Accepted papers and schedule now posted!
This workshop aims to bring together a diverse and cross-disciplinary set of researchers to discuss recent advances and future directions for developing new network methods in statistics and machine learning. In particular, we are interested in
- network methods that learn the patterns of interaction, flow of information, or propagation of effects in social and information systems,
- empirical studies, particularly attempts to bridge observational methods and causal inference, and studies that combine learning, networks, and computational social science,
- research that unifies the study of both structure and content in rich network datasets.
While this research field is broad and diverse, there are emerging signs of convergence. For example, in the study of information diffusion, social media and social network researchers are beginning to use rigorous tools to distinguish effects driven by social influence, homophily, or external processes -- subjects historically of intense interest amongst statisticians and social scientists. Similarly, there is a growing statistics literature developing learning approaches to study topics popularized earlier within the physics community, including clustering in graphs, network evolution, and random-graph models. Finally, learning methods are increasingly used in highly complex application domains, such as large-scale knowledge graph construction and use, and massive social networks like Facebook and LinkedIn. These applications are stimulating new scientific and practical questions that often cut across disciplinary boundaries.
Workshop scheduleMorning Session
9:00 - 9:15 Opening Remarks
9:15 - 10:00 Invited talk, Jon Kleinberg, Cornell University
10:00 - 10:30 (Coffee break)
10:30 - 11:15 Poster spotlight session
11:15 - 12:30 Poster session
Lunch break (12:30-3:00pm)
3:00 - 3:45 Invited talk, Emily Fox, University of Washington
3:45 - 4:30 Invited talk, Hanna Wallach, Microsoft Research NYC
4:30 - 5:00 (Coffee break)
5:00 - 5:45 Invited talk, Deepak Agarwal, LinkedIn
5:45 - 6:00 Panel Discussion (with Deepak, Emily, Jon, and Hanna)
Poster presenters: please arrive 8:45-9:00 to set up your posters. Supplies for hanging posters will be provided.
Accepted papersBopeng Li, Sougata Chaudhuri, and Ambuj Tewari.
Handling Class Imbalance in Link Prediction using Learning to Rank Techniques
Justin Khim and Po-Ling Loh.
Confidence Sets for the Source of a Diffusion in Regular Trees
Victor Veitch and Daniel Roy.
The Class of Random Graphs Arising from Exchangeable Random Measures
Shandian Zhe, Pengyuan Wang, Kuang-Chih Lee, Zenglin Xu, Jian Yang, Youngja Park, and Yuan Qi.
Distributed Flexible Nonlinear Tensor Factorization for Large Scale Multiway Data Analysis
Joshua Blumenstock, Gabriel Cadamuro, and Robert On.
Predicting Poverty and Wealth from Mobile Phone Metadata
Argyris Kalogeratos, Kevin Scaman, and Nicolas Vayatis.
Learning to Suppress SIS Epidemics in Networks
Elisabeth Baseman and David Jensen.
Collaborative Behavior in Social Networks: A Relational Statistical Approach
Varun Gangal, Abhishek Narwekar, Balaraman Ravindran, and Ramasuri Narayanam.
Trust And Distrust Across Coalitions - Shapley Value Centrality Measures For Signed Networks
Tanmay Sinha, Wenjun Wang, and Xuechen Lei.
Teasing Apart Behavioral Protocols in Longitudinal Self-reported Friendship Networks
Tanmay Sinha, Wei Wei, and Kathleen Carley.
Modeling Similarity in Incentivized Interaction: A Longitudinal Case Study of StackOverFlow
Yali Wan and Marina Meila.
Benchmarking recovery theorems for the DC-SBM
James Atwood and Don Towsley.
Search-Convolutional Neural Networks
Guilllermo Santamaría and Vicenç Gomez.
Convex inference for community discovery in signed networks
Aldo Porco, Andreas Kaltenbrunner, and Vicenç Gomez.
Low-rank approximations for predicting voting behaviour
Kevin Carter, Rajmonda Caceres, and Benjamin Priest.
Characterization of Latent Social Networks Discovered through Computer Network Logs
Lin Li and William Campbell.
Matching Community Structure Across Online Social Networks
Sucheta Soundarajan, Tina Eliassi-Rad, Brian Gallagher, and Ali Pinar.
MaxOutProbe: An Algorithm for Increasing the Size of Partially Observed Networks
Sharan Vaswani, Laks V.S. Lakshmanan, and Mark Schmidt.
Influence Maximization with Bandits
Konstantina Palla, Francois Caron, and Yee Whye Teh.
A Bayesian nonparametric model for sparse dynamic networks
Tamara Broderick and Diana Cai.
Edge-exchangeable graphs and sparsity
Pau Perng-Hwa Kung and Deb Roy.
Measuring Responsiveness in the Online Public Sphere for the 2016 U.S. Election
Xiaojian Wu, Daniel Sheldon, and Shlomo Zilberstein.
Efficient Algorithms to Optimize Diffusion Processes under the Independent Cascade Model
Aaron Schein, Mingyuan Zhou, David Blei, and Hanna Wallach.
Modeling Topic-Partitioned Assortativity and Disassortativity in Dyadic Event Data (Best Student Poster Award)
Jack Hessel, Alexandra Schofield, Lillian Lee, and David Mimno.
What do Vegans do in their Spare Time? Latent Interest Detection in Multi-Community Networks
Diana Cai and Tamara Broderick.
Completely random measures for modeling power laws in sparse graphs
Yike Liu, Neil Shah, and Danai Koutra.
An Empirical Comparison of the Summarization Power of Graph Clustering Methods
Gintare Karolina Dziugaite, and Daniel Roy.
Neural Network Matrix Factorization
Chris Lloyd, Tom Gunter, Michael Osborne, and Stephen Roberts.
Inferring Dynamic Interaction Networks with N-LPPA
Charalampos Mavroforakis, Isabel Valera, and Manuel Gomez Rodriguez.
Hierarchical Dirichlet Hawkes Process for modeling the Dynamics of Online Learning Activity
Pinar Yanardag, and S.V.N. Vishwanathan.
A Submodular Framework for Graph Comparison
We welcome the following types of papers:
- Research papers that introduce new models or apply established models to novel domains,
- Research papers that explore theoretical and computational issues, or
- Position papers that discuss shortcomings and desiderata of current approaches, or propose new directions for future research.
Submissions will be lightly peer-reviewed. We encourage authors to emphasize the role of learning and its relevance to the application domains at hand. In addition, we hope to identify current successes in the area, and will therefore consider papers that apply previously proposed models to novel domains and data sets.
Submissions should be 4-to-8 pages long, and adhere to NIPS format (http://nips.cc/PaperInformation/StyleFiles). Please make the author information visible as in the final draft.
Please contribute your submissions through https://easychair.org/conferences/?conf=nipsnetworks2015
- Deadline for Submissions: Monday, November 2, 2015, 11:59pm PST
- Notification of Decision: Thursday, November 5, 2015
- Edo Airoldi, Harvard University
- David Choi, Carnegie Mellon University
- Aaron Clauset, University of Colorado, Boulder
- Panos Toulis, Harvard University
- Johan Ugander, Stanford University
Previous NIPS workshops:
- NIPS 2014: Networks: From Graphs to Rich Data
- NIPS 2013: Frontiers of Network Analysis: Methods, Models, and Applications
- NIPS 2012: Social Network and Social Media Analysis: Methods, Models and Applications
- NIPS 2010: Networks Across Disciplines: Theory and Applications
- NIPS 2009: Analyzing Networks and Learning with Graphs
You can reach the organizers at NIPSnetworks@gmail.com