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.
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: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:2608.29535v1 Announce Type: cross
Abstract: Infectious disease transmission is shaped by patterns of human interaction, which adapt as epidemic conditions change. Capturing these context-depend...
By Yicheng Mao, Haoyang Li, Rob Deardon, Hongru Du
arXiv:2606. 02867v1 Announce Type: cross Abstract: Human behaviour during epidemics affects infectious disease dynamics, but quantifying this remains deeply challenging.
By Petra Ferenz, Ava Keeling, Tobias O'Keefe, Lorenzo Stigliano, Francesco Di Lauro, Andres Colubri, Jasmina Panovska-Griffiths