Finding Influential Nodes in Social Networks Using Minimum k-Hop Dominating Set

Basuchowdhuri, Partha; Majumder, Subhashis

doi:10.1007/978-3-319-04126-1_12

Cited by 22 publications

(21 citation statements)

References 9 publications

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“…(We leave the proof to the reader.) Note that the choice of node x in Corollary 4 satisfies property (1). Second, the property (3) leads in a natural way to our recursive algorithm OptKhdset.…”

Section: Algorithm For Optimal K-hdsetmentioning

confidence: 97%

“…For the special case k = 1, the 1-hop dominating sets are the same as 1-tuple dominating (or simply dominating) sets. Both the problems of optimal k-hop dominating sets and k-tuple dominating sets are NP-complete [1] [4] because the problem is known to be NP-complete for k = 1. There is a significant literature about k-tuple dominating sets [6] [7], which is sometimes called simply k-dominating set.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A linear time algorithm for optimal k -hop dominating set of a tree

Kundu

Majumder²

2016

Information Processing Letters

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Section: Algorithm For Optimal K-hdsetmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A linear time algorithm for optimal k -hop dominating set of a tree

Kundu

Majumder²

2016

Information Processing Letters

Self Cite

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“…Traditional algorithms such as [3] have guaranteed performance but are usually time-consuming In particular, heuristic algorithms for MdDSP are commonly designed with greedy strategy. Basuchowdhuri and Majumder [1] propose a greedy heuristic algorithm for MdDSP that repetitively selects the node with maximum coverage among the remaining nodes. Nguyen et al [13] precompute the multi-hop coverage of each node and add a pre-optimization and a post-optimization phase, which empirically improves the speed of [1].…”

Section: Related Workmentioning

confidence: 99%

FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks

Ni¹,

Li²,

Li³

et al. 2021

Preprint

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Finding influential users in social networks is a fundamental problem with many possible useful applications. Viewing the social network as a graph, the influence of a set of users can be measured by the number of neighbors located within a given number of hops in the network, where each hop marks a step of influence diffusion. In this paper, we reduce the problem of IM to a budget-constrained d-hop dominating set problem (k-dDSP). We propose a unified machine learning (ML) framework, FastCover, to solve k-dDSP by learning an efficient greedy strategy in an unsupervised way. As one critical component of the framework, we devise a novel graph neural network (GNN) architecture, graph reversed attention network (GRAT), that captures the diffusion process among neighbors. Unlike most heuristic algorithms and concurrent ML frameworks for combinatorial optimization problems, FastCover determines the entire seed set from the nodes' scores computed with only one forward propagation of the GNN and has a time complexity quasi-linear in the graph size. Experiments on synthetic graphs and real-world social networks demonstrate that FastCover finds solutions with better or comparable quality rendered by the concurrent algorithms while achieving a speedup of over 1000x. The source code of our model is available at https://github.com/pqros/fastCover.

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“…Using six different online social networks, the authors experimentally proved that nodes belonging to the maximal K-truss subgraph have better diffusion behavior. Basuchowdhuri et al introduced an algorithm for detecting influential nodes using a minimum K-Hop dominating set [23]. Basuchowdhuri et al built their algorithm on the idea of deciding a maximum number of k-Hops to diffuse information among all the nodes of the graph.…”

Section: Related Workmentioning

confidence: 99%

Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

Wagih

Mokhtar

Ghoniemy

2019

IJGI

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Recently, social networks have shown huge potential in terms of collaborative web services and the study of peer influence as a result of the massive amount of data, datasets, and interrelations generated. These interrelations cannot guarantee the success of online social networks without ensuring the existence of trust between nodes. Detecting influential nodes improves collaborative filtering (CF) recommendations in which nodes with the highest influential capability are most likely to be the source of recommendations. Although CF-based recommendation systems are the most widely used approach for implementing recommender systems, this approach ignores the mutual trust between users. In this paper, a trust-based algorithm (TBA) is introduced to detect influential spreaders in social networks efficiently. In particular, the proposed TBA estimates the influence that each node has on the other connected nodes as well as on the whole network. Next, a Friend-of-Friend recommendation (FoF-SocialI) algorithm is addressed to detect the influence of social ties in the recommendation process. Finally, experimental results, performed on three large scale location-based social networks, namely, Brightkite, Gowalla, and Weeplaces, to test the efficiency of the proposed algorithm, are presented. The conducted experiments show a remarkable enhancement in predicting and recommending locations in various social networks.

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Finding Influential Nodes in Social Networks Using Minimum k-Hop Dominating Set

Cited by 22 publications

References 9 publications

A linear time algorithm for optimal k -hop dominating set of a tree

A linear time algorithm for optimal k -hop dominating set of a tree

FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social Networks

Exploring Trusted Relations among Virtual Interactions in Social Networks for Detecting Influence Diffusion

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