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Events for May 17, 2024
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PhD Defense- Hongkuan Zhou
Fri, May 17, 2024 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Defense- Hongkuan Zhou
Title: Scaling up Temporal Graph Learning: Powerful Models, Efficient Algorithms, and Optimized Systems
Committee Members: Prof. Keith Michael Chugg, Prof. Rajgopal Kannan, Prof Viktor K. Prasanna (Chair), Prof. Mukund Raghothaman
Abstract: Recently, Temporal Graph Neural Networks (TGNNs) have extended the scope of Graph Representation Learning (GRL) to dynamic graphs. TGNNs generate high-quality and versatile dynamic node embeddings by simultaneously encoding the graph structures, node and edge contexts, and their temporal dependencies. TGNNs are shown to demonstrably outperform traditional dynamic graph analytic algorithms in impactful applications that address critical real-world challenges, such as social network analysis, healthcare applications, and traffic prediction and management. However, due to the challenges of the prevalent noise in real-world data, irregular memory accesses, complex temporal dependencies, and high computation complexity, current TGNNs face the following problems when scaling to large dynamic graphs: (1) Unpowerful models. Current TGNN models struggle to capture high-frequency information and handle the diverse and dynamic noise. (2) Ineffcieint algorithms. Current training algorithms cannot leverage the massive parallel processing architecture of modern hardware, while current inference algorithms cannot meet the requirements in different scenarios. And (3) Unoptimized systems. Current TGNN systems suffer from inefficient designs that hinder overall performance. In this dissertation, we address the above issues via model-algorithm-system co-design. For model improvements, we propose a static node-memory-enhanced TGNN model and a temporal adaptive sampling technique. For algorithm improvements, we propose a scalable distributed training algorithm with heuristic guidelines to achieve the optimal configuration, and a versatile inference algorithm. For system improvements, we propose techniques of dynamic feature caching, simplified temporal attention, etc., to compose optimized training and inference systems. We demonstrate significant improvements in accuracy, training time, inference latency, and throughput compared with state-of-the-art TGNN solutions.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Hongkuan Zhou
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PhD Dissertation Defense - Binh Vu
Fri, May 17, 2024 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Exploiting Web Tables and Knowledge Graphs for Creating Semantic Descriptions of Data Sources
Committee: Craig Knoblock (Chair), Sven Koenig, Daniel Edmund O'Leary, Yolanda Gil, Jay Pujara
Date and Time: Friday, May 17th - 3:00p - 5:00p
Location: SAL 322
Abstract: There is an enormous number of tables available on the web, and they can provide valuable information for diverse applications. To harvest information from the tables, we need precise mappings, called semantic descriptions, of concepts and relationships in the data to classes and properties in a target ontology. However, creating semantic descriptions, or semantic modeling, is a complex task requiring considerable manual effort and expertise. Much research has focused on automating this problem. However, existing supervised and unsupervised approaches both face various difficulties. The supervised approaches require lots of known semantic descriptions for training and, thus, are hard to apply to a new or large domain ontology. On the other hand, the unsupervised approaches exploit the overlapping data between tables and knowledge graphs; hence, they perform poorly on tables with lots of ambiguity or little overlapping data. To address the aforementioned weaknesses, we present novel approaches for two main cases: tables that have overlapping data with a knowledge graph (KG) and tables that do not have overlapping data. Exploiting web tables that have links to entities in a KG, we automatically create a labeled dataset to learn to combine table data, metadata, and overlapping background knowledge (if available) to find accurate semantic descriptions. Our methods for the two cases together provide a comprehensive solution to the semantic modeling problem. In the evaluation, our approach in the overlapping setting yields an improvement of approximately 5\% in F$_1$ scores compared to the state-of-the-art methods. In the non-overlapping setting, our approach outperforms strong baselines by 10\% to 30\% in F$_1$ scores.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Felante' Charlemagne
