Universality of neural dynamics on complex networks

This paper discusses the capacity of graph neural networks to learn the functional form of ordinary differential equations that govern dynamics on complex networks. We propose necessary elements for such a problem, namely, inductive biases, a neural network architecture and a learning task. Statistical learning theory suggests that generalisation power of neural networks relies on independence and identical distribution (i.i.d.)\ of training and testing data. Although this assumption together with an appropriate neural architecture and a learning mechanism is sufficient for accurate out-of-sample predictions of dynamics such as, e.g.\ mass-action kinetics, by studying the out-of-distribution generalisation in the case of diffusion dynamics, we find that the neural network model: (i) has a generalisation capacity that depends on the first moment of the initial value data distribution; (ii) learns the non-dissipative nature of dynamics implicitly; and (iii) the model's accuracy resolution limit is of order $\mathcal{O}(1/\sqrt{n})$ for a system of size $n$.

Topic Community Based Temporal Expertise for Question Routing

Question Routing in Community-based Question Answering websites aims at recommending newly posted questions to potential users who are most likely to provide "accepted answers". Most of the existing approaches predict users' expertise based on their past question answering behavior and the content of new questions. However, these approaches suffer from challenges in three aspects: 1) sparsity of users' past records results in lack of personalized recommendation that at times does not match users' interest or domain expertise, 2) modeling based on all questions and answers content makes periodic updates computationally expensive, and 3) while CQA sites are highly dynamic, they are mostly considered as static. This paper proposes a novel approach to QR that addresses the above challenges. It is based on dynamic modeling of users' activity on topic communities. Experimental results on three real-world datasets demonstrate that the proposed model significantly outperforms competitive baseline models

Ask "Who", Not "What": Bitcoin Volatility Forecasting with Twitter Data

Understanding the variations in trading price (volatility), and its response to external information is a well-studied topic in finance. In this study, we focus on volatility predictions for a relatively new asset class of cryptocurrencies (in particular, Bitcoin) using deep learning representations of public social media data from Twitter. For the field work, we extracted semantic information and user interaction statistics from over 30 million Bitcoin-related tweets, in conjunction with 15-minute intraday price data over a 144-day horizon. Using this data, we built several deep learning architectures that utilized a combination of the gathered information. For all architectures, we conducted ablation studies to assess the influence of each component and feature set in our model. We found statistical evidences for the hypotheses that: (i) temporal convolutional networks perform significantly better than both autoregressive and other deep learning-based models in the literature, and (ii) the tweet author meta-information, even detached from the tweet itself, is a better predictor than the semantic content and tweet volume statistics.