Identifying a set of influential spreaders in complex networks (1602.00070v2)

Abstract: Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-$r$ ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and $k$-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) model, VoteRank outperforms the traditional benchmark methods on both spreading speed and final affected scale. What's more, VoteRank is also superior to other group-spreader identifying methods on computational time.

• The paper introduces VoteRank, which uses an iterative voting mechanism to identify influential nodes in complex networks.
• It reduces the voting capacity of selected nodes to ensure that chosen spreaders remain spatially distinct and minimize overlap.
• Empirical tests using SIR and SI models on real-world datasets confirm VoteRank's superior efficiency and scalability.

VoteRank: A Method for Identifying Influential Spreaders in Complex Networks

The effective dissemination of information in complex networks is an increasingly critical aspect of network theory. The identification of influential nodes or spreaders within these networks is fundamental for optimizing information propagation. This paper by Zhang et al. presents an iterative algorithm known as VoteRank, designed to select a set of nodes that maximize information spread efficiently.

Overview of Influence Identification Strategies

Prior strategies for influence identification in networks have ranged from classic techniques such as PageRank and $k$-shell decomposition to more recent heuristic methods like hill-climbing and SPIN. However, these methods often encounter issues, particularly when spreaders overlap in terms of influence or are computationally intensive for large-scale networks.

VoteRank distinguishes itself by adopting a decentralized approach. Nodes within the network vote for each other, and a node's voting capability diminishes after its selection as a spreader, thereby encouraging the selection of nodes that are spatially distinct.

Methodological and Experimental Insights

VoteRank's core concept is iterative voting, where each node initially possesses a voting ability. In each iteration, nodes vote for their neighbors, and the node with the highest accumulated votes is elected as an influential spreader. The voting ability of the neighbors of the selected node is subsequently reduced. This reduction mechanism prevents central nodes from repeatedly dominating the selection process, effectively distributing influence.

The paper evaluates VoteRank's performance against several benchmark methods, using the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) epidemic models on four real-world datasets: YOUTUBE, COND-MAT, BERKSTAN, and NOTRE DAME. Experimental results demonstrate that VoteRank not only enhances the spreading rate and scale of affected nodes but does so with superior computational efficiency compared to existing methods. For instance, VoteRank shows fast convergence and better scalability, indicating its suitability for large networks.

Implications and Future Directions

The practical implications of VoteRank are significant. In real-world applications such as viral marketing on social platforms, identifying key influencers can greatly amplify information dissemination. Theoretically, VoteRank contributes to the understanding of network topology's role in optimal spread strategies, especially in the context of structured network subsets that limit redundancy and overlap among spreaders.

Future research might extend VoteRank to dynamic and temporal networks, considering how influence identification adapts to evolving network structures. Additionally, further investigation could explore VoteRank's applicability to weighted networks or its integration with other machine learning approaches for real-time influence maximization.

Conclusion

VoteRank represents an advancement in the domain of influence maximization within complex networks, offering a balance between computational efficiency and effectiveness in identifying spatially diverse influential spreaders. Through a systematic voting procedure and adaptable neighbor influence mechanisms, VoteRank is a robust tool for practitioners and researchers aiming to harness the power of network spread in various contexts. Its successful application to large-scale real-world networks paves the way for its adoption in broader and more dynamic networking environments.