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On the non-progressive spread of influence through social networks

Fazli, M ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1007/978-3-642-29344-3_27
  3. Publisher: 2012
  4. Abstract:
  5. The spread of influence in social networks is studied in two main categories: the progressive model and the non-progressive model (see e.g. the seminal work of Kempe, Kleinberg, and Tardos in KDD 2003). While the progressive models are suitable for modeling the spread of influence in monopolistic settings, non-progressive are more appropriate for modeling non-monopolistic settings, e.g., modeling diffusion of two competing technologies over a social network. Despite the extensive work on the progressive model, non-progressive models have not been studied well. In this paper, we study the spread of influence in the nonprogressive model under the strict majority threshold: given a graph G with a set of initially infected nodes, each node gets infected at time τ iff a majority of its neighbors are infected at time τ - 1. Our goal in the MinPTS problem is to find a minimum-cardinality initial set of infected nodes that would eventually converge to a steady state where all nodes of G are infected. We prove that while the MinPTS is NP-hard for a restricted family of graphs, it admits an improved constant-factor approximation algorithm for power-law graphs. We do so by proving lower and upper bounds in terms of the minimum and maximum degree of nodes in the graph. The upper bound is achieved in turn by applying a natural greedy algorithm. Our experimental evaluation of the greedy algorithm also shows its superior performance compared to other algorithms for a set of realworld graphs as well as the random power-law graphs. Finally, we study the convergence properties of these algorithms and show that the nonprogressive model converges in at most O(|E(G)|) steps
  6. Keywords:
  7. Constant-factor approximation algorithms ; Convergence properties ; Experimental evaluation ; Graph G ; Greedy algorithms ; Lower and upper bounds ; Maximum degree ; Nonprogressive ; NP-hard ; Power-law graph ; Progressive models ; Real-world graphs ; Social Networks ; Steady state ; Upper Bound ; Approximation algorithms ; Graphic methods ; Information science ; Social networking (online) ; Social sciences computing ; Graph theory
  8. Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 7256 LNCS , 2012 , Pages 315-326 ; 03029743 (ISSN) ; 9783642293436 (ISBN)
  9. URL: http://link.springer.com/chapter/10.1007%2F978-3-642-29344-3_27