Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influen...
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Autor*in:	Tian, Shan [verfasserIn] Mo, Songsong [verfasserIn] Wang, Liwei [verfasserIn] Peng, Zhiyong [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Social network Influence maximization Graph embedding Reinforcement learning

Übergeordnetes Werk:	Enthalten in: Data science and engineering - Berlin : Springer, 2016, 5(2020), 1 vom: 28. Feb., Seite 1-11
Übergeordnetes Werk:	volume:5 ; year:2020 ; number:1 ; day:28 ; month:02 ; pages:1-11

Links:	Volltext

DOI / URN:	10.1007/s41019-020-00117-1

Katalog-ID:	SPR039036197

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520			\|a Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
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allfields	10.1007/s41019-020-00117-1 doi (DE-627)SPR039036197 (SPR)s41019-020-00117-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Tian, Shan verfasserin aut Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mo, Songsong verfasserin aut Wang, Liwei verfasserin aut Peng, Zhiyong verfasserin aut Enthalten in Data science and engineering Berlin : Springer, 2016 5(2020), 1 vom: 28. Feb., Seite 1-11 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:5 year:2020 number:1 day:28 month:02 pages:1-11 https://dx.doi.org/10.1007/s41019-020-00117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 1 28 02 1-11
spelling	10.1007/s41019-020-00117-1 doi (DE-627)SPR039036197 (SPR)s41019-020-00117-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Tian, Shan verfasserin aut Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mo, Songsong verfasserin aut Wang, Liwei verfasserin aut Peng, Zhiyong verfasserin aut Enthalten in Data science and engineering Berlin : Springer, 2016 5(2020), 1 vom: 28. Feb., Seite 1-11 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:5 year:2020 number:1 day:28 month:02 pages:1-11 https://dx.doi.org/10.1007/s41019-020-00117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 1 28 02 1-11
allfields_unstemmed	10.1007/s41019-020-00117-1 doi (DE-627)SPR039036197 (SPR)s41019-020-00117-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Tian, Shan verfasserin aut Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mo, Songsong verfasserin aut Wang, Liwei verfasserin aut Peng, Zhiyong verfasserin aut Enthalten in Data science and engineering Berlin : Springer, 2016 5(2020), 1 vom: 28. Feb., Seite 1-11 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:5 year:2020 number:1 day:28 month:02 pages:1-11 https://dx.doi.org/10.1007/s41019-020-00117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 1 28 02 1-11
allfieldsGer	10.1007/s41019-020-00117-1 doi (DE-627)SPR039036197 (SPR)s41019-020-00117-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Tian, Shan verfasserin aut Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mo, Songsong verfasserin aut Wang, Liwei verfasserin aut Peng, Zhiyong verfasserin aut Enthalten in Data science and engineering Berlin : Springer, 2016 5(2020), 1 vom: 28. Feb., Seite 1-11 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:5 year:2020 number:1 day:28 month:02 pages:1-11 https://dx.doi.org/10.1007/s41019-020-00117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 1 28 02 1-11
allfieldsSound	10.1007/s41019-020-00117-1 doi (DE-627)SPR039036197 (SPR)s41019-020-00117-1-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE Tian, Shan verfasserin aut Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework. Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213 Mo, Songsong verfasserin aut Wang, Liwei verfasserin aut Peng, Zhiyong verfasserin aut Enthalten in Data science and engineering Berlin : Springer, 2016 5(2020), 1 vom: 28. Feb., Seite 1-11 (DE-627)844076856 (DE-600)2842814-6 2364-1541 nnns volume:5 year:2020 number:1 day:28 month:02 pages:1-11 https://dx.doi.org/10.1007/s41019-020-00117-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 1 28 02 1-11
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source	Enthalten in Data science and engineering 5(2020), 1 vom: 28. Feb., Seite 1-11 volume:5 year:2020 number:1 day:28 month:02 pages:1-11
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authorswithroles_txt_mv	Tian, Shan @@aut@@ Mo, Songsong @@aut@@ Wang, Liwei @@aut@@ Peng, Zhiyong @@aut@@
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author	Tian, Shan
spellingShingle	Tian, Shan ddc 004 misc Social network misc Influence maximization misc Graph embedding misc Reinforcement learning Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
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topic_title	004 ASE Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization Social network (dpeaa)DE-He213 Influence maximization (dpeaa)DE-He213 Graph embedding (dpeaa)DE-He213 Reinforcement learning (dpeaa)DE-He213
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title	Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
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title_full	Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
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title_sort	deep reinforcement learning-based approach to tackle topic-aware influence maximization
title_auth	Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
abstract	Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
abstractGer	Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
abstract_unstemmed	Abstract Motivated by the application of viral marketing, the topic-aware influence maximization (TIM) problem has been proposed to identify the most influential users under given topics. In particular, it aims to find k seeds (users) in social network G, such that the seeds can maximize the influence on users under the specific query topics and diffusion model such as independent cascade (IC) or linear threshold (LT).This problem has been proved to be NP-hard, and most of the proposed techniques suffer from the efficiency issue due to the lack of generalization. Even worse, the design of these algorithms requires significant specialized knowledge which is hard to be understood and implemented. To overcome these issues, this paper aims to learn a generalized heuristic framework to solve TIM problems by meta-learning. To this end, we first propose two topic-aware social influence propagation models based on IC and LT model, respectively, which is conducive to better advertising injections. We then encode the feature of each node by a vector and introduce a model, called deep influence evaluation model , to evaluate the user influence under different circumstances. Based on this model, we can construct the solution according to the influence evaluations efficiently, rather than spending a high cost to compute the exact influence by considering the complex graph structure. We conducted experiments on generated graph instances and real-world social networks. The results show the superiority in performance and comparable quality of our framework.
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title_short	Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization
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up_date	2024-07-03T21:31:41.026Z
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Deep Reinforcement Learning-Based Approach to Tackle Topic-Aware Influence Maximization

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