Daily Archives: Saturday November 23rd, 2019

3 posts

Algorithmic guidance on cultural good consumption (online/offline)

Computer Science: One possible internship

Context

The effects of recommendation algorithms on the access to information and cultural goods is at the center of a growing debate, which aims at assessing whether they rather contribute to enlarge or to restrain the horizon of users with respect to their “organic” behavior, i.e. absent algorithms.

Goals

This internship topic focuses on the impact of algorithmic guidance on cultural good consumption, specifically musical goods. It aims at addressing the following question: to what extent could we say that traditional musical “recommendation” (radios, music libraries, record stores) is more or less diversified and/or serendipous than algorithmic recommendation (e.g., on YouTube or leading music streaming platforms)? In other words, we aim at appraising the discrepancy between online and offline guidance.

The intern would benefit from significant autonomy in the design and realization of the empirical measures, protocol, and result analysis. Besides, fully anonymized data coming from a leading music streaming platform would be readily available from the beginning of the internship.

Intended audience

We open one internship under the joint supervision of Camille Roth and Jérémie Poiroux. Applicants should ideally be achieving a master’s degree in computer science and related fields (e.g., applied mathematics); modeling and/or online data collection skills are desirable.

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).

User confinement in online communities (SNA and info-viz)

Computer Science: Two possible internships

Context

User confinement (or containment) in online communities – variously denoted as, inter alia, balkanization, bubbles, echo chambers, fragmentation – is at the core of a growing number of studies. In the framework of Algodiv and Socsemics, the team contributes to advancing the formalization and the empirical appraisal of the informational and interactional confinement of individuals in web communities. This internship would aim at either appraising or visualizing user confinement on Twitter and its topical subnetworks, building upon exploratory work previously achieved within the team.

Goals

There are two main directions for further research at the moment in this context:

  • First, adopting a graph-theoretic and social network analysis perspective, in order to refine existing measures of structural confinement in topical Twitter subnetworks and then generalize some of the preliminary results, both in a methodological and in an empirical manner (principally by streamlining a robust empirical protocol to assert the distribution and magnitude of confinement);
  • Second, from an information visualization standpoint, by developing an interactive visualization interface that renders the structural-topical confinement of users both at the ego-centered level (local neighborhood of users) and the global level (a whole topical network).

Intended audience

We open up to two internships under the joint supervision of Camille Roth and Jérémie Poiroux.

For the first internship, applicants should ideally be achieving a master’s degree in computer science and related fields (e.g., applied mathematics); prior knowledge of network theory and/or online data collection is desirable.

The second internship targets students in information visualization and information design.

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).

Automatic Hypothesis Generation for Network Growth Models

Computer Science: Two possible internships

Context

Networks have become a fundamental abstraction for modeling systems across many scientific fields. Plausible hypothesis describing their growth processes can help us understand a wide range of phenomena, but formulating such hypothesis is often challenging and requires insights that may be counter-intuitive. In the last years, we have developed an approach to automatically discover realistic network growth models from empirical data, employing a machine learning technique inspired by natural selection, and defining a unified formalism to describe such models as a mathematical function of arbitrary complexity [1]. As the proposed method is completely general and does not assume any pre-existing models, it can be applied “out of the box” to any given network. By automating hypothesis generation and validation, this research is aligned with the ambitious idea of creating Artificial Scientists. We have released an open source tool [2], recently ported to Python, to make this method easily accessible to the scientific community.

Goals

There are two main areas of improvement at the moment in this context:

  • The first one is related to the efficiency and speed of the search that this tool achieves, and which is probably one of the main barriers for a more general adoption of this scientific instrument, especially for larger networks. There are a number of opportunities for improving performance and scalability. In this respect the internship would focus on proposing algorithmic improvements targeting speed and scalability; performing rigorous tests of these proposals, both in terms of performance and correctness; applying viable improvements to the open source tool.
  • The other one corresponds to a categorization issue: similar or equivalent generators can be described by different mathematical functions, which are expressed as formal trees combining mathematical operators and constants. Automatically detecting groups of relatively similar functions is a highly desirable improvement to the tool which would demonstrate the existence of families of fundamental network generative processes. This issue is at the interface between computer science, network science and applied mathematics.

Both topics put much more importance on the improvement of the scientific concepts underlying each task rather than the more low-level issues regarding the pure optimization of the code itself.

Intended audience

We open up to two internships. Candidates should be achieving a Masters Degree in Computer Science and related fields. Beyond a sufficient knowledge in Computer Science, desirable skills include in particular : algorithmic complexity and graph theory, network science, machine learning (especially evolutionary computation / genetic programming), as well as python and its common scientific libraries. Ability to innovate autonomously is expected.

References

[1] Menezes, T. and Roth, C., 2014. Symbolic regression of generative network models. Scientific reports, 4, p.6284. https://www.nature.com/articles/srep06284

[2] https://github.com/telmomenezes/synthetic

Practical Details

  • The internship could last between three and six months;
  • The internship is based in Berlin at the Centre Marc Bloch;
  • The intern should have working proficiency in either English, French or German;
  • The internship allowance is fixed by law at an amount of about 500 euros on a 38 hours basis.
  • Students registered at non-EU universities should also inquire first about the administrative issues related to the possibility of being hosted at the Centre, at a Germany-based institution.

Apply

To apply, please send an e-mail along with your resume to Camille Roth (roth[@]cmb.hu-berlin.de) and Jérémie Poiroux (poiroux[@]cmb.hu-berlin.de).