In this article, we study the international mobility of researchers in the field of computer science (CS). Our analysis hinges upon Scopus data spanning a time period of 30 years (1991–2020) and ...involves a total of 969,835 researchers and 8,412,543 publications. Our contribution is two-fold. First, we characterize mobility as a fairly common phenomenon in CS, we highlight a strong correlation with standard bibliometric indicators at all seniority levels and a lower propensity of female researchers to relocate internationally than their male colleagues. Second, we analyze individual career paths building from them a mobility graph and identifying common patterns, such as the most traveled connections between different countries, whether they are equally traversed in both directions and the most frequently visited countries. The temporal evolution of the above patterns within our 30-year time frame is also investigated. The United States emerged as a preferred destination for internationally mobile authors, with strong connections to China (from the early 2000s), Canada, and several prominent European countries, most notably the United Kingdom, Germany, and France.
Abstract Listing dense subgraphs is a fundamental task with a variety of network analytics applications. A lot of research has been done focusing on $k$-cliques, i.e. complete subgraphs on $k$ nodes. ...However, requiring complete connectivity between the nodes of a subgraph may be too restrictive in many real applications. Hence, in this paper, we consider a natural relaxation of cliques, called $k$-diamonds and defined as cliques of size $k$ with one missing edge. We first provide a sequential algorithm that, in $O(nm^{(k-1)/2})$ time, counts and lists all the $k$-diamonds in large graphs, for any constant $k \geq 4$. A parallel extension of the sequential algorithm is then proposed and analyzed in a MapReduce-style model, achieving the same local and total space usage of the state-of-the-art algorithms for $k$-cliques. The running time is optimal on dense graphs and $O(\sqrt{m})$ larger than $k$-clique counting if the graph is sparse. Our algorithms compute induced diamonds by analyzing the structure of directed stars formed by the graph nodes and their neighbors.
In this paper we develop simple and fast multicore parallel algorithms for counting the number of k-cliques in large undirected graphs, for any small constant k ≥ 4. Clique counting is an important ...problem in a variety of network analytics applications. Differently from existing solutions, which mainly target distributed memory settings (e.g., MapReduce), our algorithms work on off-the-shelf shared-memory multicore platforms.
We assess the effectiveness of our approach through an extensive experimental analysis on a variety of real-world graphs, considering different clique sizes and scalability on different numbers of cores. The experimental results show that our parallel algorithms largely outperform the running times of highly optimized sequential solutions and gracefully scale to non-trivial values of k even on medium/large graphs. For instance, computing hundreds of billions of cliques for rather demanding Web graphs and social networks requires about 15 min on a 32-core machine. As a by-product of our experimental analysis, we also compute the exact number of k-cliques with at most 20 nodes in many real-world networks from the SNAP repository.
In this article, we study the evolution of the computer science research community over the past 30 years. Analyzing data from the full Scopus database, we investigate how aspects such as the ...community size, gender composition, and academic seniority of its members changed over time. We also shed light on the varying popularity of specific research areas, as derived from the ACM’s Special Interest Groups and IEEE classifications. Our analysis spans 19 nations (all members of the G20 group, excluding the EU) and involves a total of 728,374 authors and 8,412,543 publications. This work shows that the overall size of the computer science community has increased by a factor of ten in the time period 1991–2020, with China and India enjoying the highest growth. At the same time, this increase has not been uniform across research areas. Female participation has also increased, but more slowly than expected and not uniformly across countries and areas.
Due to its quantitative nature, bibliometrics is becoming increasingly popular among policy makers for academic hiring and career promotions. In this article, we quantitatively assess the impact that ...the granularity level in the classification of scientific areas would entail on research evaluation based on bibliometric indicators. We use as a case study the Italian national habilitation system (ASN), which classifies faculty members according to their academic discipline and relies on journal counts, citations, and h-indices as a basis for promoting tenure track researchers to associate professors and associate to full professors. The assessment checks whether the individual indicators of a researcher are above a certain threshold, e.g., the median over the population of researchers working in the same discipline. Our investigation focuses on two related, rather broad disciplines: computer science and computer engineering. We show that the ASN practice of using the same thresholds for all members of a scientific discipline can favor certain sub-communities that are characterized by higher bibliometric indicators, and disfavor others. We report evidence that up to 30% of Italian faculty members of certain sub-communities would see their indicators drop below the threshold, thus becoming not eligible for promotion, if the ASN were conducted on a more accurate, fine-grained classification. Conversely, in the same scenario, up to 11% of faculty members, in different sub-communities, would see their indicators rise above the threshold, granting them eligibility. Our data set includes 1685 authors, 89,185 distinct publications, and 262,286 author-publication pairs.
Mining hot calling contexts in small space D'Elia, Daniele Cono; Demetrescu, Camil; Finocchi, Irene
Software, practice & experience,
08/2016, Volume:
46, Issue:
8
Journal Article
In this paper we present CrumbTrail, an algorithm to clean large and dense knowledge graphs. CrumbTrail removes cycles, out-of-domain nodes and non-essential nodes, i.e., those that can be safely ...removed without breaking the knowledge graph’s connectivity. It achieves this through a bottom-up topological pruning on the basis of a set of input concepts that, for instance, a user can select in order to identify a domain of interest. Our technique can be applied to both noisy hypernymy graphs – typically generated by ontology learning algorithms as intermediate representations – as well as crowdsourced resources like Wikipedia, in order to obtain clean, domain-focused concept hierarchies. CrumbTrail overcomes the time and space complexity limitations of current state-of-art algorithms. In addition, we show in a variety of experiments that it also outperforms them in tasks such as pruning automatically acquired taxonomy graphs, and domain adaptation of the Wikipedia category graph.
In this article we present a building block technique and a toolkit towards automatic discovery of workload-dependentperformance bottlenecks. From one or more runs of a program, our profiler ...automatically measures how the performance of individual routines scales as a function of the input size, yielding clues to their growth rate. The output of the profiler is, for each executed routine of the program, a set of tuples that aggregate performance costs by input size. The collected profiles can be used to produceperformance plots and derive trend functions by statistical curve fitting techniques. A key feature of our method is the ability toautomatically measure the size of the input given to a generic code fragment: to this aim, we propose an effective metric for estimating the input size of a routine and show how to compute it efficiently. We discuss several examples, showing that our approach can reveal asymptotic bottlenecks that other profilers may fail to detect and can provide useful characterizations of the workload and behavior of individual routines in the context of mainstream applications, yielding several code optimizations as well as algorithmic improvements. To prove the feasibility of our techniques, we implemented a Valgrind tool called aprof and performed an extensive experimentalevaluation on the SPEC CPU2006 benchmarks. Our experiments show that aprof delivers comparable performance to otherprominent Valgrind tools, and can generate informative plots even from single runs on typical workloads for mostalgorithmically-critical routines.
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