Workshops

Fall 2013

Unifying Theory and Experiment for Large-Scale Networks

Nov 18, 2013 to Nov 21, 2013

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Click on the titles of individual talks for abstract, slides and archived video.

All events take place in the Calvin Lab Auditorium.

Monday, November 18th, 2013
8:30 am–8:50 am		Coffee and Check-In
8:50 am–9:00 am		Opening Remarks
9:00 am–9:10 am		Overview: Graph Models Michael Kearns, University of Pennsylvania
9:10 am–9:35 am		Limiting Behavior in Large Networks Patrick Wolfe, University College London
9:35 am–10:00 am		Practice is Better than Theory: Using Approximate Counting to Mine Large Graphs Sebastiano Vigna, University of Milan
10:00 am–10:30 am		Break
10:30 am–10:55 am		Bayesian Models for Graph Data and the Open Problem of Invariance in Networks Peter Orbanz, Columbia University
10:55 am–11:20 am		Know Your Epidemic: But How? Matt Salganik, Princeton University
11:20 am–12:00 pm		Discussion
12:00 pm–1:30 pm		Lunch Break
1:30 pm–1:40 pm		Overview: Clustering Jennifer Neville, Purdue University
1:40 pm–2:05 pm		Asymptotic Analysis of Unlabelled Networks Peter Bickel, UC Berkeley
2:05 pm–2:30 pm		Finding Small Structures in Large Datasets Andrea Montanari, Stanford University
2:30 pm–3:00 pm		Break
3:00 pm–3:25 pm		Local Clustering and the Blessing of Transitivity Karl Rohe, University of Wisconsin-Madison
3:25 pm–3:50 pm		Large-scale Graph Clustering and Message-passing-based Distributed Framework Vahab Mirrokni, Google
3:50 pm–4:30 pm		Discussion

Tuesday, November 19th, 2013
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:10 am		Overview: Network Formation Ashish Goel, Stanford University
9:10 am–9:35 am		Challenges of Modeling Network Formation in Tractable Ways Arun Chandrasekhar, Stanford University
9:35 am–10:00 am		Relating Developmental Transcription Factors (TFs) Based on Fruitfly Embryoic Images Bin Yu, UC Berkeley
10:00 am–10:30 am		Break
10:30 am–10:55 am		Issues in Developing Estimable Models of Strategic Network Formation Matt Jackson, Stanford University
10:55 am–11:20 am		Behavioral Experiments in a Network Formation Game Michael Kearns, University of Pennsylvania
11:20 am–12:00 pm		Discussion
12:00 pm–2:00 pm		Lunch Break
2:00 pm–2:10 pm		Overview: Causality/Experiments Matt Jackson, Stanford University
2:10 pm–2:35 pm		Are Observational Studies of Social Contagion Doomed? Cosma Shalizi, Carnegie Mellon University
2:35 pm–3:00 pm		On Sampling in Dynamic Networks for Inference, Experiments, and Interventions Steve Thompson, Simon Fraser University
3:00 pm–3:30 pm		Break
3:30 pm–3:55 pm		The Effect of Social Media on News Consumption Markus Mobius, Microsoft Research New England
3:55 pm–4:30 pm		Discussion
4:30 pm–5:30 pm		Reception

Wednesday, November 20th, 2013
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:10 am		Overview: Label Prediction/Diffusion Michael Kearns, University of Pennsylvania
9:10 am–9:35 am		Semi-supervised Learning on Graphs, Using Observed Correlation Structure Art Owen, Stanford University
9:35 am–10:00 am		How Predictable are Online Cascades Lada Adamic, Facebook
10:00 am–10:30 am		Break
10:30 am–10:55 am		The Impact of Network Structure on Relational Learning and Inference Jennifer Neville, Purdue University
10:55 am–11:20 am		The Emergence of Convention: An Experimental Study of Social Order Damon Centola, Massachusetts Institute of Technology
11:20 am–12:00 pm		Discussion
12:00 pm–1:30 pm		Lunch Break
1:30 pm–1:40 pm		Overview: Networks Over Time Patrick Wolfe, University College London
1:40 pm–2:05 pm		Estimating Time-Varying Networks Mladen Kolar, Carnegie Mellon University
2:05 pm–2:30 pm		Pre-Processing of Dynamic Networks: Its Impact on Analytics Sofus Macskássy, Facebook
2:30 pm–3:00 pm		Break
3:00 pm–3:25 pm		Graph Streams and Sketches: What Can and Can't Be Done Andrew McGregor, University of Massachusetts Amherst
3:25 pm–3:50 pm		The Trials and Tribulations of Tractably Counting Triangles C. Seshadhri, Sandia National Laboratories
3:50 pm–4:30 pm		Discussion

Thursday, November 21st, 2013
8:30 am–9:00 am		Coffee and Check-In
9:00 am–9:10 am		Overview: Scalability/Practical Challenges Deepak Agarwal, LinkedIn
9:10 am–9:35 am		Graph Algorithms for Modern Data Models Ashish Goel, Stanford University
9:35 am–10:00 am		On Unifying Asymptotic Complexity with Real-world Performance in Matrix-based Network Computations David Gleich, Purdue University
10:00 am–10:30 am		Break
10:30 am–10:55 am		Evolving Graphs Ravi Kumar, Google
10:55 am–11:20 am		Theoretical and Practical Challenges of Recommending Content to Users in a Social Network Deepak Agarwal, LinkedIn
11:20 am–12:00 pm		Discussion