رئیس دانشکده مهندسی برق و کامپیوترمجله مهندسی برق دانشگاه تبریز2008-779951320211023Influence Maximization using Time Delay based Harmonic Centrality in Social NetworksInfluence Maximization using Time Delay based Harmonic Centrality in Social Networks35937014274FAسلمانمختارزادهDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iranبهزادزمانی دهکردیDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran/ Department of Engineering, Shahrekord Branch, Islamic Azad University, Shahrekord, Iran0000-0002-2584-5734محمدمصلحDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, IranعلیبراتیDepartment of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran0000-0001-5132-5540Journal Article20211020With the extension of social networks, research on influence maximization (IM) in time-sensitive 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, meta-heuristic methods. However, most of these methods ignore the time-sensitivity to propagation delay and duration. The preceding time-sensitive 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 time-sensitive 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 time-sensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. Therefore, four measures of time-sensitive closeness centrality (TSCloseness), time-sensitive harmonic (TSHarmonic), time-sensitive decay centrality (TSDecay), and time-sensitive eccentricity centrality (TSEccentricity) were proposed. The experiments on five datasets demonstrate the efficiency and influence performance of the TSHarmonic measure on evaluation metrics.With the extension of social networks, research on influence maximization (IM) in time-sensitive 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, meta-heuristic methods. However, most of these methods ignore the time-sensitivity to propagation delay and duration. The preceding time-sensitive 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 time-sensitive 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 time-sensitive centrality measures that consider propagation value and both the direct and the indirect neighbors. Therefore, four measures of time-sensitive closeness centrality (TSCloseness), time-sensitive harmonic (TSHarmonic), time-sensitive decay centrality (TSDecay), and time-sensitive eccentricity centrality (TSEccentricity) were proposed. The experiments on five datasets demonstrate the efficiency and influence performance of the TSHarmonic measure on evaluation metrics.https://tjee.tabrizu.ac.ir/article_14274_09c413d370f8b5d4b8563690a142891b.pdf