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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)
How do company insiders trade? Do their trading behaviors differ based on their roles (e.g., chief executive officer vs. chief financial officer)? Do those behaviors change over time (e.g., impacted by the 2008 market crash)? Can we identify insiders who have similar trading behaviors? And what does that tell us? This work presents the first academic,(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)
BBN Technologies designed an algorithm called Swift Start for TCP to mitigate the start up problem. Some drawbacks face that algorithm. This paper introduces a modification to that algorithm then it will evaluate the modified swift start TCP congestion control algorithm analytically and by simulation and then it will compare between its performance with(More)
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model. The spread of fake news and mitigation events within the network is modeled by a multivariate Hawkes process with additional exogenous control terms. By choosing a feature(More)
Many combinatorial optimization problems over graphs are NP-hard, and require significant specialized knowledge and trial-and-error to design good heuristics or approximation algorithms. Can we automate this challenging and tedious process, and learn the algorithms instead? In many real world applications, it is typically the case that the same type of(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)
In this modern world, fashion & styles are changing frequently. The emergence of fast changes in fashion has given rise to shorten production cycle time in the garment industry. To meet the dynamic customer demands of momentous quantities in shorten lead time, assembly line production systems are used, where the garment components are assembled into a(More)