Influence Maximization for Fixed Heterogeneous Thresholds

Abstract

Influence Maximization is a NP-hard problem of selecting the optimal set of influencers in a network. Here, we propose two new approaches to influence maximization based on two very different metrics. The first metric, termed Balanced Index (BI), is fast to compute and assigns top values to two kinds of nodes: those with high resistance to adoption, and those with large out-degree. This is done by linearly combining three properties of a node: its degree, susceptibility to new opinions, and the impact its activation will have on its neighborhood. Controlling the weights between those three terms has a huge impact on performance. The second metric, termed Group Performance Index (GPI), measures performance of each node as an initiator when it is a part of randomly selected initiator set. In each such selection, the score assigned to each teammate is inversely proportional to the number of initiators causing the desired spread. These two metrics are applicable to various cascade models; here we test them on the Linear Threshold Model with fixed and known thresholds. Furthermore, we study the impact of network degree assortativity and threshold distribution on the cascade size for metrics including ours. The results demonstrate our two metrics deliver strong performance for influence maximization.

Influencer identification in dynamical complex systems

“…In a recent study by Karampourniotis et al, two different metrics were proposed to find influencers for LTMs with fixed heterogeneous thresholds [192]. The first metric, termed Balanced Index (BI), tends to select nodes with high resistance to activation and those with large out-degree.…”

“…Select nodes with high resistance to activation and with large out-degree, fast to compute [192] Group Performance Index (GPI)…”

“…Measure performance of each node as a seed when it is a part of randomly selected seed set [192] Beliefpropagation algorithm Large deviations of LTM dynamics, consider cost and revenue, Max-Sum equations, solved by iteration until convergence [47] Survey propagation like algorithm Near-optimal performance, solved by iteration until convergence, applied to random regular graphs [100] Collective Influence in Threshold Model (CI-TM)…”

Realistic influence maximization based on followers score and engagement grade on instagram

“…Engagement is one of the most prevalent factors in IM, with engagement forms such as conversation content and reply [14], assortativity, influence on second neighborhoods [15], network topology [21], silent users [22]. However, these studies were either relied on assumptions [15] or only worked in a limited environment [14]. Furthermore, the influence spread remains to be the most popular benchmark method, which remains theoretical.…”

“…Recent studies are more focused on making IM more realistic. Incorporation of various factors have been studied, such as influence susceptibility [9], sentiment [10,11], freeloaders [12], targeted ads [13], and engagement [14,15]. There are also bandit-based IM algorithms [16,17] 1047 typically uses feedbacks from actual data.…”