Why Is the Shape of the Web a Bowtie?

The first time in Graph Theory a graph was characterized as “Bowtie” was in the seminal paper by Broder et. al. Though no textbook had ever mentioned this type of graph before, no less an important network than the Web Graph itself is supposed to resemble this shape. In two large collections of crawled Web pages and in numerous smaller collections, researchers discovered Bowties and Bowtie-looking graphs by studying millions of web pages. But why do collections of Web pages resemble a Bowtie? The short answer is “because, given the way the Web is created, that’s the only shape it could have”. This paper shows why this is the case and presents an algorithm and software to visualize Bowtie graphs

A 2007 Model Curriculum for a Liberal Arts Degree in Computer Science

In 1986, guidelines for a computer science major degree program offered in the context of the liberal arts were developed by the Liberal Arts Computer Science Consortium (LACS) [4]. In 1996 the same group offered a revised curriculum reflecting advances in the discipline, the accompanying technology, and teaching pedagogy [6]. In each case, the LACS models represented, at least in part, a response to the recommendations of the ACM/IEEE-CS [1][2]. Continuing change in the discipline, technology, and pedagogy coupled with the appearance of Computing Curriculum 2001 [3] have led to the 2007 Model Curriculum described here. This report begins by considering just what computer science is and what goals are appropriate for the study of computer science in the landscape of the liberal arts. A curricular model for this setting follows, updating the 1996 revision. As in previous LACS curricula, [4] and [6], the model is practical; that is, students can schedule it, it can be taught with a relatively small size faculty, and it contributes to the foundation of an excellent liberal arts education. Finally, this 2007 Model Curriculum is compared with the recommendations of CC2001 [3]

Technology, Propaganda, and the Limits of Human Intellect

Fake news is a recent phenomenon, but misinformation and propaganda are not. Our new communication technologies make it easy for us to be exposed to high volumes of true, false, irrelevant, and unprovable information. Future AI is expected to amplify the problem even more. At the same time, our brains are reaching their limits in handling information. How should we respond to propaganda? Technology can help, but relying on it alone will not suffice in the long term. We also need ethical policies, laws, regulations, and trusted authorities, including fact­checkers. However, we will not solve the problem without the active engagement of the educated citizen. Epistemological education, recognition of self biases and protection of our channels of communication and trusted networks are all needed to overcome the problem and continue our progress as democratic societies

Dynamic binding of driven interfaces in coupled ultrathin ferromagnetic layers

We demonstrate experimentally dynamic interface binding in a system consisting of two coupled ferromagnetic layers. While domain walls in each layer have different velocity-field responses, for two broad ranges of the driving field, H, walls in the two layers are bound and move at a common velocity. The bound states have their own velocity-field response and arise when the isolated wall velocities in each layer are close, a condition which always occurs as H->0. Several features of the bound states are reproduced using a one dimensional model, illustrating their general nature.Comment: 5 pages, 4 figures, to be published in Physical Review Letter

Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201

Plasmon interactions in the quark-gluon plasma

Yang-Mills theory at finite temperature is rewritten as a theory of plasmons which provides a Hamiltonian framework for perturbation theory with resummation of hard thermal loops.Comment: 12 pages, LaTeX, minor typos corrected, discussion adde

Sifting the Sand on the River Bank: Social Media as a Source for Research Data

Computational social science has been described as a new field at the intersection of computer science and social sciences, aiming to study the ways that society evolves, interacts, and reacts. Like prospectors sifting the sand in a river bed for gold, computational social science researchers are looking into the streams of social media for insight on our social interactions. Enabled by the availability of and easy accessibility to vast amounts of data generated by social entities, as well as by powerful computing hardware and algorithms, its researchers conduct observations of social interaction and experiments testing social theories in scales not realizable before. In this paper, after a short review of the characteristics of this new area, we discuss issues related to the types of data sought and used, and some of the challenges in collecting and interpreting the data. Throughout the paper we also examine some of the pitfalls awaiting and the standards that need to be observed