influence maximization using time delay based harmonic centrality in social networks

With the extension of social networks, research on influence maximization (im) in timesensitive graphs has increased in recent years. im is a problem to find a seed set with k nodes to maximize the information propagation range in the graph. most of the research in this area consists of greedy, heuristic, metaheuristic methods. however, most of these methods ignore the timesensitivity to propagation delay and duration. the preceding timesensitive centrality measures as a part of heuristic approaches take the propagation delay but only consider the nodes locally so that each graph node considers only the direct neighbors. based on the above analysis, this article focuses on the timesensitive im problem. here, a propagation value for each path in the graph is defined in terms of the probability of affecting through the edge and freshness amount of the edge. to solve the problem, we propose timesensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. therefore, four measures of timesensitive closeness centrality (tscloseness), timesensitive harmonic (tsharmonic), timesensitive decay centrality (tsdecay), and timesensitive eccentricity centrality (tseccentricity) were proposed. the experiments on five datasets demonstrate the efficiency and influence performance of the tsharmonic measure on evaluation metrics.

Influence Maximization using Time Delay based Harmonic Centrality in Social Networks

With the extension of social networks, research on influence maximization (IM) in timesensitive graphs has increased in recent years. IM is a problem to find a seed set with k nodes to maximize the information propagation range in the graph. Most of the research in this area consists of greedy, heuristic, metaheuristic methods. However, most of these methods ignore the timesensitivity to propagation delay and duration. The preceding timesensitive centrality measures as a part of heuristic approaches take the propagation delay but only consider the nodes locally so that each graph node considers only the direct neighbors. Based on the above analysis, this article focuses on the timesensitive IM problem. Here, a propagation value for each path in the graph is defined in terms of the probability of affecting through the edge and freshness amount of the edge. To solve the problem, we propose timesensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. Therefore, four measures of timesensitive closeness centrality (TSCloseness), timesensitive harmonic (TSHarmonic), timesensitive decay centrality (TSDecay), and timesensitive eccentricity centrality (TSEccentricity) were proposed. The experiments on five datasets demonstrate the efficiency and influence performance of the TSHarmonic measure on evaluation metrics.