Learning user-specific latent influence and susceptibility from information cascades

Predicting cascade dynamics has important implications for understanding information propagation and launching viral marketing. Previous works mainly adopt a pair-wise manner, modeling the propagation probability between pairs of users using n^2 independent parameters for n users. Consequently, these models suffer from severe overfitting problem, specially for pairs of users without direct interactions, limiting their prediction accuracy. Here we propose to model the cascade dynamics by learning two low-dimensional user-specific vectors from observed cascades, capturing their influence and susceptibility respectively. This model requires much less parameters and thus could combat overfitting problem. Moreover, this model could naturally model context-dependent factors like cumulative effect in information propagation. Extensive experiments on synthetic dataset and a large-scale microblogging dataset demonstrate that this model outperforms the existing pair-wise models at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015

Stability of Influence Maximization

The present article serves as an erratum to our paper of the same title, which was presented and published in the KDD 2014 conference. In that article, we claimed falsely that the objective function defined in Section 1.4 is non-monotone submodular. We are deeply indebted to Debmalya Mandal, Jean Pouget-Abadie and Yaron Singer for bringing to our attention a counter-example to that claim. Subsequent to becoming aware of the counter-example, we have shown that the objective function is in fact NP-hard to approximate to within a factor of $O(n^{1-\epsilon})$ for any $\epsilon > 0$. In an attempt to fix the record, the present article combines the problem motivation, models, and experimental results sections from the original incorrect article with the new hardness result. We would like readers to only cite and use this version (which will remain an unpublished note) instead of the incorrect conference version.Comment: Erratum of Paper "Stability of Influence Maximization" which was presented and published in the KDD1

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)

The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability. We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios. We are not aware of any other formalism in the literature that meets all of the above requirements

Organic Hop Variety Trial: Results from Year Five

Hops production continues to increase throughout the the Northeast. While hops were historically grown in the Northeast, they have not been commercially produced in this region for over a hundred years. With the lack of regional production knowledge, a great need has been identified for region-specific, science-based research on this reemerging crop. The vast majority of hops production in the United States occurs in the arid Pacific Northwest on a very large scale. In the Northeast, the average hop yard is well under 10 acres and the humid climate provides challenges not addressed by the existing hops research. Knowledge is needed on how best to produce hops on a small-scale in our region. With this in mind, in August of 2010, the UVM Extension Northwest Crops and Soils Program initiated an organic hops variety evaluation program at Borderview Research Farm in Alburgh, Vermont. Since this time, UVM Extension has been evaluating 22 publicly-available hop varieties. The goals of these efforts are to find hop varieties that demonstrate disease and pest resistance, high yields, and present desirable characteristics to brewers in our region. Hops are a perennial crop – most varieties reach full cone production in year three. The following are the results from the fifth year of production

Benchmark of structured machine learning methods for microbial identification from mass-spectrometry data

Microbial identification is a central issue in microbiology, in particular in the fields of infectious diseases diagnosis and industrial quality control. The concept of species is tightly linked to the concept of biological and clinical classification where the proximity between species is generally measured in terms of evolutionary distances and/or clinical phenotypes. Surprisingly, the information provided by this well-known hierarchical structure is rarely used by machine learning-based automatic microbial identification systems. Structured machine learning methods were recently proposed for taking into account the structure embedded in a hierarchy and using it as additional a priori information, and could therefore allow to improve microbial identification systems. We test and compare several state-of-the-art machine learning methods for microbial identification on a new Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) dataset. We include in the benchmark standard and structured methods, that leverage the knowledge of the underlying hierarchical structure in the learning process. Our results show that although some methods perform better than others, structured methods do not consistently perform better than their "flat" counterparts. We postulate that this is partly due to the fact that standard methods already reach a high level of accuracy in this context, and that they mainly confuse species close to each other in the tree, a case where using the known hierarchy is not helpful

Organic Hop Variety Trial Final Report

Hops production has increased steadily throughout the Northeast over the past 6 years. While hops were historically grown in the Northeast, they have not been commercially produced in this region for over a hundred years. With this large gap in regional production knowledge, we have a great need for region-specific, science-based research on this reemerging crop. The vast majority of hop production in the United States occurs in the arid Pacific Northwest on a very large scale. In the Northeast, the average hop yard is well under 10 acres and the humid climate provides challenges not addressed by existing hops research. Knowledge is needed on how best to produce hops on a small-scale in our region. With this in mind, in August of 2010, the UVM Extension Northwest Crops and Soils Program initiated an organic hops variety evaluation program at Borderview Research Farm in Alburgh, Vermont. Since then, UVM Extension has been evaluating 22 publicly available hop varieties and 2 experimental varieties. The goal of these efforts is to find hop varieties that demonstrate disease and pest resistance, high yields, and desirable characteristics to brewers in our region. The UVM hop variety trial was initiated in 2010 and completed with a final harvest in 2016. This seven year trial helped us learn whether we could grow hops in the Northeast. The results and observations from each of the years the variety trial was conducted can be found online on the UVM Extension Northwest Crops and Soils Hops web page: www.uvm.edu/ extension/cropsoil/hops. This document provides a summary of the knowledge gained in growing hops over the duration of this study