Signed-PageRank: An Efficient Influence Maximization Framework for Signed Social Networks

@article{Yin2021SignedPageRankAE,
  title={Signed-PageRank: An Efficient Influence Maximization Framework for Signed Social Networks},
  author={Xiaoyan Yin and Xiao Hu and Yanjiao Chen and Xu Yuan and Baochun Li},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  volume={33},
  pages={2208-2222}
}
Influence maximization in social networks is of great importance for marketing new products. Signed social networks with both positive (friends) and negative (foes) relationships pose new challenges and opportunities, since the influence of negative relationships can be leveraged to promote information propagation. In this paper, we study the problem of influence maximization for advertisement recommendation in signed social networks. We propose a new framework to characterize the information… 
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11 Citations
Background Citations
5
Methods Citations
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11 Citations

Citation Type

Citation Type

Net positive influence maximization in signed social networks
TLDR
The concept of net positive influence is introduced and an improved R-Greedy algorithm is proposed to solve the NPIM problem for signed social networks, to select the seed set with as much positive influence as possible and as less negative influence as Possible.
Time-sensitive Positive Influence Maximization in signed social networks
Parallel social behavior-based algorithm for identification of influential users in social network
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This paper introduces a new parallel algorithm, named PSAIIM, for identification of influential users in social network, which uses the community structure to apply perfect parallelism to the CPU architecture of the machines to compute an optimal set of influential nodes.
POLE: Polarized Embedding for Signed Networks
TLDR
POLE (POLarized Embedding for signed networks), a signed embedding method for polarized graphs that captures both topological and signed similarities jointly via signed autocovariance, significantly outperforms state-of-the-art methods in signed link prediction, particularly for negative links with gains of up to one order of magnitude.
A Novel Greedy FluidSpread Algorithm With Equilibrium Temperature for Influence Diffusion in Social Networks
TLDR
In this article, the behavior of the users, such as the interactions and relationships of each user, and the content of a topic are modeled to the user interest vector and the topic distribution vector, respectively, to calculate the information acceptance probability.
ComPath: User Interest Mining in Heterogeneous Signed Social Networks for Internet of People
TLDR
A new proximity function that measures the similarity between users based on their interests/disinterests with respect to the relative popularity of these interests/ Disinterests among other users and a greedy community detection algorithm that detects communities of users with common interests with possible overlapping communities using the adaptive clique relaxation technique are proposed.
Spam Classification Based on Signed Network Analysis
TLDR
A signed-network-analysis-based spam classification method that assumes that the edge signs are highly likely to be determined by considering users’ social relationships, so there will be a substantial difference between the edge sign patterns of spammers and that of non-spammers.
Betweenness Approximation for Hypernetwork Dismantling with Hypergraph Neural Network
TLDR
This paper proposes a novel HyperNetwork Dismantling methods based on hypergraph neural network, called HND, which outperforms various baselines and is utilized to approximate the nodes’ betweenness in real world hypernetworks and further used for dismantling.
A Blockchain-Based Microgrid Data Disaster Backup Scheme in Edge Computing
TLDR
A microgrid data disaster backup scheme based on blockchain in edge computing environment and the traditional proof of authority (PoA) consensus mechanism is improved partially, and the improved PoA is used to make each node reach consensus and pack blocks on the chain.
Improved Evolution Algorithm that Guides the Direction of Individual Mutation for Influence Maximization in Social Networks
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