Ruozhou Yu

Rui Xue, Xipeng Shen, Ruozhou Yu et al.

Learning from Text-Attributed Graphs (TAGs) has attracted significant attention due to its wide range of real-world applications. The rapid evolution of language models (LMs) has revolutionized the way we process textual data, which indicates a strong potential to replace shallow text embedding generally used in Graph Neural Networks (GNNs). However, we find that existing LM approaches that exploit text information in graphs suffer from inferior computation and data efficiency. In this study, we introduce LEADING, a novel and efficient approach for end-to-end fine-tuning of language models on TAGs. To enhance data efficiency, LEADING efficiently transfers rich knowledge from LMs to downstream graph learning tasks with limited labeled data by employing end-to-end training of LMs and GNNs in a semi-supervised learning setting. To address associated computation efficiency issues, it introduces two techniques: neighbor decoupling targeting LMs and implicit graph modeling targeting GNNs, respectively. Our proposed approach demonstrates superior performance, achieving state-of-the-art (SOTA) results on the ogbn-arxiv leaderboard, while maintaining computation cost and memory overhead comparable to graph-less fine-tuning of LMs. Through comprehensive experiments, we showcase its superior computation and data efficiency, presenting a promising solution for various LMs and graph learning tasks on TAGs.

Zhouyu Li, Zhijin Yang, Huayue Gu et al.

Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in an Earth observation constellation. OrbitChain decomposes analytics applications into microservices and allocates computational resources for time-constrained analysis. A traffic routing algorithm is devised to minimize the inter-satellite communication overhead. OrbitChain adopts a pipeline workflow that completes Earth observation tasks in real-time, facilitates time-sensitive applications and inter-constellation collaborations such as tip-and-cue. To evaluate OrbitChain, we implement a hardware-in-the-loop orbital computing testbed. Experiments show that our system can complete up to 60% analytics workload than existing Earth observation analytics framework while reducing the communication overhead by up to 72%.

Fangtong Zhou, Xiaorui Liu, Ruozhou Yu et al.

Traffic engineering (TE) in large-scale computer networks has become a fundamental yet challenging problem, owing to the swift growth of global-scale cloud wide-area networks or backbone low-Earth-orbit satellite constellations. To address the scalability issue of traditional TE algorithms, learning-based approaches have been proposed, showing potential of significant efficiency improvement over state-of-the-art methods. Nevertheless, the intrinsic limitations of existing learning-based methods hinder their practical application: they are not generalizable across diverse topologies and network conditions, incur excessive training overhead, and do not respect link capacities by default. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale networks, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of "predicting the optimal TE solution" into "predicting the optimal TE algorithm", which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact network topology or traffic patterns, and can efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. We trained and evaluated TELGEN on random and real-world networks with up to 5000 nodes and 106 links. TELGEN achieved less than 3% optimality gap while ensuring feasibility in all cases, even when the test network had up to 20x more nodes than the largest in training. It also saved up to 84% solving time than classical optimal solver, and could reduce training time per epoch and solving time by 2-4 orders of magnitude than latest learning algorithms on the largest networks.