The paper introduces TranCE, a doubly‑robust algorithm for estimating causal effects when an intervention is applied to one network but the interest lies in another, differing network. By extending selection diagrams to capture covariate and structural network shifts, the authors derive transport formulas for direct, spillover, and total effects, and validate the method on semi‑synthetic social‑network benchmarks and a real weather‑insurance field experiment.
By Xiaojing Du, Jiuyong Li, Lin Liu, Debo Cheng, Jixue Liu, Thuc Duy Le
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:2605. 30432v2 Announce Type: replace-cross Abstract: Social systems consist of networks of individuals who influence one another through social interactions.
By Moyi Tian, Daniel A. Messenger, Vanja Dukic, Nancy Rodr\'iguez, David M. Bortz
arXiv:2608. 02633v1 Announce Type: new Abstract: Regional surveillance data reflect local transmission, reporting, seeding, and external infection pressure, which are difficult to identify separately.
By Weixiong Hua, Fan Bu
arXiv:2607. 07425v1 Announce Type: cross Abstract: Many biological processes are governed by complex dynamical mechanisms that remain incompletely understood despite increasing volumes of experimental data.
By Rebecca M. Crossley, Yuan Yin, Sarah L. Waters, Ruth E. Baker
arXiv:2508. 18173v2 Announce Type: replace Abstract: The discovery of symbolic governing equations is a central goal in science; yet, it remains challenging particularly for graph dynamical systems, where the network topology further shapes the system behavior.
By Riccardo Cappi, Paolo Frazzetto, Nicol\`o Navarin, Alessandro Sperduti
arXiv:2510. 17088v3 Announce Type: replace-cross Abstract: Financial anomalies arise from heterogeneous mechanisms - price shocks, liquidity freezes, contagion cascades, and momentum reversals - yet existing detectors produce uniform anomaly scores without revealing which mechanism is failing or where risks concentrate.
By Zan Li, Rui Fan
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
The paper introduces MS‑WDRO, a multi‑source Wasserstein distributionally robust optimization framework for reconstructing complex network topologies from scarce target‑domain data and abundant heterogeneous source data. It fuses sources via a weighted Wasserstein barycenter, builds an ambiguity set around it, and solves a regularized Laplacian estimator using a provably convergent ADMM scheme. The authors provide finite‑sample guarantees, demonstrate that naive aggregation is suboptimal, and show through experiments on synthetic data and the ABIDE I neuroimaging dataset that MS‑WDRO outperforms seven baselines in graph recovery, sample efficiency, and diagnostic utility, especially when target samples are limited.
By Chuansen Peng, Yifan Xia, Jinshan Zhong, Xiaojing Shen
arXiv:2607. 13120v1 Announce Type: cross Abstract: Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs.
By Jiaze Song, Runhao Zhao, Minghao Xu, Bin Cui, Wentao Zhang
arXiv:2608. 13171v1 Announce Type: cross Abstract: To avoid missing important variables and their connections in networks, more and more variables are included in network analysis.
By Lourens Waldorp