Demo Data from VLDB 2026 / reviewers in the wild / expert
Zongzong Lin
dblp:182/7544
· DBLP profile ↗
3ranked-venue papers
2as first author
0since 2021 · last 2020
0000-0003-0014-8307ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorComputer networks · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Network security · 100% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › malware propagation
epidemic modeling |
0.4 | 1 | 2019 | Unified Preventive and Reactive Cyber Defense Dynamics Is Still Globally Convergent · IEEE/ACM Trans. Netw. 2019 |
Methods — techniques the papers use, named apart from their topics
dynamical systems analysis · 0.4convergence analysis · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2020 | Synchronizing non-identical time-varying delayed neural network systems via iterative learning control
Zongzong Lin, Xiaoguang Zou, Changkai Sun, Wenlian Lu |
Neurocomputing | 2 |
| 2019 | η(t)-consensus of multi-agent systems with directed graphs via event-triggered principles
Zongzong Lin, Wenlian Lu, Tianping Chen |
Neurocomputing | 1 |
| 2019 | Unified Preventive and Reactive Cyber Defense Dynamics Is Still Globally ConvergentabstractA class of the preventive and reactive cyber defense dynamics has recently been proven to be globally convergent, meaning that the dynamics always converges to a unique equilibrium whose location only depends on the values of the model parameters (but not the initial state of the dynamics). In this paper, we unify the aforementioned class of preventive and reactive cyber defense dynamics models and the closely related class of N-intertwined epidemic models into a single framework. We prove that the unified dynamics is still globally convergent under some mild conditions, which are naturally satisfied by the two specific classes of dynamics models mentioned above and are inevitable when analyzing a more general framework. We also characterize the convergence speed of the unified dynamics. As a corollary, we obtain that the N-intertwined epidemic model and its extension are globally convergent, together with a full characterization on their convergence speed, which is only partially addressed in the literature. Zongzong Lin, Wenlian Lu, Shouhuai Xu |
IEEE/ACM Trans. Netw. | 1 |