LATTE: Application Oriented Social Network Embedding

In recent years, many research works propose to embed the network structured data into a low-dimensional feature space, where each node is represented as a feature vector. However, due to the detachment of embedding process with external tasks, the learned embedding results by most existing embedding models can be ineffective for application tasks with specific objectives, e.g., community detection or information diffusion. In this paper, we propose study the application oriented heterogeneous social network embedding problem. Significantly different from the existing works, besides the network structure preservation, the problem should also incorporate the objectives of external applications in the objective function. To resolve the problem, in this paper, we propose a novel network embedding framework, namely the "appLicAtion orienTed neTwork Embedding" (Latte) model. In Latte, the heterogeneous network structure can be applied to compute the node "diffusive proximity" scores, which capture both local and global network structures. Based on these computed scores, Latte learns the network representation feature vectors by extending the autoencoder model model to the heterogeneous network scenario, which can also effectively unite the objectives of network embedding and external application tasks. Extensive experiments have been done on real-world heterogeneous social network datasets, and the experimental results have demonstrated the outstanding performance of Latte in learning the representation vectors for specific application tasks.Comment: 11 Pages, 12 Figures, 1 Tabl

Land Acquisiton For Flood Adaptation Under Changing Climate

Flood has caused tremendous life and economic losses over the past few decades and is expected to cause more in the future under the effects of climate change and socioeconomic development. With limited resources, cost-effective adaptation policies are urgently needed to minimize the economic and social impacts of future floods. The increasing flood losses and recent catastrophic flood events around the world expose two major issues of current flood adaptation schemes: 1) Flood risks are not well captured and predicted under climate change, leaving many communities unprepared for potential extreme flood events; 2) Current flood adaptation measures focus on the reduction of economic losses that mainly consists of property damages, and do not give adequate consideration to the social impacts in equity and human risks. Focusing on the United States, this research aims to fill in the research gaps of the two flood adaptation issues mentioned above. First, an examination of observed snowfall trends from 1961 to 2017 in central North America shows that snowfall has increased in low-temperature areas in the northern part of the plains and high-elevation areas in the mountain regions, indicating that the interplay of climate change and topography can lead to increased flood risks in cold regions that should not be underestimated. Then, land acquisition, an emerging flood adaptation measure with high economic potential, is investigated for its economic and social performance under future flood risks for both itself and when combined with flood insurance. As a result, this research shows acquisition can be guided towards the low-income and highly populated areas to benefit the poorer populations, and can be further shifted towards areas with high flood insurance costs to reduce the total societal cost and the poorer homeowners’ cost of flood recovery. Together, these findings provide valuable implications on how flood adaptation approaches can be cost-effectively designed to tackle both economic and social challenges of future flood risks under changing climate