activity
20212025
most citedBeyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media

23 citations · 25 across the 11 of their papers we have counts for

collaborators

11 papers

q-bio.MN2025

Tracking large chemical reaction networks and rare events by neural networks

Jiayu Weng, Xinyi Zhu, Jing Liu +3

Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via the chemical master equation becomes int…

cs.SI2025

Meritocracy versus Matthew-effect: Two underlying network formation mechanisms of online social platforms

Yuchen Xu, Wenjun Mei, Ge Chen +1

With the rapid development of the internet industry, online social networks have come to play an increasingly significant role in everyday life. In recent years, content-based emer…

physics.soc-ph2025

Universal Network Generation Model via Exponential Probabilistic Growth and Vari-linear Preferential Attachment

Jinhu Ren, Linyuan Lü

Generated networks are widely used in network-based research as a convenient simulation environment. Generating universal networks that more accurately reflect real-world patterns…

physics.soc-ph2024

Uncovering key predictors of high-growth firms via explainable machine learning

Yiwei Huang, Shuqi Xu, Linyuan Lü +2

Predicting high-growth firms has attracted increasing interest from the technological forecasting and machine learning communities. Most existing studies primarily utilize financia…

cs.SI2024★ 1 cited

A universal optimization framework based on cycle ranking for influence maximization in complex networks

Wenfeng Shi, Tianlong Fan, Shuqi Xu +2

Influence maximization aims to identify a set of influential individuals, referred to as influencers, as information sources to maximize the spread of information within networks,…

cs.SI2024★ 1 cited

The role of susceptible individuals in spreading dynamics

Chang Su, Fang Zhou, Linyuan Lü

Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the…

physics.soc-ph2024

Network higher-order structure dismantling

Peng Peng, Tianlong Fan, Linyuan Lü

Diverse higher-order structures, foundational for supporting a network's "meta-functions", play a vital role in structure, functionality, and the emergence of complex dynamics. Nev…

physics.soc-ph2023

Stock network inference: A framework for market analysis from topology perspective

Yijie Teng, Rongmei Yang, Shuqi Xu +1

From a complex network perspective, investigating the stock market holds paramount significance as it enables the systematic revelation of topological features inherent in the mark…

physics.soc-ph2023

A generalized simplicial model and its application

Rongmei Yang, Fang Zhou, Bo Liu +1

Higher-order structures, consisting of more than two individuals, provide a new perspective to reveal the missed non-trivial characteristics under pairwise networks. Prior works ha…

cs.SI2021★ 23 cited

Beyond network centrality: Individual-level behavioral traits for predicting information superspreaders in social media

Fang Zhou, Linyuan Lü, Jianguo Liu +1

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To th…

cs.SI2021

Cost-effective Network Disintegration through Targeted Enumeration

Zhigang Wang, Ye Deng, Petter Holme +3

Finding an optimal subset of nodes or links to disintegrate harmful networks is a fundamental problem in network science, with potential applications to anti-terrorism, epidemic co…