A scalable heuristic for viral marketing under the tipping model

Abstract

In a "tipping" model, each node in a social network, representing an individual, adopts a property or behavior if a certain number of his incoming neighbors currently exhibit the same. In viral marketing, a key problem is to select an initial "seed" set from the network such that the entire network adopts any behavior given to the seed. Here we introduce a method for quickly finding seed sets that scales to very large networks. Our approach finds a set of nodes that guarantees spreading to the entire network under the tipping model. After experimentally evaluating 31 real-world networks, we found that our approach often finds seed sets that are several orders of magnitude smaller than the population size and outperform nodal centrality measures in most cases. In addition, our approach scales well -on a Friendster social network consisting of 5.6 million nodes and 28 million edges we found a seed set in under

Real-Time Analytics for Fast Evolving Social Graphs

“…Various techniques based on centrality measures [4] and more recently the tipping model [7] exist. Irrespective of the method, traditional approaches consider static graphs or periodic snapshots on which they run the entire algorithm and recompute the set from scratch.…”

“…We rely on a recently proposed algorithm based on shell decomposition [7] as it was demonstrated to outperform the classic centrality measures and show robustness against the removal of high-degree nodes. This algorithm allows us to determine the smallest possible set of individuals (seed set) such that, if initially activated, the entire population will eventually become activated, adopting the new property, or in our case re-tweeting the ad.…”

“…We developed an incremental algorithm based on the original algorithm presented in [7] and implement it in our framework by using its distributed message passing model. The static graph algorithm [7] is a communication heavy algorithm which needs heavy communication.…”

“…The static graph algorithm [7] is a communication heavy algorithm which needs heavy communication. The proposed incremental algorithm tries to perform local updates with minimal communication across workers to maintain the minimal seed set.…”

Effects of inspections in small world social networks with different contagion rules

“…However, recent investigations have shown that the micro-processes involved in social contagion are complex [13][14][15][16]. A family of models take into account social peculiarities by assuming a discontinuous response of a node to the inflow information [17]. These models are inspired in the classical threshold model [18] where the adoption of an initiative depends basically on whether a minimum number of neighbors have already adopted it.…”