Synwalk: community detection via random walk modelling

Toth, Christian; Helić, Denis; Geiger, Bernhard C.

doi:10.1007/s10618-021-00809-w

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…With the enhanced estimation, we attempt to promote the best possible estimation from each basic algorithm. This idea follows the principle that there is no link prediction algorithm which is the best for every type of network, but there are link prediction algorithms which, depending on the type of network to which they are applied [30][31][32], return good or bad results or estimates.…”

Section: System Modelmentioning

confidence: 99%

A Combinatory Framework for Link Prediction in Complex Networks

Dimitriou,

Karyotis

2023

Applied Sciences

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Link prediction is a very important field in network science with various emerging algorithms, the goal of which is to estimate the presence or absence of an edge in the network. Depending on the type of network, different link prediction algorithms can be applied, being less or more effective in the relevant scenarios. In this work, we develop a novel framework that attempts to compose the best features of link prediction algorithms when applied to a network, in order to have even more reliable predictions, especially in topologies emerging in Industrial Internet of Things (IIoT) environments. According to the proposed framework, we first apply appropriate link prediction algorithms that we have chosen for an analyzed network (basic algorithms). Each basic algorithm gives us a numerical estimate for each missing edge in the network. We store the results of each basic algorithm in appropriate structures. Then we provide them as input to a developed genetic algorithm. The genetic algorithm evaluates the results of the basic algorithms for each missing edge of the network. At each missing edge of the network and from generation to generation, it composes the estimates of the basic algorithms regarding each edge and produces a new optimized estimate. This optimization results in a vector of weights where each weight corresponds to the effectiveness of the prediction for each of the basic algorithms we have employed. With these weights, we build a new enhanced predictor tool, which can obtain new optimized estimates for each missing edge in the network. The enhanced predictor tool applies to each missing edge the basic algorithms, normalizes the basic algorithms’ estimates, and, using the weights of the estimates derived from the genetic algorithm, returns a new estimate of whether or not an edge will be added in the future. According to the results of our experiments on several types of networks with five well-known link prediction algorithms, we show that the new enhanced predictor tool yields in every case better predictions than each individual algorithm, therefore providing an accuracy-targeting alternative in the existing state of the art.

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Section: System Modelmentioning

confidence: 99%

A Combinatory Framework for Link Prediction in Complex Networks

Dimitriou,

Karyotis

2023

Applied Sciences

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“…They assigned importance scores to each node using a novel local similarity measure, selected initial core nodes, and expanded communities by balancing the diffusion of labels from core to boundary nodes, achieving rapid convergence in large-scale networks with stable and accurate results. Toth et al [22] proposed the Synwalk algorithm, which incorporates the concept of random blocks into random walk-based community detection algorithms, combining the strengths of representative algorithms like Walktrap [23] and Infomap [24], yielding promising results. Yang et al [25] introduced a method of enhancing Markov similarity, which utilizes the steady-state Markov transition of the initial network to derive an enhanced Markov similarity matrix.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Huang,

2023

Applied Sciences

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Community detection is an important task in the analysis of complex networks, which is significant for mining and analyzing the organization and function of networks. As an unsupervised learning algorithm based on the particle competition mechanism, stochastic competitive learning has been applied in the field of community detection in complex networks, but still has several limitations. In order to improve the stability and accuracy of stochastic competitive learning and solve the problem of community detection, we propose an unsupervised community detection algorithm LNSSCL (Local Node Similarity-Integrated Stochastic Competitive Learning). The algorithm calculates node degree as well as Salton similarity metrics to determine the starting position of particle walk; local node similarity is incorporated into the particle preferential walk rule; the particle is dynamically adjusted to control capability increments according to the control range; particles select the node with the strongest control capability within the node to be resurrected; and the LNSSCL algorithm introduces a node affiliation selection step to adjust the node community labels. Experimental comparisons with 12 representative community detection algorithms on real network datasets and synthetic networks show that the LNSSCL algorithm is overall better than other compared algorithms in terms of standardized mutual information (NMI) and modularity (Q). The improvement effect for the stochastic competition learning algorithm is evident, and it can effectively accomplish the community detection task in complex networks.

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“…While the connection between clustering and coarse graining is easy to see, also other approaches to Markov model reduction can be used to solve unsupervised machine learning problems. As just one example we point at the community detection method proposed in [27], where the authors aimed for a simplified parameterization of a Markov chain derived from the original graph. The existence of this example suggests that many more approaches discussed in this survey may be successfully applied to problems of knowledge discovery and data mining.…”

Section: Quantizing or Pruning The Transition Probability Matrixmentioning

confidence: 99%

“…Further, while the approximation P in [79] has low rank, the approximation in [24] may not be so. The authors of [27] simplify the model parameterization by prescribing that P has a specific structure. Specifically, also assuming a candidate coarse graining g, the authors require that P is constructed as follows:…”

Section: Other Approachesmentioning

confidence: 99%

Information-Theoretic Reduction of Markov Chains

Geiger¹

2022

Preprint

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We survey information-theoretic approaches to the reduction of Markov chains. Our survey is structured in two parts: The first part considers Markov chain coarse graining, which focuses on projecting the Markov chain to a process on a smaller state space that is informative about certain quantities of interest. The second part considers Markov chain model reduction, which focuses on replacing the original Markov model by a simplified one that yields similar behavior as the original Markov model. We discuss the practical relevance of both approaches in the field of knowledge discovery and data mining by formulating problems of unsupervised machine learning as reduction problems of Markov chains. Finally, we briefly discuss the concept of lumpability, the phenomenon when a coarse graining yields a reduced Markov model.

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Synwalk: community detection via random walk modelling

Cited by 11 publications

References 45 publications

A Combinatory Framework for Link Prediction in Complex Networks

A Combinatory Framework for Link Prediction in Complex Networks

Unsupervised Community Detection Algorithm with Stochastic Competitive Learning Incorporating Local Node Similarity

Information-Theoretic Reduction of Markov Chains

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