Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges

Michalski, Radosław; Kazienko, Przemysław

doi:10.1007/978-3-319-15916-4_14

Cited by 12 publications

(6 citation statements)

References 74 publications

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“…Ideally, seed selection techniques construct this set as the one that has the highest potential for influencing nodes in the network. Due to the hardness of this problem, this implies using heuristics, either basing on the network structure or by exploiting the attributes of nodes [33]. As the purpose of this work is not to evaluate the performance of particular seed selection strategies, but to investigate the capabilities of sequential seeding in a temporal network setting, we did choose a single heuristic that is based on a degree of nodes.…”

Section: Seed Selectionmentioning

confidence: 99%

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Effective Influence Spreading in Temporal Networks With Sequential Seeding

2020

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The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence. To tackle this and similar challenges, more than a decade ago, researchers started to investigate the influence maximisation problem. The challenge is to find the best set of initially activated seed nodes in order to maximise the influence spread in networks. In typical approach we will activate all seeds in single stage, at the beginning of the process, while in this work we introduce and evaluate a new approach for seeds activation in temporal networks based on sequential seeding. Instead of activating all nodes at the same time, this method distributes the activations of seeds, leading to higher ranges of influence spread. The results of experiments performed using real and randomised networks demonstrate that the proposed method outperforms single stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal networks are an adequate choice for modelling dynamic processes, the results of this work can be interpreted as encouraging to apply temporal sequential seeding for real world cases, especially knowing that more sophisticated seed selection strategies can be implemented by using the seed activation strategy introduced in this work.

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Section: Seed Selectionmentioning

confidence: 99%

“…This is especially the case for models that base on independent activations, since for threshold-like models, e.g. linear threshold having a committed neighbourhood is required to activate a node and different strategies should be applied there, as shown in [33], [34].…”

Section: Introductionmentioning

confidence: 99%

Effective Influence Spreading in Temporal Networks With Sequential Seeding

2020

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“…The original influence maximization problem [10] considered static social networks, and researchers followed that path when proposing new algorithms for tackling it. Moreover, they focused on single stage seeding, i.e., the best allocation of the budget assuming its immediate spending [20] without any further support. However, in many realistic scenarios, this is not the only way of managing the budget and recent studies started to investigate how distributing a budget over time influences the outcome of the process.…”

Section: Related Workmentioning

confidence: 99%

Seeds Buffering for Information Spreading Processes

Jankowski¹,

Bródka²,

Michalski³

et al. 2017

Lecture Notes in Computer Science

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Abstract. Seeding strategies for influence maximization in social networks have been studied for more than a decade. They have mainly relied on the activation of all resources (seeds) simultaneously in the beginning; yet, it has been shown that sequential seeding strategies are commonly better. This research focuses on studying sequential seeding with buffering, which is an extension to basic sequential seeding concept. The proposed method avoids choosing nodes that will be activated through the natural diffusion process, which is leading to better use of the budget for activating seed nodes in the social influence process. This approach was compared with sequential seeding without buffering and single stage seeding. The results on both real and artificial social networks confirm that the buffer-based consecutive seeding is a good trade-off between the final coverage and the time to reach it. It performs significantly better than its rivals for a fixed budget. The gain is obtained by dynamic rankings and the ability to detect network areas with nodes that are not yet activated and have high potential of activating their neighbours.

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“…If we use the traditional internet and wireless ad hoc network algorithm example, the method based on topology information to calculate the route is not accurate. Furthermore, in the social network, nodes are people in a certain social relationship [6], and information interaction and mobile mode have a certain degree of sociality [7]. Therefore, in a social opportunistic network with a human terminal carrier, human behavior characteristics and social attributes are the essential characteristics that are different from other mobile network modes [8].…”

Section: Introductionmentioning

confidence: 99%

Data Transmission and Management Based on Node Communication in Opportunistic Social Networks

Xiao

2020

Symmetry

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Due to the rapid popularization of various short distance communication mobile devices, the use scenarios of opportunistic networks are increasing day by day. However, in opportunistic networks, because of the complexity of community structure, many methods lack of symmetry between application and theoretical research. Thus, the connection strength between nodes is different, and the degree of message diffusion is different. If the above factors cannot be accurately estimated and analyzed, and effective data forwarding and scheduling strategies cannot be formulated, the delivery ratio will be low, the delay will be relatively high, and the network overhead will be large. In light of improving symmetry problems in opportunistic networks, this paper establishes the message duplicate adaptive allocation and spray routing strategy (MDASRS) algorithm model, measures the connection strength between nodes through social pressure, and estimates the diffusion of current messages in the network through the probability of messages leaving the current node successfully, so as to develop the self-adaptive control replication transmission mode and achieve the effect of reducing the network burden and network overhead. This is done through experiments and comparison of social network algorithms, comparing the MDASRS with Epidemic, Spray and Wait, and EIMST algorithms. The experiment results showed that this method improves the cache utilization of nodes, reduces data transmission delay, and improves the network’s overall efficiency.

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Maximizing Social Influence in Real-World Networks—The State of the Art and Current Challenges

Cited by 12 publications

References 74 publications

Effective Influence Spreading in Temporal Networks With Sequential Seeding

Effective Influence Spreading in Temporal Networks With Sequential Seeding

Seeds Buffering for Information Spreading Processes

Data Transmission and Management Based on Node Communication in Opportunistic Social Networks

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