Abstract
Information diffusion has been widely discussed in various disciplines including sociology, economics, physics or computer science. In this paper, we generalize the linear threshold model in signed networks consisting of both positive and negative links. We analyze the dynamics of the spread of information based on balance theory, and find that a signed network can generate path dependence while structural balance can help remove the path dependence when seeded with balanced initialized active nodes. Simulation shows that the diffusion of information based on positive links contradicts that based on negative links. More positive links in signed networks are more likely to activate nodes and remove path dependence, but they can reduce predictability that is based on active states. We also find that a balanced structure can facilitate both the magnitude and speed of information diffusion, remove the path dependence, and cause polarization.
Highlights
Data Availability Statement: All relevant data are described in the paper
When the network structure is balanced and the initialized active nodes are assigned to a balanced state, the path of information diffusion can be predictable and this model tends to activate nodes in the direction of cluster assignation of structural balance, i.e. the active state of a node si will gradually evolve to its cluster assignation in structural balance ci
We introduce a parameter β which denotes the proportion of imbalanced edges
Summary
Data Availability Statement: All relevant data are described in the paper. the authors first generate the random networks by a fixed method, and they generate designed networks which are the extension of Newman’s proposed benchmark networks, they operate the simulation on the generated networks to analyze the simulated results. When the network structure is balanced and the initialized active nodes are assigned to a balanced state (nodes with the same state are connected by positive edges, while those with different states are connected by negative edges), the path of information diffusion can be predictable and this model tends to activate nodes in the direction of cluster assignation of structural balance, i.e. the active state of a node si will gradually evolve to its cluster assignation in structural balance ci.
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