Influence maximization across heterogeneous interconnected networks based on deep learning

Keikha, Mohammad Mehdi; Rahgozar, Maseud; Asadpour, Masoud; Abdollahi, Mohammad

doi:10.1016/j.eswa.2019.112905

Cited by 55 publications

(15 citation statements)

References 10 publications

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“…While finding the optimal solution for influence maximization is NP-hard (Kempe, Kleinberg, and Tardos 2003), a simple greedy algorithm provides a 1 − 1/eapproximation guarantee under different cascade models such as Linear Threshold (LT) and Independent Cascade (IC). Since then, extensive research has focused on studying different variations (Goyal et al 2013;Carnes et al 2007) among which Keikha et al (2020) take advantage of a network embedding approach by applying a k-means method on the embedding space to select the resulting k cluster centroids as initial seeds.…”

Section: Fair Influence Maximizationmentioning

confidence: 99%

CrossWalk: Fairness-Enhanced Node Representation Learning

Khajehnejad

Babaei

et al. 2022

AAAI

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The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node representation learning algorithm, such as DeepWalk and Node2Vec. The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups’ peripheries or (2) connect different groups in the network. CrossWalk pulls nodes that are near groups’ peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph. Extensive experiments show the effectiveness of our algorithm to enhance fairness in various graph algorithms, including influence maximization, link prediction and node classification in synthetic and real networks, with only a very small decrease in performance.

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Section: Fair Influence Maximizationmentioning

confidence: 99%

CrossWalk: Fairness-Enhanced Node Representation Learning

Khajehnejad

Babaei

et al. 2022

AAAI

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“…Keikha et al [24] proposed the DeepIM method to solve the influence maximization problem in the static network using network representation learning. The overall idea of DeepIM is using the network representation learning algorithm to generate the vectors of nodes and then calculating the similarity of chords between nodes to select r nodes with the highest similarity as the correlation vector of each node in the network.…”

Section: Initial Seed Set Calculationmentioning

confidence: 99%

“…We adopt LDAG [10] and DeepIM [24] as the baseline methods and compare them with our DIMNRL solution in terms of influence diffusion range and running time. LDAG is a static network influence maximization algorithm based on the directed cyclic graph.…”

Section: Datasets and Baselinesmentioning

confidence: 99%

Dynamic Influence Maximization via Network Representation Learning

et al. 2022

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Influence maximization is a hot research topic in the social computing field and has gained tremendous studies motivated by its wild application scenarios. As the structures of social networks change over time, how to seek seed node sets from dynamic networks has attracted some attention. However, all of the existing studies were based on network topology structure data which have the limitations of high dimensionality and low efficiency. Aiming at this drawback, we first convert each node in the network to a low-dimensional vector representation by network representation learning and then solve the problem of dynamic influence maximization in the low-dimensional latent space. Comprehensive experiments on NetHEPT, Twitter, UCI, and Wikipedia datasets show that our method can achieve influence diffusion performance similar to state-of-the-art approaches in much less time.

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“…Formula (7) indicates that all weight values shall not exceed their corresponding fluctuation range. The sum of the indicator weights in the same group is 1, which can be guaranteed by formula (8).…”

Section: Optimization Of Indicator Weightmentioning

confidence: 99%

“…Figure 1 is the MSNs model. Influence Maximization (IM) [8][9][10][11][12][13][14][15][16] problem is proposed for the study of social networks, and it comes from marketing of economics. Using social network method to analyze the social relations of mobile users in the network can further improve the efficiency of information transmission and forwarding.…”

Section: Introductionmentioning

confidence: 99%

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Gao¹,

Xu²,

Wang³

et al. 2020

Preprint

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Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

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Influence maximization across heterogeneous interconnected networks based on deep learning

Cited by 55 publications

References 10 publications

CrossWalk: Fairness-Enhanced Node Representation Learning

CrossWalk: Fairness-Enhanced Node Representation Learning

Dynamic Influence Maximization via Network Representation Learning

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

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