There was a time when polymaths like Galileo knew all the physics that was there to be known. Over the centuries, however, the body of knowledge spanned by physics exploded, encompassing topics as diverse as gravitational waves, graphene, or network science. As physics expanded in breadth and depth, physicists were forced to specialise, 1 segmenting researchers into their narrow, specialised communities. How many physicists work in each subfield of physics today and how does each subdiscipline evolve? In which subfield are physicists "born" into and where do they migrate, if at all? Here we take an intellectual census of physicists, their activities and career trajectories, helping us understand the evolution of the field and gaining arXiv:1901.02789v1 [physics.soc-ph] 9 Jan 2019 quantitative insights about several fundamental scientific processes, from resource allocation to the exchange of knowledge. Advances in this direction were limited by the challenge in answering two fundamental questions: 1) Who can be counted as a physicist? 2) How do we survey their activities? The recent availability of large datasets of scientific publications finally offers opportunities to tackle these questions by exploring the production patterns of the scientific population. 2, 3 Indeed, the close to complete publication records of all physicists allow us to reconstruct their subfields of study and career changes, offering quantitative footprints not just for the field of physics, but its intimate relation with the broader scientific community. 4, 5 Combining large-scale data on physics publications and citations with recent data and network science techniques, here we ask: What are the impact and productivity differences between subfields? As a physics student choosing my future specialty, how do I know which subfields are growing? As a funding agency, how do I compare early-career physicists from different subfields? As a journal editor, how many papers should I expect from each subfield and how do I compare their impact? A census of physics subfieldsTo offer a data-driven answer to these questions, 2, 3 we identify the relevant physics papers and citations within Web of Science (WoS). We start by selecting ∼3.2 million physics papers, published in 294 physics journals indexed by WoS. This core represents, however, only a fraction of all physics papers , 5, 6 missing for example those published in interdisciplinary journals like Nature or Science, or papers published in journals of other disciplines but that are of direct relevance for the physics community. To map out the complete physics literature we then set to detect physics papers by virtue of their patterns of citations among the other ∼47 million papers in WoS. A paper is a potential physics publication if its references and citations to the core 2/24 physics literature are significantly higher than in a null model in which each paper's citations are assigned randomly, regardless of a paper's journal or research area. We identified ∼4.5 million papers whose patterns...
The cornerstone of modern finance is the efficient market hypothesis. Under this hypothesis all information available about a financial asset is immediately incorporated into its price dynamics by fully rational investors. In contrast to this hypothesis many studies have pointed out behavioral biases in investors. Recently it has become possible to access databases that track the trading decisions of investors. Studies of such databases have shown that investors acting in a financial market are highly heterogeneous among them, and that heterogeneity is a common characteristic of many financial markets. The article describes an empirical study of the daily trading decisions of all Finnish investors investing Nokia stock over a time period of 15 years. The investigation is performed by adapting and using methods and tools in network science. By investigating daily trading decisions, and by constructing the time-evolution of statistically validated networks of investors, clusters of investors-and their time evolution-which are characterized by similar trading profiles are detected. These clusters are performing distinct trading decisions on time scales ranging from several months to twelve years. These empirical observations show the presence of an ecology of groups of investors characterized by different attributes and by various investment styles over many years. Some of the detected clusters present a persistent over-expression of specific investor categories. The study shows that the logarithm of the ratio of pairs of statistically validated trading decisions is different for different values of the market volatility. These findings suggest that an ecology of investors is present in financial markets and that groups of traders are always competing, adopting, using and eventually discarding new investment strategies. This adaptation process is observed over a multiplicity of time scales, and is compatible with several conclusions of behavioral finance and with the assumptions of the so-called adaptive market hypothesis.
A deluge of new data on real-world networks suggests that interactions among system units are not limited to pairs, but often involve a higher number of nodes. To properly encode higher-order interactions, richer mathematical frameworks such as hypergraphs are needed, where hyperedges describe interactions among an arbitrary number of nodes. Here we systematically investigate higher-order motifs, defined as small connected subgraphs in which vertices may be linked by interactions of any order, and propose an efficient algorithm to extract complete higher-order motif profiles from empirical data. We identify different families of hypergraphs, characterized by distinct higher-order connectivity patterns at the local scale. We also propose a set of measures to study the nested structure of hyperedges and provide evidences of structural reinforcement, a mechanism that associates higher strengths of higher-order interactions for the nodes that interact more at the pairwise level. Our work highlights the informative power of higher-order motifs, providing a principled way to extract higher-order fingerprints in hypergraphs at the network microscale.
Recent empirical evidence has shown that in many real-world systems, successfully represented as networks, interactions are not limited to dyads, but often involve three or more agents at a time. These data are better described by hypergraphs, where hyperlinks encode higher-order interactions among a group of nodes. In spite of the extensive literature on networks, detecting informative hyperlinks in real world hypergraphs is still an open problem. Here we propose an analytic approach to filter hypergraphs by identifying those hyperlinks that are over-expressed with respect to a random null hypothesis, and represent the most relevant higher-order connections. We apply our method to a class of synthetic benchmarks and to several datasets, showing that the method highlights hyperlinks that are more informative than those extracted with pairwise approaches. Our method provides a first way, to the best of our knowledge, to obtain statistically validated hypergraphs, separating informative connections from noisy ones.
Network dismantling has recently gained interest in the fields of intelligence agencies, anti-corruption analysts and criminal investigators due to its efficiency in disrupting the activity of malicious agents. Here, we apply this approach to detect effective strategies for targeted attacks to Cosa Nostra by analysing the collaboration network of affiliates that participate to the same crimes. We preliminarily detect statistically significant homophily patterns induced by being member of the same mafia syndicate. We also find that links between members belonging to different mafia syndicates play a crucial role in connecting the network into a unique component, confirming the relevance of weak ties. Inspired by this result we investigate the resilience properties of the network under random and targeted attacks with a percolation based toy model. Random removal of nodes results to be quite inefficient in dismantling the network. Conversely, targeted attacks where nodes are removed according to ranked network centralities are significantly more effective. A strategy based on a removal of nodes that takes into account how much a member collaborates with different mafia syndicates has an efficiency similar to the one where nodes are removed according to their degree. The advantage of such a strategy is that it does not require a complete knowledge of the underlying network to be operationally effective.
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