Linyuan Lü
Biographic Data
| ID | 7705817 |
|---|---|
| NAME | Linyuan Lü |
| GIVEN NAMES | Linyuan |
| FAMILY NAME | Lü |
| SIGNATURE | LU L |
| AFFILIATIONS | University of Science and Technology of China |
| ORCID | 0000-0003-0155-2002 |
| VERIFIED | Yes |
| TOTAL WORKS | 7 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 7 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2009 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Graph representation learning has become a vital tool for capturing intricate relationships within networked systems. However, as graphs scale up, data privacy concerns and regulatory restrictions often isolate data within different institutions, hindering collaborative analysis. While federated learning (FL) and swarm learning (SL) offer decentralized solutions, FL relies on a central coordinator, and SL struggles with non-I.I.D. graph data, lim…
EntroBot
Modeling Emotional Dynamics in Social Networks
Information cocooning—amplified by algorithmic filtering—poses complex challenges for emotional dynamics in online social networks. This study explores how algorithmically reinforced information cocooning shapes information diffusion and group emotional dynamics in online social networks. We propose a viewpoint-based network evolution model that simulates structural transformations driven by user preferences. To model the hidden influence of pers…
The Role of Susceptible Individuals in Spreading Dynamics
Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals’ diverse characteristics contribute differently to the spreading performance, leading to a heterogeneous infection rate across the system. To investigate network spreading dynamics under heterogeneous …
Citations or dollars? Early signals of a firm’s research success
Scientific and technological progress is largely driven by firms in many domains, including artificial intelligence and vaccine development. The early identification of the future performance of innovation players is a relevant goal for policymakers and practitioners. In this work, we investigate how the future trajectory of a firm can be predicted by the economic or technological value of its early patents. By inspecting the patenting life cycle…
Link prediction in complex networks
Predicting missing links via local information
No prominent works on this page.
Predicting missing links via local information
Link prediction in complex networks
Citations or dollars? Early signals of a firm’s research success
Scientific and technological progress is largely driven by firms in many domains, including artificial intelligence and vaccine development. The early identification of the future performance of innovation players is a relevant goal for policymakers and practitioners. In this work, we investigate how the future trajectory of a firm can be predicted by the economic or technological value of its early patents. By inspecting the patenting life cycle…
The Role of Susceptible Individuals in Spreading Dynamics
Exploring the internal mechanism of information spreading is critical for understanding and controlling the process. Traditional spreading models often assume individuals play the same role in the spreading process. In reality, however, individuals’ diverse characteristics contribute differently to the spreading performance, leading to a heterogeneous infection rate across the system. To investigate network spreading dynamics under heterogeneous …
Cooperative Network Learning for Large-Scale and Decentralized Graphs
Graph representation learning has become a vital tool for capturing intricate relationships within networked systems. However, as graphs scale up, data privacy concerns and regulatory restrictions often isolate data within different institutions, hindering collaborative analysis. While federated learning (FL) and swarm learning (SL) offer decentralized solutions, FL relies on a central coordinator, and SL struggles with non-I.I.D. graph data, lim…
EntroBot
Modeling Emotional Dynamics in Social Networks
Information cocooning—amplified by algorithmic filtering—poses complex challenges for emotional dynamics in online social networks. This study explores how algorithmically reinforced information cocooning shapes information diffusion and group emotional dynamics in online social networks. We propose a viewpoint-based network evolution model that simulates structural transformations driven by user preferences. To model the hidden influence of pers…
Computer Science (5 works) · Advanced Graph Neural Networks (3 works) · Complex Network Analysis Techniques (2 works) · Data modeling (2 works) · Link (geometry) (2 works) · Opinion Dynamics and Social Influence (2 works) · Physics (2 works) · Advanced Malware Detection Techniques (1 work) · Artificial Intelligence (1 work) · Big data (1 work)