol, UK)
    • Title: Ranking professional tennis players using a temporal network centrality measure
    • Abstract: Centrality measures for networks have been applied to ranking of players and teams in competitive sports. In this presentation, we discuss a dynamic variant of such a centrality measure, motivated by the fact that the results of matches constitute a time series. We apply it to men’s professional tennis data and compare its performance in terms of the prediction accuracy with other ranking systems.
    • This study has been done in collaboration with Shun Motegi.
  6. Juyong Park (KAIST, Korea)
    • Title: TBA
    • Abstract: TBA
  7. Seung-Woo Son (Hanyang University, Korea)
    • Title: TBA
    • Abstract: TBA
  8. Roberta Sinatra (Central European University, Budapest, Hungary)
    • Title: Science without Borders
    • Abstract: Assessing the research output of an institution or a country is of vital importance for governments, agencies and entities that have to decide about scientific priorities and funding. For this reason in the last years a number of analyses and metrics have been proposed to capture the scientific impact of countries, regions and institutions. Most of these analyses are based on number of publications, their citations, and GDP invested in research. However at the same time we know that knowledge flows in nontrivial ways, based on geography, distribution of and access to resources, language, potentially affecting the scientists’ citation practices from institution to institution, from country to country. For example, do scientists prefer to cite the work of fellow scientists from the same institution or the same nationality, independently of merit? How does this inflate the number of citations of large institutions, hosting a high number of scientists? Is the large number of citations of a large country the simple consequence of its productivity? What is the effect of geographic distance in getting citations? We will answer these questions by studying the flow of citations at two levels: across geographical regions and across research institutions. We will show that in both cases the citation flow is driven by three factors: distance, the national borders between countries, and local collaborations. We combine these three elements into a model, which allows us to predict the citation patterns. The model also allows us to rescale citations to discount for effects other than innate merit, providing measures of impact for a ‘science without borders’, with many crucial policy implications.


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