Social Influence Maximization in Hypergraphs

This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

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Preprocessing Rules for Target Set Selection in Complex Networks

10.5753/brasnam.2020.11167 ◽

2020 ◽

Author(s):

Renato Silva Melo ◽

André Luís Vignatti

Keyword(s):

Complex Networks ◽

Power Law ◽

Real World ◽

Degree Distribution ◽

Topological Properties ◽

Input Graph ◽

Target Set ◽

Law Degree ◽

Entire Network ◽

Target Set Selection

In the Target Set Selection (TSS) problem, we want to find the minimum set of individuals in a network to spread information across the entire network. This problem is NP-hard, so find good strategies to deal with it, even for a particular case, is something of interest. We introduce preprocessing rules that allow reducing the size of the input without losing the optimality of the solution when the input graph is a complex network. Such type of network has a set of topological properties that commonly occurs in graphs that model real systems. We present computational experiments with real-world complex networks and synthetic power law graphs. Our strategies do particularly well on graphs with power law degree distribution, such as several real-world complex networks. Such rules provide a notable reduction in the size of the problem and, consequently, gains in scalability.

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Latency-Bounded Target Set Selection in Signed Networks

Algorithms ◽

10.3390/a13020032 ◽

2020 ◽

Vol 13 (2) ◽

pp. 32 ◽

Cited By ~ 1

Author(s):

Miriam Di Ianni ◽

Giovanna Varricchio

Keyword(s):

Social Networks ◽

Diffusion Process ◽

Large Fraction ◽

New Product ◽

Negative Influence ◽

Viral Marketing ◽

Dynamic Processes ◽

Diffusion Of Information ◽

Target Set ◽

Target Set Selection

It is well-documented that social networks play a considerable role in information spreading. The dynamic processes governing the diffusion of information have been studied in many fields, including epidemiology, sociology, economics, and computer science. A widely studied problem in the area of viral marketing is the target set selection: in order to market a new product, hoping it will be adopted by a large fraction of individuals in the network, which set of individuals should we “target” (for instance, by offering them free samples of the product)? In this paper, we introduce a diffusion model in which some of the neighbors of a node have a negative influence on that node, namely, they induce the node to reject the feature that is supposed to be spread. We study the target set selection problem within this model, first proving a strong inapproximability result holding also when the diffusion process is required to reach all the nodes in a couple of rounds. Then, we consider a set of restrictions under which the problem is approximable to some extent.

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Research on the Path of Network Opinion Expression in AI Environment for College Students

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/4360792 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Yue Zhu ◽

Muhammad Talha

Keyword(s):

Diffusion Process ◽

Information Diffusion ◽

Topic Model ◽

Threshold Model ◽

Opinion Leader ◽

Group Model ◽

Two Stage ◽

Model Based ◽

Linear Threshold ◽

Linear Threshold Model

Network interaction has evolved into a grouping paradigm as civilization has progressed and artificial intelligence technology has advanced. This network group model has quickly extended communication space, improved communication content, and tailored to the demands of netizens. The fast growth of the network community on campus can assist students in meeting a variety of communication needs and serve as a vital platform for their studies and daily lives. It is investigated how to extract opinion material from comment text. A strategy for extracting opinion attitude words and network opinion characteristic words from a single comment text is offered at a finer level. The development of a semiautonomous domain emotion dictionary generating technique improves the accuracy of opinion and attitude word extraction. This paper proposes a window-constrained Latent Dirichlet Allocation (LDA) topic model that improves the accuracy of extracting network opinion feature words and ensures that network opinion feature words and opinion attitude words are synchronized by using the location information of opinion attitude words. The two-stage opinion leader mining approach and the linear threshold model based on user roles are the subjects of model simulation tests in this study. It is demonstrated that the two-stage opinion leader mining method suggested in this study can greatly reduce the running time while properly finding opinion leaders with stronger leadership by comparing the results with existing models. It also shows that the linear threshold model based on user roles proposed in this paper can effectively limit the total number of active users who are activated multiple times during the information diffusion process by distinguishing the effects of different user roles on the information diffusion process.

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