Inheritance patterns in citation networks reveal scientific memes

Memes are the cultural equivalent of genes that spread across human culture by means of imitation. What makes a meme and what distinguishes it from other forms of information, however, is still poorly understood. Our analysis of memes in the scientific literature reveals that they are governed by a surprisingly simple relationship between frequency of occurrence and the degree to which they propagate along the citation graph. We propose a simple formalization of this pattern and we validate it with data from close to 50 million publication records from the Web of Science, PubMed Central, and the American Physical Society. Evaluations relying on human annotators, citation network randomizations, and comparisons with several alternative approaches confirm that our formula is accurate and effective, without a dependence on linguistic or ontological knowledge and without the application of arbitrary thresholds or filters.Comment: 8 two-column pages, 5 figures; accepted for publication in Physical Review

Streams of Media Issues, Monitoring World Food Security. Paper presented at the United Nations

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Stepping on the cracks—transcending the certainties of big data analytics

Every aspect of modern life is dominated by decision-making and the availability of data. We constantly access, process and evaluate data as we navigate complex and uncertain problem spaces. Communication and Information Technologies (ICTs) have developed to a point where it is possible for very large data sets, measured in Exabyte, to be stored across many servers and gathered by many different people and organizations, for multiple purposes. At the same time, research into Artificial Intelligence has progressed to a point where human decision-making can be supported, or even replaced, by intelligent agents and robotics. We recognize that many routine jobs that were once carried out by people can now be done faster and more flexibly using robotics, and software robotics has now moved beyond the factory and into administrative processes. The possibilities for such systems are enormous and can deliver many benefits to business, governments and ordinary citizens. However, there is also a downside to be considered. Is there still a role for human experience and intuition? How can we ensure that the benefits of analytics and AI continue to outweigh threats? How should we approach management of BI and AI on an on-going basis? This paper advocates an open systems approach in which B&AI may be incorporated with tools that support complex methods of inquiry

Overlapping Hierarchical Clustering (OHC)

International audienceAgglomerative clustering methods have been widely used by many research communities to cluster their data into hierarchical structures. These structures ease data exploration and are understandable even for non-specialists. But these methods necessarily result in a tree, since, at each agglomeration step, two clusters have to be merged. This may bias the data analysis process if, for example, a cluster is almost equally attracted by two others. In this paper we propose a new method that allows clusters to overlap until a strong cluster attraction is reached, based on a density criterion. The resulting hierarchical structure, called a quasi-dendrogram, is represented as a directed acyclic graph and combines the advantages of hierarchies with the precision of a less arbitrary clustering. We validate our work with extensive experiments on real data sets and compare it with existing tree-based methods, using a new measure of similarity between heterogeneous hierarchical structures