CSR: A community based spreaders ranking algorithm for influence maximization in social networks

Kumar, Sanjay; Gupta, Aaryan; Khatri, Inder

doi:10.1007/s11280-021-00996-y

Cited by 27 publications

(6 citation statements)

References 42 publications

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“…By utilizing the presence of community structures in networks, the information propagation and influence spread in the system can be enhanced [24], [25]. In recent years, many community-based influence maximization algorithms are proposed.…”

Section: Related Workmentioning

confidence: 99%

Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking

Kumar

Panda

2023

IEEE Trans. Ind. Inf.

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Finding influential nodes reshuffles the very notion of linear paths in business processes and replaces it with networks of business value within a smart enterprise system. There are many existing algorithms for identifying influential nodes with certain limitations for applying in large-scale networks. In this paper, we propose a community structure with an Integrated Features Ranking (CIFR) algorithm to find influential nodes in the network. Firstly, we use the community detection algorithm to find communities in the system and then we rank the nodes of network based on three factors, namely-local ranking, gateway ranking, and community ranking, collectively termed as integrated features. Our algorithm intends to select influential nodes, which are both globally and locally optimal, leading to overall high information propagation. We perform the experimental results on total eight networks using various evaluation parameters. The obtained results validate superior performance against contemporary algorithms adding value to smart enterprises.

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Section: Related Workmentioning

confidence: 99%

Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking

Kumar

Panda

2023

IEEE Trans. Ind. Inf.

Self Cite

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“…In 26 , the authors adopted different models and considered the spreading of influence in viral marketing to estimate the final fraction of buyers. The notions of extracting community structures and identifying the most influential nodes were also investigated by researchers in recent years such as a recent study by Kumar et al 27 . In this study, the authors considered bridges nodes and communities and presented a Communities-based Spreader Ranking algorithm.…”

Section: Related Workmentioning

confidence: 99%

A new stochastic diffusion model for influence maximization in social networks

Rezvanian

Vahidipour

Meybodi

2023

Sci Rep

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Most current studies on information diffusion in online social networks focus on the deterministic aspects of social networks. However, the behavioral parameters of online social networks are uncertain, unpredictable, and time-varying. Thus, deterministic graphs for modeling information diffusion in online social networks are too restrictive to solve most real network problems, such as influence maximization. Recently, stochastic graphs have been proposed as a graph model for social network applications where the weights associated with links in the stochastic graph are random variables. In this paper, we first propose a diffusion model based on a stochastic graph, in which influence probabilities associated with its links are unknown random variables. Then we develop an approach using the set of learning automata residing in the proposed diffusion model to estimate the influence probabilities by sampling from the links of the stochastic graph. Numerical simulations conducted on real and artificial stochastic networks demonstrate the effectiveness of the proposed stochastic diffusion model for influence maximization.

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“…Various real-life networks like online social networks, biological networks, communication networks, collaboration networks are examples of complex networks consisting of a large number of nodes or entities and intricate relationships between the nodes [1]. Complex network analysis or network science offers a plethora of research problems like node classification [2], community detection [3], link prediction [4], influence maximization [5], information diffusion [6], and many more. Community detection is one of the widely studied topics in the fields of network science, which aims at grouping nodes of a network into clusters or modules such that each cluster has a dense internal connection and is sparsely connected with other clusters [7,8].…”

Section: Introductionmentioning

confidence: 99%

Community detection in complex networks using stacked autoencoders and crow search algorithm

2022

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The presence of community structures in complex networks reveals meaningful insights about such networks and their constituent entities. Finding groups of related nodes based on mutual interests, common features, objectives, or interactions in a network is known as community detection. In this paper, we propose a novel Stacked Autoencoder-based deep learning approach augmented by the Crow Search Algorithm (CSA) based k-means clustering algorithm to uncover community structure in complex networks. As per our approach, firstly, we generate a modularity matrix for the input graph. The modularity matrix is then passed through a series of stacked autoencoders to reduce the dimensionality of the matrix while preserving the topology of the network and improving the computational time of the proposed algorithm. The obtained matrix is then provided as an input to a modified k-means clustering algorithm augmented with the crow search optimization to detect the communities. We use Crow Search Algorithm based optimization to generate the initial centroids for the k-means algorithm instead of generating them randomly. We perform extensive experimental analysis on several real-world and synthetic datasets and evaluate various performance metrics. We compare

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CSR: A community based spreaders ranking algorithm for influence maximization in social networks

Cited by 27 publications

References 42 publications

Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking

Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking

A new stochastic diffusion model for influence maximization in social networks

Community detection in complex networks using stacked autoencoders and crow search algorithm

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