Xiaxuan Gao

Zhicheng Ren, Yifu Yuan, Yuxin Wu et al.

Training labels for graph embedding algorithms could be costly to obtain in many practical scenarios. Active learning (AL) algorithms are very helpful to obtain the most useful labels for training while keeping the total number of label queries under a certain budget. The existing Active Graph Embedding framework proposes to use centrality score, density score, and entropy score to evaluate the value of unlabeled nodes, and it has been shown to be capable of bringing some improvement to the node classification tasks of Graph Convolutional Networks. However, when evaluating the importance of unlabeled nodes, it fails to consider the influence of existing labeled nodes on the value of unlabeled nodes. In other words, given the same unlabeled node, the computed informative score is always the same and is agnostic to the labeled node set. With the aim to address this limitation, in this work, we introduce 3 dissimilarity-based information scores for active learning: feature dissimilarity score (FDS), structure dissimilarity score (SDS), and embedding dissimilarity score (EDS). We find out that those three scores are able to take the influence of the labeled set on the value of unlabeled candidates into consideration, boosting our AL performance. According to experiments, our newly proposed scores boost the classification accuracy by 2.1% on average and are capable of generalizing to different Graph Neural Network architectures.

Praveen Chaganlal, Jia Guo, Vivek Vaidyanathan et al.

Real-time OLAP datastores are critical infrastructure for modern enterprises, powering interactive analytics on petabyte-scale datasets with subsecond latency requirements. As these systems become integral to service architectures, maintaining strict SLAs under failures, load spikes, and cluster changes is as important as raw performance. We present a set of resiliency mechanisms developed for Apache Pinot at LinkedIn, applicable to modern OLAP systems broadly. We introduce Query Workload Isolation (QWI), which provides workload-level CPU and memory budgeting across Pinot's broker and server tiers via fine-grained resource accounting and sub-millisecond enforcement, delivering predictable tail latency and fairness with under 1% overhead. We present Impact-Free Rebalancing for SLA-safe data movement during routine operations (e.g., upgrades, scale-out, and recovery), and Maintenance Zone Awareness to place replicas across fault domains and mitigate correlated failures. We also describe Adaptive Server Selection, which routes queries using real-time load and performance signals to avoid slow or failing nodes while preserving balanced utilization. Together, these mechanisms form a holistic resiliency framework deployed in production at LinkedIn, enabling stable query latency and high availability at scale.