Document retrieval has greatly benefited from the advancements of large-scale pre-trained language models (PLMs). However, their effectiveness is often limited in theme-specific applications for specialized areas or industries, due to unique terminologies, incomplete contexts of user queries, and specialized search intents. To capture the theme-specific information and improve retrieval, we propose to use a corpus topical taxonomy, which outlines the latent topic structure of the corpus while reflecting user-interested aspects. We introduce ToTER (Topical Taxonomy Enhanced Retrieval) framework, which identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers. Through extensive quantitative, ablative, and exploratory experiments on two real-world datasets, we ascertain the benefits of using topical taxonomy for retrieval in theme-specific applications and demonstrate the effectiveness of ToTER.

Modern organizations amass their datasets into centralized repositories called data lakes, affording analytics as needed. The resultant scale and complexity of these data lakes, however, can make data navigation and monitoring challenging for users. We present DataCockpit, a Python toolkit that leverages datasets, usage logs, and associated meta-data to provision data usage and quality characteristics. DataCockpit computes these characteristics for each attribute (e.g., number of times it was queried for subsequent use in downstream applications) and record (e.g., number of non-missing, valid values) and aggregates them at the level of datasets. We develop a visual monitoring tool, powered by DataCockpit, and demonstrate how it can assist data / system administrators as well as end-users to effectively navigate and monitor a data lake. DataCockpit and the monitoring tool are available as open source software for developers to build custom monitoring applications on top of data lakes.

Mixup is an efficient data augmentation technique, which improves generalization by interpolating random examples. While numerous approaches have been developed for Mixup in the Euclidean and in the hyperbolic space, they do not fully use the intrinsic properties of the examples, i.e., they manually set the geometry (Euclidean or hyperbolic) based on the overall dataset, which may be sub-optimal since each example may require a different geometry. We propose DynaMix, a framework that automatically selects an example-specific geometry and performs Mixup between the different geometries to improve training dynamics and generalization. Through extensive experiments in image and text modalities we show that DynaMix outperforms state-of-the-art methods over six downstream applications. We find that DynaMix is more useful in low-resource and semi-supervised settings likely because it displays a probabilistic view of the geometry.

Network-based time series forecasting is a challenging task as it involves complex geometric properties, higher-order relations, and scale-free characteristics. Previous work has modeled network-based series as oversimplified graphs or has ignored the power law dynamics of real-world temporal and dynamic networks, which could yield suboptimal results. With the aim to address these issues, here we propose THINK, a novel framework based on hypergraph learning that captures the hyperbolic properties of time-evolving dynamic hypergraphs. We design an elegant hyperbolic distance-aware hypergraph attention mechanism to better capture informative internal structural features on the Poincaré ball. Through quantitative and conceptual analysis on seven tasks across temporal, and time-evolving dynamic hypergraphs, we demonstrate THINK’s practicality in comparison to a variety of benchmarks spanning finance, health, and energy networks.

Social media enable users to share their feelings and emotional struggles. They also offer an opportunity to provide community support to suicidal users. Recent studies on suicide risk assessment have explored the user's historic timeline and information from their social network to analyze their emotional state. However, such methods often require a large amount of user-centric data. A less intrusive alternative is to only use conversation trees arising from online community responses. Modeling such online conversations between the community and a person in distress is an important context for understanding that person's mental state. However, it is not trivial to model the vast number of conversation trees on social media, since each comment has a diverse influence on a user in distress. Typically, a handful of comments/posts receive a significantly high number of replies, which results in scale-free dynamics in the conversation tree. Moreover, psychological studies suggested that it is important to capture the fine-grained temporal irregularities in the release of vast volumes of comments, since suicidal users react quickly to online community support. Building on these limitations and psychological studies, we propose HCN, a Hyperbolic Conversation Network, which is a less user-intrusive method for suicide ideation detection. HCN leverages the hyperbolic space to represent the scale-free dynamics of online conversations. Through extensive quantitative, qualitative, and ablative experiments on real-world Twitter data, we find that HCN outperforms state-of-the art methods, while using 98% less user-specific data, and while maintaining a 74% lower carbon footprint and a 94% smaller model size. We also find that the comments within the first half an hour are most important to identify at-risk users.

The rapidly rising ubiquity and dissemination of online information such as social media text and news improve user accessibility towards financial markets, however, modeling these vast streams of irregular, temporal data poses a challenge. Such temporal streams of information show power-law dynamics, scale-free characteristics, and time irregularities that sequential models are unable to accurately model. In this work, we propose the first Hierarchical Time-Aware Hyperbolic LSTM (HTLSTM), which leverages the Riemannian manifold for encoding the scale-free nature of a sequence of text in a time-aware fashion. Through experiments on three financial tasks: stock trading, equity price movement prediction, and financial risk prediction, we demonstrate HTLSTM's applicability for modeling temporal sequences of online information. On real-world data from four global stock markets and three stock indices spanning data in English and Chinese, we make a step towards time-aware text modeling via hyperbolic geometry.

Quantitative trading and investment decision making are intricate financial tasks in the ever-increasing sixty trillion dollars global stock market. Despite advances in stock forecasting, a limitation of most existing neural methods is that they treat stocks independent of each other, ignoring the valuable rich signals between related stocks’ movements. Motivated by financial literature that shows stock markets and inter-stock correlations show scale-free network characteristics, we leverage domain knowledge on the Web to model inter-stock relations as a graph in four major global stock markets and formulate stock selection as a scale-free graph-based learning to rank problem. To capture the scale-free spatial and temporal dependencies in stock prices, we propose HyperStockGAT: Hyperbolic Stock Graph Attention Network, the first model on the Riemannian Manifolds for stock selection. Our work’s key novelty is the proposal of modeling the complex, scale-free nature of inter-stock relations through temporal hyperbolic graph learning on Riemannian manifolds that can represent the spatial correlations between stocks more accurately. Through extensive experiments on long-term real-world data spanning over six years on four of the world’s biggest markets: NASDAQ, NYSE, TSE, and China exchanges, we show that HyperStockGAT significantly outperforms state-of-the-art stock forecasting methods in terms of profitability by over 12%, and risk-adjusted Sharpe Ratio by over 4%. We analyze HyperStockGAT’s components’ contributions through a series of exploratory and ablative experiments to demonstrate its practical applicability to real-world trading. Furthermore, we propose a novel hyperbolic architecture that can be applied across various spatiotemporal problems on the Web’s commonly occurring scale-free networks.

Stock movement prediction, a widely addressed research avenue in the world of computer science and finance, it finds fundamental applications in quantitative trading and investment decision making. Predicting future trends in stock prices is a complex problem, fundamentally due to the highly stochastic and dynamic nature of the market. Advances in neural stock forecasting through deep learning models have made improvements in stock movement prediction. However, a majority of existing research treats stocks independent of each other or simplifies the complex higher-order relations between stocks in a pairwise fashion through graphs. Another limitation of recent graph-based approaches for stock movement prediction is the lack of time-aware modeling of the temporal evolution of stock prices jointly while modeling inter stock relations. To this end, we propose STHGCN: Spatio-Temporal Hypergraph Convolution Network, the first neural hypergraph model for stock trend forecasting. At the core of STHGCN, we devise a gated temporal convolution over hypergraphs for learning stock price evolution over stock relations in a time-aware manner. STHGCN significantly outperforms state-of-the-art stock forecasting methods over extensive experiments on long term realworld S&P500 index data of stocks traded in the NASDAQ and NYSE markets over 12 diverse phases. We highlight STHGCN's practical applicability through a market simulation and a latency analysis with competitive models. Furthermore, we propose a novel architecture for stock trend forecasting that can be applied across various problems in the spatiotemporal domain.