Artificial Intelligence for Complex Network: Potential, Methodology and Application
Name
3701716.3715857.pdf
Size
855.34 KB
Format
Adobe PDF
Checksum (MD5)
9da2672fc56f533ad6a0f8dc1dfa2ad3
Author(s)
Ding, Jingtao
Zheng, Yu
Wang, Huandong
Cannistraci, Carlo Vittorio
Gao, Jianxi
Li, Yong
Shi, Chuan
Date Issued
May 23, 2025
Publisher
ACM|Companion Proceedings of the ACM Web Conference 2025
Citation
Jingtao Ding, Yu Zheng, Huandong Wang, Carlo Vittorio Cannistraci, Jianxi Gao, Yong Li, and Chuan Shi. 2025. Artificial Intelligence for Complex Network: Potential, Methodology and Application. In Companion Proceedings of the ACM on Web Conference 2025 (WWW '25). Association for Computing Machinery, New York, NY, USA, 5–8.
Version
Final published version
Abstract
This tutorial will explore the fascinating domain of empirical network modeling through artificial intelligence (AI) techniques, with
applications across social media, web systems, and urban environments. Participants will gain valuable insights into incorporating
advanced AI methods—such as graph machine learning, deep reinforcement learning, and generative models—within complex network science. The goal is to provide a comprehensive understanding
of how these models can effectively represent, predict, and control
empirical networked systems with heterogeneous structures and
dynamic processes. The tutorial will begin by introducing essential background knowledge, outlining motivations and challenges,
exploring recent methodological advances, and highlighting key
applications.
Description
WWW Companion ’25, Sydney, NSW, Australia
MIT Department
Senseable City Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3701716.3715857
