Real Estate Pricing (Appraisal) seeks to estimate the “true” value of a house in the current marketplace. This task presents several distinct challenges that makes the development of machine learning solutions a non-trivial task. First, unlike for several other asset classes, the notion of an intrinsic value for a house is vague, producing unique challenges for interpretability. Second, the real estate market is not as efficient or as liquid as other asset classes (stocks, bonds, etc.). So, the available data is often sparse, with highly irregular temporal and spatial latency. Finally, real estate prices are highly dependent on both temporal and geographic changes (when and where). In this paper, to address these challenges, we propose a novel Transformers Graph Neural Net (T-GNN) framework. Specifically, we provide an end- to-end, data/preprocessing agnostic solution with T -GNN that leverages an intuitive representation of real estate transactions as a graph. (1) We find that the T -GNN significantly boosts the performance (400/0-50%) across all metrics compared to tabular- based, non-GNN solutions (e.g., XGBoost). (2) We propose and evaluate a novel interpretability scheme to generate a list of comparable homes for each house valuation, improving on the baseline method by 50%. (3) We investigate why T -GNN significantly outperforms the respective benchmarks, attributing the superior performance to a better representational understanding of time and space.

A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.

A widely used measure of scientific impact is citations. However, due to their heavy-tailed distribution, citations are fundamentally difficult to predict. Instead, to characterize scientific impact, we address two analogous questions asked by many scientific researchers: “How will myh-index evolve over time, and which of my previously or newly published papers will contribute to it?” To answer these questions, we perform two related tasks. First, we develop a model to predict authors’ futureh-indices based on their current scientific impact. Second, we examine the factors that drive papers—either previously or newly published—to increase their authors’ predicted futureh-indices. By leveraging relevant factors, we can predict an author'sh-index in five years with an$R^2$value of 0.92 and whether a previously (newly) published paper will contribute to this futureh-index with an$F_1$score of 0.99 (0.77). We find that topical authority and publication venue are crucial to these effective predictions, while topic popularity is surprisingly inconsequential. Further, we develop an online tool that allows users to generate informedh-index predictions. Our work demonstrates the predictability of scientific impact, and can help researchers to effectively leverage their scholarly position of “standing on the shoulders of giants.”

Collaboration is an integral element of the scientific process that often leads to findings with significant impact. While extensive efforts have been devoted to quantifying and predicting research impact, the question of how collaborative behavior influences scientific impact remains unaddressed. In this work, we study the interplay between scientists' collaboration signatures and their scientific impact. As the basis of our study, we employ an ArnetMiner dataset with more than 1.7 million authors and 2 million papers spanning over 60 years. We formally define a scientist's collaboration signature as the distribution of collaboration strengths with each collaborator in his or her academic ego network, which is quantified by four measures: sociability, dependence, diversity, and self-collaboration. We then demonstrate that the collaboration signature allows us to effectively distinguish between researchers with dissimilar levels of scientific impact. We also discover that, even from the early stages of one's researcher career, a scientist's collaboration signature can help to reveal his or her future scientific impact. Finally, we find that as a representative group of outstanding computer scientists, Turing Award winners collectively produce distinctive collaboration signatures throughout the entirety of their careers. Our conclusions on the relationship between collaboration signatures and scientific impact give rise to important implications for researchers who wish to expand their scientific impact and more effectively stand on the shoulders of "collaborators."

Scientific impact plays a central role in the evaluation of the output of scholars, departments, and institutions. A widely used measure of scientific impact is citations, with a growing body of literature focused on predicting the number of citations obtained by any given publication. The effectiveness of such predictions, however, is fundamentally limited by the power-law distribution of citations, whereby publications with few citations are extremely common and publications with many citations are relatively rare. Given this limitation, in this work we instead address a related question asked by many academic researchers in the course of writing a paper, namely: "Will this paper increase my h-index?" Using a real academic dataset with over 1.7 million authors, 2 million papers, and 8 million citation relationships from the premier online academic service ArnetMiner, we formalize a novel scientific impact prediction problem to examine several factors that can drive a paper to increase the primary author's h-index. We find that the researcher's authority on the publication topic and the venue in which the paper is published are crucial factors to the increase of the primary author's h-index, while the topic popularity and the co-authors' h-indices are of surprisingly little relevance. By leveraging relevant factors, we find a greater than 87.5% potential predictability for whether a paper will contribute to an author's h-index within five years. As a further experiment, we generate a self-prediction for this paper, estimating that there is a 76% probability that it will contribute to the h-index of the co-author with the highest current h-index in five years. We conclude that our findings on the quantification of scientific impact can help researchers to expand their influence and more effectively leverage their position of "standing on the shoulders of giants."

Hellinger Distance Decision Trees [10] (HDDT) has been previously used for static datasets with skewed distributions. In unbalanced data streams, state-of-the-art techniques use instance propagation and standard decision trees (e.g. C4.5 [27]) to cope with the unbalanced problem. However it is not always possible to revisit/store old instances of a stream. In this paper we show how HDDT can be successfully applied in unbalanced and evolving stream data. Using HDDT allows us to remove instance propagations between batches with several benefits: i) improved predictive accuracy ii) speed iii) single-pass through the data. We use a Hellinger weighted ensemble of HDDTs to combat concept drift and increase accuracy of single classifiers. We test our framework on several streaming datasets with unbalanced classes and concept drift.