WelcomeThe Statistical Inference for Network Models symposium is a satellite of the NetSci conference, which has moved online due to COVID-19. SINM2020 will be held online on Sept 20, 2020. SINM will feature a mix of invited and contributed talks. You can view on the invited speakers who have confirmed so far. All attendees of this symposium must be registered for the NetSci2020 conference in order to attend. Please note the important dates and deadlines:
Call for AbstractsSubmit here but please see requirements below. Any abstract submitted between now and the next call for abstracts will be given full consideration. We invite abstracts of new and/or previously published work for contributed talks to take place at the symposium. We hope for a broad range of topics to be covered, across theory, methodology, and application to empirical network data. Potential topics include:
- Generative models for network structure
- Community structure, hierarchical structure, block modeling
- Model selection, comparison, and validation
- Efficient algorithms
- Intersections between statistical physics and machine learning
- Detectability limits
- Network comparison
- Prediction and anomaly detection
- Statistical relational learning
- Bayesian nonparametrics
- Graphon estimation
- Interfaces with spectral methods
- Social networks and social media
- Biological networks
- Model-based knowledge discovery
- New domains of application
- New models for applied problems
Extended Abstract InstructionsAbstract submission will be double blind and handled by EasyChair. Guidelines:
- Extended abstract format. Example from 2018.
- 1-page maximum, PDF format.
- PDF may contain a figure.
- Please remove all author names and affiliations from the abstract. Reviews will be double blind.
- Abstract deadline is July 15, 2020.
This workshop will address the intersection of two trends in network science. On the one hand, real-world networks are increasingly annotated with rich metadata, including vertex or edge attributes or temporal information. Making sense of such data requires moving beyond simple models of network structure. On the other, hypotheses about network structure and the processes that create those patterns are increasingly sophisticated. The tools of statistical inference for network models offer a principled and effective approach for both understanding richly annotated network data and testing interesting network hypotheses.
In particular, probabilistic models are a quantitative approach that allow researchers both to infer complicated hidden structural patterns in existing data and generate synthetic data sets whose structure is statistically similar to real data. These models facilitate handling many of the challenges of understanding real data, including accounting for noise and missing values, and they connect theory with data by providing interpretable results. Statistical inference is thus a powerful and useful tool for modeling and understanding networks.
The development of new tools and their application to understand real systems is now a major community effort in network science. Despite their power and utility, however, these techniques are not as easy or approachable as simpler tools, like degree distributions, centrality scores, and clustering coefficients. Increasingly, new applications and richer data sets offer new opportunities for developing and applying the principled techniques of statistical inference to networks.
This satellite symposium will build on six successful previous satellites at NetSci2014, NetSci2015, NetSci2016, NetSci2017, NetSci2018, and NetSci2019 by uniting theoretical and applied researchers, bringing together approaches from across network science, including machine learning, statistics, and physics. This broad cross-section of disciplines shares problems and even approaches, but each discipline brings a different perspective, emphasis and vocabulary. The purpose of this symposium is to provide a platform for cross-pollination of ideas and promote interdisciplinary interactions which leverage the diversity of approaches to a common set of problems.
There will be no published proceedings for this satellite.
- Danai Koutra, Michigan
- David Gleich, Purdue
- Yves-Alexandre de Montjoye, Imperial
- Rose Yu, UC San Diego
- Cris Moore, Santa Fe Institute