arXiv Machine Learning
Degree-Mass Message Passing for Betweenness Ranking in Directed and Undirected Networks
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arXiv Machine Learning
Jul 13
Graph Neural Networks for Scalable and Transferable Node Centrality Approximation
arXiv:2607. 09372v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) provide a learning-based framework for approximating graph quantities that are expensive to compute exactly.
arXiv AI
Jun 2
A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks
arXiv:2507. 19702v1 Announce Type: cross Abstract: Identifying influential nodes in complex networks is a critical task with a wide range of applications across different domains.
arXiv Machine Learning
Sep 11
SynCo: Synthetic Community-Aware Attributed Graph Generator for Graph Neural Network Benchmarking
SynCo is a synthetic graph generator that lets users control node degree distributions and sub‑community structures, addressing limitations of existing generators that rely on power‑law distributions and lack flexibility. It is evaluated on graph mimicking, hyperparameter tuning, and node clustering, outperforming state‑of‑the‑art methods while preserving original data distributions. SynCo can generate large graphs with up to 2.1 million nodes.
arXiv Machine Learning
Aug 28
Scaling Graph Neural Networks for Friend Recommendation: Multi-Hash User Embeddings and Temporal Neighbor Sampling
The paper presents a scalable graph neural network (GNN) system for friend recommendation on a massive social graph. It introduces two key design choices: multi-hash ID embeddings that shrink the embedding table by over 98% without hurting ranking quality, and a timestamp-sorted compressed sparse row (CSR) storage with binary search that reduces temporal neighbor sampling from linear to logarithmic time. Experiments on a 194‑million‑user, 28‑billion‑edge graph show that these techniques enable production‑grade performance, boosting friend additions by 16% and unique friend adders by 11.5% in an online A/B test.
arXiv Machine Learning
Jun 10
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
arXiv:2606. 10249v1 Announce Type: new Abstract: We examine whether graph neural network (GNN) design rules generalize across benchmark families by studying aggregator selection (sum, mean, max) on 24 node-classification datasets spanning citation, heterophilic, LINKX Facebook-100, co-purchase, and co-authorship graphs.
arXiv Machine Learning
Jun 2
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.