Probing Limits of Information Spread with Sequential Seeding

Abstract

We consider here information spread which propagates with certain probability from nodes just activated to their not yet activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. Such activation is commonly performed in a single stage. A novel approach based on sequential seeding is analyzed here resulting in three fundamental contributions. First, we propose a coordinated execution of randomized choices to enable precise comparison of different algorithms in general. We apply it here when the newly activated nodes at each stage of spreading attempt to activate their neighbors. Then, we present a formal proof that sequential seeding delivers at least as large coverage as the single stage seeding does. Moreover, we also show that, under modest assumptions, sequential seeding achieves coverage provably better than the single stage based approach using the same number of seeds and node ranking. Finally, we present experimental results showing how single stage and sequential approaches on directed and undirected graphs compare to the well-known greedy approach to provide the objective measure of the sequential seeding benefits. Surprisingly, applying sequential seeding to a simple degree-based selection leads to higher coverage than achieved by the computationally expensive greedy approach currently considered to be the best heuristic.

References in corpus (6)

• The structure of scientific collaboration networks
• Influence maximization in complex networks through optimal percolation
• Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach
• Scalable Methods for Adaptively Seeding a Social Network
• Maximizing Influence Propagation in Networks with Community Structure
• Seeds Buffering for Information Spreading Processes

Cited by in corpus (5)

• Network resilience
• Fast and principled simulations of the SIR model on temporal networks
• Effective Influence Spreading in Temporal Networks with Sequential Seeding
• Habituation Effect in Social Networks as a Potential Factor Silently Crushing Influence Maximisation Efforts
• A partial knowledge of friends of friends speeds social search