A novel meta‐heuristic approach for influence maximization in social networks

Chatterjee, Bitanu; Bhattacharyya, Trinav; Ghosh, Kushal Kanti; Chatterjee, Agneet; Sarkar, Ram

doi:10.1111/exsy.12676

Cited by 10 publications

(4 citation statements)

References 61 publications

(82 reference statements)

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“…Tests are undertaken on three underlying networks of varied sizes. This helps in examining the performance of the presented technique: the NetHEPT Network (large), the NetScience Network (medium) and the Dolphin Network (small) [12]. Detailed data description is provided in Table 2 when IM-CAβCRO is evaluated using 2-hop influence spread.…”

Section: Datasetsmentioning

confidence: 99%

“…We have varied the seed size for comparison purposes. The performance of IM-CAβCRO is compared with CELF [31], CELF++ [23], Degree centrality [26], Page Rank [10], Random [27], CMA-IM [21], DPSO [22], Infuence Maximization using Social Spider Optimization (IM-SSO) [42], Ant-colony optimization (ACO-IM) [43] and HFSLA-IM [12]. The obtained results are depicted in Fig 4 . As it is obvious from the graphs, IM-CAβCRO performs better compared to other techniques.…”

Section: Comparison Based On 2-hop Influence Spreadmentioning

confidence: 99%

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Influence Maximization using Community Detection aided with Adaptive β Coral Reefs Optimization

Ahmed

Maulik

2022

Preprint

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The purpose of influence maximization in a cascade model is to extricate a group of hubs from underlying network that can maximize propagation. Greedy methods may be used to perform influence maximization. However, as it has ahigh computational cost, it becomes inefficient. Centrality-based heuristic methods can be considered as a replacement. But it gets trapped in local optima. To address these issues, a framework has been introduced which uses community detection, tries to find the most significant nodes from each community using closeness centrality. Then, it employs Coral Reefs Optimization to determine the best solutions, i.e., the most important nodes that maximize propagation. Adaptive β-Hill Climbing has been used. It acts as a local search to avoid getting caught in local optima. A few datasets were used to evaluate the proposed framework’ scalabilities. Moreover, the outcomes were compared to several state-of-the-art techniques based on 2-hop influence spread and Monte-Carlo simulations.

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Section: Datasetsmentioning

confidence: 99%

Section: Comparison Based On 2-hop Influence Spreadmentioning

confidence: 99%

Influence Maximization using Community Detection aided with Adaptive β Coral Reefs Optimization

Ahmed

Maulik

2022

Preprint

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“…Researchers have also leveraged local search techniques 16 in combination with global optimizers to enhance exploitation of the search space and thereby achieve a better quality solution. Chatterjee et al 71 proposed a novel local search embedded meta‐heuristic framework for influence maximization in social networks. Yousri et al 72 combined Flower Pollination Algorithm with a fractional‐order calculus based local search method for image segmentation.…”

Section: Related Workmentioning

confidence: 99%

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Chattopadhyay

Kundu

Singh

et al. 2021

Int J of Intelligent Sys

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Pneumonia is a major cause of death among children below the age of 5 years, globally. It is especially prevalent in developing and underdeveloped nations where the risk factors for the disease such as unhygienic living conditions, high levels of pollution and overcrowding are higher. Radiological examination (usually X-ray scans) is conducted to detect pneumonia, yet it is prone to subjective variability and can lead to disagreements among different radiologists. To detect traces of pneumonia from X-ray images, a more robust method is therefore required, which can be achieved by using a computer-aided diagnosis (CAD) system. In this study, we develop a two-stage framework, using the combination of deep learning and optimization algorithms, which is both accurate and timeefficient. In its first stage, the proposed framework extracts feature using a customized deep learning model called DenseNet-201 following the concept of transfer learning to cope with the scanty available data. In the second stage, we then reduce the feature dimension using an improved sine cosine algorithm equipped with adaptive beta hill climbing-based local search algorithm. The optimized feature subset is utilized for the classification of "Pneumonia" and "Normal" X-ray images using a support vector machines classifier. Upon an evaluation on a publicly available data set, the proposed method demonstrates the

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“…However, since the greedy‐based algorithm needs to compute each user's marginal influence in each iteration, most approaches for this direction take long running time and have low efficiency. The other direction is the heuristic‐based algorithms and their extensions which could increase the efficiency while they usually have relatively small influence spread (Boroujeni & Soleimani, 2023; Chatterjee et al, 2021; Chen et al, 2009; He et al, 2019; Jiang et al, 2011; Kim et al, 2013; Ma & Liu, 2019; Tang et al, 2017). Although, many researchers have explored the influence maximization problem, there still lacks an efficient and accurate scheme for the influence maximization problem.…”

Section: Introductionmentioning

confidence: 99%

Exploiting community and structural hole spanner for influence maximization in social networks

Li,

Chen

2023

Expert Systems

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Viral marketing is a frequently used social marketing strategy which aims to promote products on various social media. In order to devise an efficient viral marketing strategy, influence maximization problem is widely studied in social networks. Influence maximization problem tries to find a set of influential users who could influence social users maximally. However, existed influence maximization algorithms could not satisfy the needs of social marketers well. In social networks, community structure is an important feature where social users are closely connected together in groups, and structural hole spanners are those users who bridge different communities. In order to balance the effectiveness and efficiency of viral marketing in social networks, in this paper, we present to exploit the community structure and structural hole spanners in social networks for solving the influence maximization problem. Different to traditional algorithms, we devise a strategy to approximate social influences of social users to find seeds efficiently. In particular, for an internal user inside a community in social networks, we utilize his social influence to members inside this community to approximate his social influence in the whole social network. For a structural hole spanner who bridges multiple communities, we utilize his social influence to members in these communities to approximate his social influence in the whole network. Based on the approximate social influence, information diffusion model and greedy framework, we propose a Community and Structural Hole Spanner based Greedy (CSHS‐G) algorithm which devises several influence lists to store social influences of users and fast computation scheme to find seeds. Besides, to further improve the efficiency, we propose a Community and Structural Hole Spanner based Heuristic (CSHS‐H) algorithm which approximates social influences of structural hole spanners by utilizing their 2‐hops influences. We conduct a comprehensive performance evaluation on the crawled real‐world data set. Experimental results show that, compared to all of the baseline algorithms, our proposed algorithms not only have high efficiency, but also could guarantee larger influence spread.

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A novel meta‐heuristic approach for influence maximization in social networks

Cited by 10 publications

References 61 publications

Influence Maximization using Community Detection aided with Adaptive β Coral Reefs Optimization

Influence Maximization using Community Detection aided with Adaptive β Coral Reefs Optimization

Pneumonia detection from lung X‐ray images using local search aided sine cosine algorithm based deep feature selection method

Exploiting community and structural hole spanner for influence maximization in social networks

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