WelcomeThe Statistical Inference for Network Models symposium is a satellite of the NetSci 2023 conference. SINM2023 will be held onsite on July 11, 2023 as a fully-day event in Vienna, Austria. SINM will feature a mix of invited and contributed talks. All attendees of this symposium must be registered for the NetSci 2023 conference to attend.
Call for AbstractsThe deadline for abstract submission is May 15, 2023, and acceptance notifications will be sent by May 22nd. There will be no published proceedings for this satellite. Submit your abstract by email at email@example.com, and see the 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
- Causal inference with networks
- Community structure, hierarchical structure, block modeling
- Ranking and linear hierarchy in networks
- Network regression
- Network clustering
- Model selection, comparison, and validation
- Statistical computing for network problems
- Detectability limits for statistical network models
- Network comparison
- Prediction and anomaly detection
- Network representation learning and network embeddings
- Bayesian nonparametrics for networks
- Graphon estimation
- Interfaces with spectral methods for networks
- Model-based knowledge discovery
- New domains of application
- New models for applied problems
Extended Abstract Instructions
- 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.
- The deadline is May 15th, 2023.
This satellite focuses on the development and application of statistical inference tools for networks. It addresses intersecting trends, whereby network data, hypotheses about network structure, and the processes that create those patterns are increasingly sophisticated. Statistical inference for network models offers a principled and effective approach for understanding and testing such richly annotated network data and network hypotheses.
This 10th anniversary of the SINM satellite symposium will be a unique occasion to reflect on the progress and achievements that have been made in the field of statistical inference in network science over the past decade. The development of new tools and their application for understanding real systems is now a major community effort in network science. Increasingly, new applications and richer data sets offer new opportunities for developing and applying the principled techniques of statistical inference to networks. In this spirit, SINMaims brings together theoretical and applied researchers from across network science, including machine learning, statistics, social network analysis, and physics. This broad cross-section of disciplines shares problems and even approaches, but each discipline brings a different perspective, emphasis, and vocabulary.
This satellite symposium will build on nine successful previous satellites at NetSci2014, NetSci2015, NetSci2016, NetSci2017, NetSci2018, NetSci2019, NetSci2020, Networks 2021, and NetSci 2022 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.
- Leto Peel, Maastricht University
- Marion Hoffman, Institute For Advanced Study in Toulouse: IAST
- Kiran Tomlinson, Cornell
- Vince Lyzinski, University of Maryland
- Marianna Pensky, Univerity of Florida