Absence of influential spreaders in rumor dynamics (1112.2239v1)

Abstract: Recent research [1] has suggested that coreness, and not degree, constitutes a better topological descriptor to identifying influential spreaders in complex networks. This hypothesis has been verified in the context of disease spreading. Here, we instead focus on rumor spreading models, which are more suited for social contagion and information propagation. To this end, we perform extensive computer simulations on top of several real-world networks and find opposite results. Namely, we show that the spreading capabilities of the nodes do not depend on their $k$-core index, which instead determines whether or not a given node prevents the diffusion of a rumor to a system-wide scale. Our findings are relevant both for sociological studies of contagious dynamics and for the design of efficient commercial viral processes.

• The paper demonstrates that influential spreaders do not exist in rumor dynamics, as the final spread shows minimal correlation with the initial node's k-core index.
• It identifies 'firewall' nodes that rapidly become stiflers, effectively halting information dissemination in the network.
• The study shows that high-core nodes, despite limited spreading power, maintain greater awareness by being frequently exposed to ongoing rumor processes.

Absence of Influential Spreaders in Rumor Dynamics

This paper examines the dynamics of rumor spreading across various complex networks, challenging the prevalent notion that influential spreaders play a pivotal role in propagating information. The research builds on previous studies that identified central nodes as key influencers in epidemic models, particularly through metrics like the $k$-core index. Here, the authors explore whether such centrality measures hold equivalent predictive power in the context of rumor dynamics.

Key Findings

The paper utilizes extensive simulations conducted on four real-world networks: an email contact network, a political blogs network, the autonomous systems (AS) level of the Internet, and a Twitter social network. The simulations employ two variations of a rumor-spreading model: the Contact Process (CP) and the Truncated Process (TP). In both models, nodes transition between three states: ignorant, spreader, and stifler. The transition depends on interactions with other nodes and predefined rates of spreading and stopping.

1. Absence of Influential Spreaders: Contrary to epidemic spreading dynamics, the researchers find that there are no influential spreaders in rumor dynamics. The final density of stiflers—indicative of how far a rumor spreads through the network—shows minimal correlation with the $k$-core index of the initial spreader node. This finding opposes the suggestion that a node’s topological centrality directly translates to higher influence in information diffusion.
2. Role of Firewalls: The paper introduces the concept of "firewall" nodes, which rapidly become stiflers and effectively halt the spread of information. These nodes tend to occupy high-core levels within the network structure, acting as barriers that inhibit widespread dissemination, a dynamic absent in previous notions of influential spreaders.
3. Awareness Levels: Despite the absence of influential spreaders, network centrality remains important for awareness levels. Nodes within higher $k$-cores are more frequently exposed to rumors, acting as critical observers within the network. This suggests that while central nodes may not enhance dissemination, they retain significant informational advantage by being aware of most ongoing rumor processes.

Implications and Future Work

The findings have important implications for understanding the nature of information spreading, particularly in contrast to epidemic models. The identification that centrality does not equate to enhanced spreading capacity in rumor dynamics can redefine strategies in viral marketing and information campaigns. It also emphasizes the need to consider alternative roles of central nodes, such as information regulators or sinks, within a network.

Furthermore, the work points towards the necessity for refined models that accurately capture the nuances of rumor spreading. Future research could incorporate more sophisticated dynamics, potentially integrating empirical data from online social platforms to assess real-world behaviors more closely.

By challenging the established role of influential spreaders, this research contributes to a deeper comprehension of information dynamics and the structural features that govern them. This approach may lead to the development of more effective methods for managing information flow in complex systems, with wide-ranging applications from marketing strategies to understanding social movements.