Effective Influence Spreading in Temporal Networks With Sequential Seeding

Abstract

The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence. To tackle this and similar challenges, more than a decade ago, researchers started to investigate the influence maximisation problem. The challenge is to find the best set of initially activated seed nodes in order to maximise the influence spread in networks. In typical approach we will activate all seeds in single stage, at the beginning of the process, while in this work we introduce and evaluate a new approach for seeds activation in temporal networks based on sequential seeding. Instead of activating all nodes at the same time, this method distributes the activations of seeds, leading to higher ranges of influence spread. The results of experiments performed using real and randomised networks demonstrate that the proposed method outperforms single stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal networks are an adequate choice for modelling dynamic processes, the results of this work can be interpreted as encouraging to apply temporal sequential seeding for real world cases, especially knowing that more sophisticated seed selection strategies can be implemented by using the seed activation strategy introduced in this work.

Burstiness and information spreading in active particle systems

“…As we have mentioned, temporal inhomogeneities can strongly affect, i.e., promoting or slowing down, the spreading dynamics on the temporal networks. [10][11][12][13][14][15] To specify the information-spreading dynamics on the temporal networks constructed by the Vicsek model, the simple susceptibleinfected (SI) model is employed. An active particle can only have two states, either susceptible or infected.…”

“…9 Besides, it can also suppress or promote the spreading dynamics on the temporal networks. [10][11][12][13][14][15] Vazquez et al 16 analyzed the spreading dynamics of a temporal contact network. They found that the interevent time distribution of the edges exhibits a heavy tail, which indicates the system's heterogeneity.…”

Burstiness and information spreading in the active particles systems

“…As we have mentioned, that the temporal inhomogeneities can strongly affect, i.e., promoting or slowing down, the spreading dynamics on the temporal networks [10][11][12][13][14][15].…”

“…When the bursty nature of a signal is identified, it can produce aging effects, resulting in the breaking of the time translation invariance of the degree distribution, in the corresponding time-integrated network [9]. Besides, it can also suppress or promote the spreading dynamics on the temporal networks [10][11][12][13][14][15]. Vazquz et al [16] analyzed the spreading dynamics on the temporal contact network, of which the interevent time distribution of the edges exhibits a heavy-tail, i.e., it is a temporally inhomogeneous bursty contact process.…”

Evaluation and Comparison of Diffusion Models with Motif Features

“…By recording the process of spreading the influence/information in a community, the temporal network provides a comprehensive description of time-dependent network structures such as social networks, traffic networks, communication networks, logistics networks, etc. Such comprehensiveness paves the way to many studies on the timedependency of networks [1][2] [3].…”