Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

TL;DR

This paper compares static and temporal approaches for seed selection in influence spread within social networks, demonstrating that temporal strategies with higher granularity and recent data weighting lead to more influenced nodes.

Contribution

It introduces a temporal network construction method with forgetting mechanisms for seed selection, showing its superiority over static methods in influence spread.

Findings

Temporal approach outperforms static in influence spread.

Higher temporal granularity yields more influenced nodes.

Exponential forgetting with outdegree measure is most effective.

Abstract

The problem of finding optimal set of users for influencing others in the social network has been widely studied. Because it is NP-hard, some heuristics were proposed to find sub-optimal solutions. Still, one of the commonly used assumption is the one that seeds are chosen on the static network, not the dynamic one. This static approach is in fact far from the real-world networks, where new nodes may appear and old ones dynamically disappear in course of time. The main purpose of this paper is to analyse how the results of one of the typical models for spread of influence - linear threshold - differ depending on the strategy of building the social network used later for choosing seeds. To show the impact of network creation strategy on the final number of influenced nodes - outcome of spread of influence, the results for three approaches were studied: one static and two temporal with different granularities, i.e. various number of time windows. Social networks for each time window encapsulated dynamic changes in the network structure. Calculation of various node structural measures like degree or betweenness respected these changes by means of forgetting mechanism - more recent data had greater influence on node measure values. These measures were, in turn, used for node ranking and their selection for seeding. All concepts were applied to experimental verification on five real datasets. The results revealed that temporal approach is always better than static and the higher granularity in the temporal social network while seeding, the more finally influenced nodes. Additionally, outdegree measure with exponential forgetting typically outperformed other time-dependent structural measures, if used for seed candidate ranking.