FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism

Graph embedding maps graph nodes into low-dimensional vectors to support applications such as recommendation, fraud detection, and retrieval-augmented generation. As graphs scale to billions of edges, scalable and efficient graph embedding has become increasingly important. Existing frameworks commonly adopt a sampling-training paradigm, in which mini-batches are constructed by sampling nodes and their neighbors. However, sampling is typically decoupled from evolving embedding quality, causing redundant exploration of well-trained regions while under-sampling undertrained nodes. At the system level, such decoupling further leads to excessive communication, serialized execution, and low resource utilization in distributed settings. We present FeLoG, a feedback loop-driven system for distributed graph embedding. ( 1 ) FeLoG introduces feedback-coupled sampling and training, dynamically prioritizing undertrained nodes according to real-time embedding-quality feedback, reducing redundant computation and accelerating convergence. ( 2 ) It employs activity-aware communication that compresses frequently occurring node sequences to reduce intra-machine PCIe traffic and selectively synchronizes frequently updated embeddings to reduce inter-machine communication. ( 3 ) It adopts a round-interleaved pipeline that overlaps next-round sampling with current-round training to improve CPU-GPU utilization. Experiments against state-of-the-art baselines on large-scale graphs show that FeLoG achieves an average speedup of 27.9×, reduces communication cost by more than 53.1%, and sustains over 80% CPU-GPU utilization.