DeepInf: Social Influence Prediction with Deep Learning

Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn influenced by them. Consequently, an effective social influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional social influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting social influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for social applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding

Cost-efficient vaccination protocols for network epidemiology

We investigate methods to vaccinate contact networks -- i.e. removing nodes in such a way that disease spreading is hindered as much as possible -- with respect to their cost-efficiency. Any real implementation of such protocols would come with costs related both to the vaccination itself, and gathering of information about the network. Disregarding this, we argue, would lead to erroneous evaluation of vaccination protocols. We use the susceptible-infected-recovered model -- the generic model for diseases making patients immune upon recovery -- as our disease-spreading scenario, and analyze outbreaks on both empirical and model networks. For different relative costs, different protocols dominate. For high vaccination costs and low costs of gathering information, the so-called acquaintance vaccination is the most cost efficient. For other parameter values, protocols designed for query-efficient identification of the network's largest degrees are most efficient

Coordinating Collaboration to End Homelessness: A Mid-Point Learning Assessment of the Reaching Home Campaign and Opening Doors, Connecticut

In Connecticut, the Partnership for Strong Communities (PSC) and a group of advocacy organizations, government agencies, and community providers are leading a campaign to end homelessness in the state. Guided by the vision that "No one should experience homelessness," the Reaching Home Campaign and Opening Doors—Connecticut (the "Campaign") emphasizes housing as an essential platform for human and community development. The Campaign brings together a broad spectrum of partners representing diverse sectors to collectively build the political and civic will to end homelessness. In just three years, the Campaign has already achieved remarkable success advocating for and securing over $300 million in funding for programs to end homelessness and to create permanent supportive and affordable housing. Among its many accomplishments, the Campaign conducted the state's first study of youth experiencing homelessness and released the Opening Doors for Youth plan to end youth homelessness. The Campaign is also closing in on the goal of ending homelessness among Veterans, as well as launching a pilot program to connect families receiving rapid rehousing with employment supports and implementing a successful pilot that identifies and connects frequent users of emergency departments at hospitals to housing and supportive services. To support the Campaign's work at this important juncture as it moves past planning and towards implementation and sustainability, the Melville Charitable Trust—a private foundation and longtime partner of the effort—approached The Building Movement Project (BMP) to conduct a mid-point learning assessment. One goal of the assessment was to help the Campaign take stock of its internal structures and processes. Another goal was to share insights on what it means to coordinate collaboration, given the growing use of "collective impact" as a strategy to address social problems

A Comparative Analysis of Influenza Vaccination Programs

The threat of avian influenza and the 2004-2005 influenza vaccine supply shortage in the United States has sparked a debate about optimal vaccination strategies to reduce the burden of morbidity and mortality caused by the influenza virus. We present a comparative analysis of two classes of suggested vaccination strategies: mortality-based strategies that target high risk populations and morbidity-based that target high prevalence populations. Applying the methods of contact network epidemiology to a model of disease transmission in a large urban population, we evaluate the efficacy of these strategies across a wide range of viral transmission rates and for two different age-specific mortality distributions. We find that the optimal strategy depends critically on the viral transmission level (reproductive rate) of the virus: morbidity-based strategies outperform mortality-based strategies for moderately transmissible strains, while the reverse is true for highly transmissible strains. These results hold for a range of mortality rates reported for prior influenza epidemics and pandemics. Furthermore, we show that vaccination delays and multiple introductions of disease into the community have a more detrimental impact on morbidity-based strategies than mortality-based strategies. If public health officials have reasonable estimates of the viral transmission rate and the frequency of new introductions into the community prior to an outbreak, then these methods can guide the design of optimal vaccination priorities. When such information is unreliable or not available, as is often the case, this study recommends mortality-based vaccination priorities

Controllability of Social Networks and the Strategic Use of Random Information

This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network, and considers two well-known strategies for influencing social contexts. One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad-hoc metrics defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills, supporting our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable

Use of a Bayesian belief network to predict the impacts of commercializing non-timber forest products on livelihoods

Commercialization of non-timber forest products (NTFPs) has been widely promoted as a means of sustainably developing tropical forest resources, in a way that promotes forest conservation while supporting rural livelihoods. However, in practice, NTFP commercialization has often failed to deliver the expected benefits. Progress in analyzing the causes of such failure has been hindered by the lack of a suitable framework for the analysis of NTFP case studies, and by the lack of predictive theory. We address these needs by developing a probabilistic model based on a livelihood framework, enabling the impact of NTFP commercialization on livelihoods to be predicted. The framework considers five types of capital asset needed to support livelihoods: natural, human, social, physical, and financial. Commercialization of NTFPs is represented in the model as the conversion of one form of capital asset into another, which is influenced by a variety of socio-economic, environmental, and political factors. Impacts on livelihoods are determined by the availability of the five types of assets following commercialization. The model, implemented as a Bayesian Belief Network, was tested using data from participatory research into 19 NTFP case studies undertaken in Mexico and Bolivia. The model provides a novel tool for diagnosing the causes of success and failure in NTFP commercialization, and can be used to explore the potential impacts of policy options and other interventions on livelihoods. The potential value of this approach for the development of NTFP theory is discussed

Assessing the geographic dimensions of London's innovation networks

A wide range of authors have highlighted the potential benefits for innovation that may arise from effective networking between organisations along and across the supply-chain. As many organisations have downsized or out-sourced basic research activities Universities have an increasingly important role within such networks. A number of UK initiatives have been established to encourage greater 'entanglement' between academia and commerce; the London Technology Network is one example which is intended to encourage interactions between London's leading research institutes and innovation organisations. Using the detailed data acquired by this network this development paper is intended to investigate the geographic distribution of these activities with the aim of establishing the extent to which location and/or distance play a significant role in participation in the network's activities. A wide range of authors have highlighted the potential benefits for innovation that may arise from effective networking between organisations along and across the supply-chain. As many organisations have downsized or out-sourced basic research activities Universities have an increasingly important role within such networks. A number of UK initiatives have been established to encourage greater 'entanglement' between academia and commerce; the London Technology Network is one example which is intended to encourage interactions between London's leading research institutes and innovation organisations. Using the detailed data acquired by this network this development paper is intended to investigate the geographic distribution of these activities with the aim of establishing the extent to which location and/or distance play a significant role in participation in the network's activities