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How can we optimize the topology of a networked system to bring a flu under control, propel a video to popularity, or stifle a network malware in its infancy? Previous work on information diffusion has focused on modeling the diffusion dynamics and selecting nodes to maximize/minimize influence. Only a paucity of recent studies have attempted to address the(More)
The design of strategies for branching in Mixed Integer Programming (MIP) is guided by cycles of parameter tuning and offline experimentation on an extremely heterogeneous testbed, using the average performance. Once devised, these strategies (and their parameter settings) are essentially input-agnostic. To address these issues, we propose a machine(More)
Mixed Integer Programs (MIP) are solved exactly by tree-based branch-and-bound search. However, various components of the algorithm involve making decisions that are currently addressed heuris-tically. Instead, I propose to use machine learning (ML) approaches such as supervised ranking and multi-armed bandits to make better-informed, input-specific(More)
We study the problem of determining the proper aggrega-tion granularity for a stream of time-stamped edges. Such streams are used to build time-evolving networks, which are subsequently used to study topics such as network growth. Currently, aggregation lengths are chosen arbitrarily, based on intuition or convenience. We describe ADAGE, which detects the(More)
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