Abstract.In this paper, we analyze the pseudo multi-hop distributed consensus algorithm with time-delay. If there is time-delay among agents, the convergence performance of distributed consensus algorithms degrades. Supposing that the time-delay in every link is identical and invariant in time and spatial, we analyze the convergence performance of the pseudo multi-hop distributed consensus algorithm with time-delay, and calculate the maximum time-delay that the pseudo multi-hop algorithm remains stable. Finally, simulation results are provided to verify these analytical results.
In the paper, we analyze the distributed flocking algorithms with communication noise. Under Boid model, flocking algorithm with communication noise is easy to diverge. In order to improve the convergence performance of flocking algorithms with communication noise and overcome the impact brought by communication noise on flocking algorithm, in the paper, a distributed flocking algorithm based on SODCT distributed consensus algorithm is proposed. The second-order flocking algorithm under Boid model is analyzed, and simulations are done. Results show that the second-order distributed flocking algorithm can reach cohesion, and its convergence performance is better than that of the first-order distributed flocking algorithm, moreover, the impact of communication noise on the second-order flocking algorithm is smaller.
Keywords-flocking; multi-agent system; communication noise; second-order algorithmI.
The widespread dissemination of fake news on social media brings adverse effects on the public and social development. Most existing techniques are limited to a single domain (e.g., medicine or politics) to identify fake news. However, many differences exist commonly across domains, such as word usage, which lead to those methods performing poorly in other domains. In the real world, social media releases millions of news pieces in diverse domains every day. Therefore, it is of significant practical importance to propose a fake news detection model that can be applied to multiple domains. In this paper, we propose a novel framework based on knowledge graphs (KG) for multi-domain fake news detection, named KG-MFEND. The model’s performance is enhanced by improving the BERT and integrating external knowledge to alleviate domain differences at the word level. Specifically, we construct a new KG that encompasses multi-domain knowledge and injects entity triples to build a sentence tree to enrich the news background knowledge. To solve the problem of embedding space and knowledge noise, we use the soft position and visible matrix in knowledge embedding. To reduce the influence of label noise, we add label smoothing to the training. Extensive experiments are conducted on real Chinese datasets. And the results show that KG-MFEND has a strong generalization capability in single, mixed, and multiple domains and outperforms the current state-of-the-art methods for multi-domain fake news detection.
In order to accelerate the convergence rate of distributed consensus problem under complex topology, in the paper, the distributed consensus algorithm based label propagation algorithm was proposed. Firstly, we composed the complex topology into two layer of topologies by label propagation algorithm, the first layer of topology was consist of a few small communities, and every small community was considered as a node of the second layer of topology. The consensus firstly was reached in the first layer topology, then the consensus was reached in the second layer topology. In the paper, the convergence performance of the algorithm was proved. The analysis and simulation on convergence rate were done, the results show the convergence rate of the algorithm was higher than that of the usually first-order distributed consensus algorithm.
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