In-time estimation for influence maximization in large-scale social networks

Liu, Xiaodong; Li, Shanshan; Liao, Xiangke; Wang, Lei; Wu, Qingbo

doi:10.1145/2181176.2181179

Cited by 13 publications

(8 citation statements)

References 7 publications

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“…MixGreedy is an improved greedy algorithm on the IC model proposed by Chen et al in [7]. ESMCE is a power-law exponent supervised estimation approach designed by Liu et al in [25]. MIA is a heuristic that uses local arborescence structures of each node to approximate the influence propagation [8].…”

Section: Methodsmentioning

confidence: 99%

“…S = S ∪ {v} 5: end for Independent Cascade (IC) Model. IC model is a popular diffusion model that has been well-studied in [7,17,19,25,32]. Given an initial set S, the diffusion process of IC model works as follows.…”

Section: Preliminaries On Influence Maximizationmentioning

confidence: 99%

“…Existing researches and solutions on influence maximization focus mainly on developing effective and efficient algorithms on a given static social network. Although one could possibly run any of the static influence maximization methods, such as [7,8,24,25], to find the new top-K influential individuals when the network is updated, this approach has some inherent drawbacks that cannot be neglected: (1) the running time of a specific static method can be extremely long and unacceptable especially on large-scale social networks, and (2) whenever the network topology is changed, we need to recalculate the influence spreads for all the nodes which leads to very high costs. Can we quickly and efficiently identify the influential nodes in evolving social networks?…”

Section: Introductionmentioning

confidence: 99%

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On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Liu

Liao

et al. 2017

Complexity

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Identifying the most influential individuals can provide invaluable help in developing and deploying effective viral marketing strategies. Previous studies mainly focus on designing efficient algorithms or heuristics to find top-K influential nodes on a given static social network. While, as a matter of fact, real-world social networks keep evolving over time and a recalculation upon the changed network inevitably leads to a long running time, significantly affecting the efficiency. In this paper, we observe from real-world traces that the evolution of social network follows the preferential attachment rule and the influential nodes are mainly selected from high-degree nodes. Such observations shed light on the design of IncInf, an incremental approach that can efficiently locate the top-K influential individuals in evolving social networks based on previous information instead of calculation from scratch. In particular, IncInf quantitatively analyzes the influence spread changes of nodes by localizing the impact of topology evolution to only local regions, and a pruning strategy is further proposed to effectively narrow the search space into nodes experiencing major increases or with high degrees. We carried out extensive experiments on real-world dynamic social networks including Facebook, NetHEPT, and Flickr. Experimental results demonstrate that, compared with the state-of-the-art static heuristic, IncInf achieves as much as 21× speedup in execution time while maintaining matching performance in terms of influence spread.

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Section: Methodsmentioning

confidence: 99%

Section: Preliminaries On Influence Maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

Liu

Liao

et al. 2017

Complexity

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“…However, this method is not suggested to be applicable to various social networks. Liu et al 21 developed an algorithm to identify the top-k influential nodes in the network. Such an algorithm estimates the number of nodes based on the power-law exponent of the social network, while calculating the sub-nodes satisfying the accuracy requirements iteratively by employing the Monte Carlo method, that is, the top-k influential nodes.…”

Section: Related Workmentioning

confidence: 99%

A relationship matrix resolving model for identifying vital nodes based on community in opportunistic social networks

Luo

Yang

2021

Trans Emerging Tel Tech

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The introduction of the 5G Internet and big data communication into human society underpins the opportunistic social networks (OSNs) to survive. The storage-carry-forward mechanism refers to a data transmission strategy of OSNs, seeking any data transmission opportunities to communicate in the mobile process. Accordingly, identifying influential nodes is considered critical to OSNs and an essential prerequisite for developing efficient collaborative transmission algorithms. According to conventional OSNs, however, the node identification methods exhibit several defects (eg, low recognition accuracy, high time complexity, and poor performance in complexity networks). To address the mentioned defects, this study builds a relationship matrix resolving model to identify vital nodes by complying with community, which is capable of effectively identifying influential nodes in the network. The proposed model iterates by drawing upon the community features in the network to yield the optimal solution that satisfies the set condition, that is, the influential nodes in the community. As revealed from the experimental results, the method exhibits high performance.

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“…Addressed the scalability also, heuristic algorithm designed by Wang et al [236] can be easily scalable to millions of nodes and edges in their experiments under IC model. A power-law exponent supervised Monte Carlo method is utilized to efficiently estimate the influence spread for nodes with specified precision by sampling only part of child nodes [161].…”

Section: 2mentioning

confidence: 99%

Influence analysis: A survey of the state-of-the-art

Han¹,

Li²

2018

Mathematical Foundations of Computing

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Online social networks have seen an exponential growth in number of users and activities recently. The rapid proliferation of online social networks provides rich data and infinite possibilities for us to analyze and understand the complex inherent mechanism which governs the evolution of the new online world. This paper summarizes the state-of-art research results on social influence analysis in a broad sense. First, we review the development process of influence analysis in social networks based on several basic conceptions and features in a social aspect. Then the online social networks are discussed. After describing the classical models which simulate the influence spreading progress, we give a bird's eye view of the up-to-date literatures on influence diffusion models and influence maximization approaches. Third, we present the applications including web services, marketing, and advertisement services which based on the influence analysis. At last, we point out the research challenges and opportunities in this area for both industry and academia reference. 201 202 MENG HAN AND YINGSHU LIachieved. Even so, prior to the Internet, quantitative data of social networks were scanty and the further influence analysis in social networks was in the slow-lane. In 2007, Nicholas et al. [47] published their years of research results based on the historical data from the spreading of obesity over 32 years. From the same research filed, David et al. [11] proposed another idea about the spread of obesity in social networks based on the simulations which further considered the group effect in obesity spreading. Both works tried to explore how the influence diffusion in social networks affects obesity. In their model, individuals' influence over each other rely on food intake and physical activities [70]. Since other models consider obesity as a "contagious" phenomenon that can be caught if most social contracts are deemed obese, the interaction of social networks with environmental factors could not be explored. It was not accounted for in the general model where the social networks were proposed as a means to mitigate the obesity epidemic. Many other research results have been obtained recently, such as smoking behavior [48][52], happiness [68], and loneliness [25][117] which spread along a social network over time.At an American high school, Salath et al.[207] obtained high-resolution data of close proximity interactions during a typical day, and their work helps with the reconstruction of a social network for infectious disease transmission by using wireless sensor network technology [246,98]. Through simulations, they showed that targeted immunization using the contact-network data is much more effective than random immunization. Stehle et al. [216] report a similar result like Salath [207] in a French primary school. The team headed by Stehle also provided several public-health implications of infectious diseases by collecting a period of history data from their experiments. By analyzing the real experiments in two ...

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In-time estimation for influence maximization in large-scale social networks

Cited by 13 publications

References 7 publications

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

On the Shoulders of Giants: Incremental Influence Maximization in Evolving Social Networks

A relationship matrix resolving model for identifying vital nodes based on community in opportunistic social networks

Influence analysis: A survey of the state-of-the-art

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