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
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. 29596v1 Announce Type: cross Abstract: Characterizing the scenario underlying an epidemic from its disease cascade is an important task in simulation analytics.
By Amro Alabsi Aljundi, Galen Harrison, Jiangzhuo Chen, Abhijin Adiga, Anil Kumar Vullikanti, Madhav V. Marathe
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
arXiv:2608. 08406v1 Announce Type: new Abstract: Existing learning-based influence maximization frameworks rely heavily on complex neural architectures and continuous optimization over seed representations.
By Yiqiao Liao, Parinaz Naghizadeh
arXiv:2606. 07151v1 Announce Type: new Abstract: Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social networks or physical systems.
By William Cappelletti, \'Etienne Voutaz, Pascal Frossard
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
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. 05982v1 Announce Type: new Abstract: Inadequate target--disease linkage accounts for 40--50\% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients.
By Pui Chung Siu, Claudia Cabrera, Mani Mudaliar, Arkaitz Zubiaga
arXiv:2606. 27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure.
By Abderaouf Bahi
arXiv:2603. 10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery.
By Baoheng Zhu, Deyu Bo, Delvin Ce Zhang, Xiao Wang
arXiv:2606. 12581v1 Announce Type: cross Abstract: Real-world networks are inherently incomplete, noisy, and dynamically evolving, making it difficult to capture all actors and their relationships.
By Mateusz Stolarski, Micha{\l} Czuba, Piotr Bielak, Piotr Br\'odka