The paper introduces an unsupervised graph neural network framework for solving the Minimum Dominating Set problem in social networks. By training on 12,000 synthetic graphs, the method achieves up to 55× faster inference than metaheuristic baselines and 14× faster than supervised approaches while producing optimal or near‑optimal dominating sets on real‑world benchmarks. The learned heuristic generalizes well to unseen graph distributions, indicating strong practical applicability for large‑scale social network analysis.
By Erfan Ahmadi, Mina Shirazi, Behnam Bahrak
Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification. Enhancing local features with relevant global information is crucial for accurate link prediction, yet it remains challenging.
arXiv:2608. 05016v1 Announce Type: cross Abstract: Predicting the existence and type of links (edges) between nodes in a multi-relational graph is key for applications from social interaction prediction to knowledge relationship identification.
By Zidu Yin, Yuankai Qi, Dong Gong, Ehsan Abbasnejad, Kun Yue, Javen Qinfeng Shi
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.
By Mohammed A. Ramadhan, Abdulhakeem O. Mohammed
arXiv:2601. 16233v2 Announce Type: replace-cross Abstract: HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment.
By Akseli Kangaslahti, Davin Choo, Lingkai Kong, Milind Tambe, Alastair van Heerden, Cheryl Johnson
arXiv:2605. 12513v2 Announce Type: replace-cross Abstract: Influence maximization (IM) in real platforms is challenged by incomplete, noisy social graphs and non-stationary diffusion dynamics.
By Haohua Niu, Yuxuan Yang, Lingfeng Zhang, Hao Li, Jiao Liang, Zongfu Luo, Luca Rossi
arXiv:2606. 08306v1 Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where models are trained on a single network and cannot be reused on unseen graphs without retraining.
By Micha{\l} Czuba, Mateusz Stolarski, Adam Pir\'og, Piotr Bielak, Piotr Br\'odka
The paper evaluates deep graph generative models against traditional network science models by comparing the topological similarity of generated networks to real-world networks and their effectiveness in identifying node immunization strategies for epidemic or misinformation spread. It finds that two deep graph generative models produce synthetic networks that closely resemble real-world structural properties, enabling them to identify effective immunization strategies.
By Tianrui Mao, Abele Malan, Megha Khosla, Lydia Chen, Huijuan Wang
arXiv:2606. 04468v1 Announce Type: cross Abstract: Offline multi-objective optimization (Offline MOO) aims to discover novel Pareto-optimal designs based on static datasets without expensive environment interactions.
By Ruiqing Sun, Sen Yang, Dawei Feng, Bo Ding, Yijie Wang, Huaimin Wang
arXiv:2606. 18317v1 Announce Type: new Abstract: Most graph neural network (GNN) cores rely on graph convolutions, typically implemented as message passing between direct (single-hop) neighbors.
By Xuling Zhang, Peng Wang, Daiyan Li, Aoran Huang, Zeiwei Chen, Yongkui Yang
The paper introduces an action‑conditioned Network World Model that learns how a network’s diffusion dynamics evolve under interventions over time. This model can quickly predict the outcomes of actions, enabling a coding agent to design and refine algorithms that select actions to maximize expected performance on complex network tasks. Experiments on eight network tasks and five diffusion models show that the resulting algorithms match or surpass the best existing baselines in 138 of 141 settings while achieving up to 14.5× faster rollouts than traditional Monte Carlo simulation.
By Rishab Alagharu, Hongji Pu, Zeeshan Memon, Xinyuan Song, Yuntong Hu, Liang Zhao
arXiv:2606. 04287v1 Announce Type: cross Abstract: Generating realistic and diverse graphs is a key problem in machine learning, with applications in molecular discovery, circuit design, cybersecurity, and beyond.
By Alessio Barboni, Massimiliano Lupo Pasini, Bishal Lakha, Edoardo Serra