Analysis of epidemic propagation using mean field theory on signed graphs

Over the past few decades, the study of epidemic propagation has caught widespread attention from many areas. The field of graphs contains a wide body of research, yet only a few studies explore epidemic propagation’s dynamics in “signed” networks. Motivated by this problem, in this paper we propose a new epidemic propagation model for signed networks, denoted as S-SIS. To explain our analysis, we utilized the mean field theory to demonstrate the theoretical results. When we compare epidemic propagation through negative links to those only having positive links, we find that a higher proportion of infected nodes actually spreads at a relatively small infection rate. It is also found that when the infection rate is higher than a certain value, the overall spreading in a signed network begins showing signs of suppression. Finally, in order to verify our findings, we apply the S-SIS model on Erdös–Rényi random network and scale-free network, and the simulation results is well consist with the theoretical analysis.

Event-triggered bipartite consensus of multi-agent systems in signed networks

<abstract><p>This paper focuses on the event-triggered bipartite consensus of multi-agent systems in signed networks, where the dynamics of each agent is assumed to be Lur'e system, and both the cooperative interaction and antagonistic interaction are allowed among neighbor agents. A novel event-triggered communication scheme is presented to save limited network resources, and distributed bipartite control techniques are raised to address the bipartite leaderless consensus and bipartite leader-following consensus respectively. By virtue of the Lyapunov stability theory and algebraic graph theory, bipartite consensus conditions are derived, which can be easily solved by MATLAB. In addition, the upper bounds of the sampling period and triggered parameter can be estimated. Finally, two examples are employed to show the validity and advantage of the proposed transmission scheme.</p></abstract>

The impact of negative edges on spreading in signed networks

In signed networks, negative edges represent negative relationships; the increase or gathering of negative interpersonal relationships can lead to social turmoil, which will affect the spreading of diseases or information. Therefore, it is significant to study the impact of the proportion and configuration of negative edges on spreading. In this paper, to study the impact of negative relationships on spreading, we propose a heterogeneous spreading model using signed networks. In this model, we use the balance of the local structure to quantify the probability of contact between individuals, making the contacts heterogeneous. We then examine the impact of negative edges on spreading using numerical simulations. We find that the balance of the network and the spreading coverage (i.e. outbreak size) gradually decrease with the proportion of negative edges. Compared with preference configurations (in which negative signs are placed on edges that have an important impact on spreading), a random configuration (in which negative signs are placed on random edges) has a suppressive effect on spreading. This provides information for epidemic prevention. Finally, we find that there are two important factors — contact probability and spreading paths — that could explain the observed spreading phenomena.

Identifying hidden coalitions in the US House of Representatives by optimally partitioning signed networks based on generalized balance

In network science, identifying optimal partitions of a signed network into internally cohesive and mutually divisive clusters based on generalized balance theory is computationally challenging. We reformulate and generalize two binary linear programming models that tackle this challenge, demonstrating their practicality by applying them to partition signed networks of collaboration and opposition in the US House of Representatives. These models guarantee a globally optimal network partition and can be practically applied to signed networks containing up to 30,000 edges. In the US House context, we find that a three-cluster partition is better than a conventional two-cluster partition, where the otherwise hidden third coalition is composed of highly effective legislators who are ideologically aligned with the majority party.