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arXiv 2017年

作者： Ren, Xiao-Long Gleinig, Niels Tolić, Dijana Antulov-Fantulin, Nino Computational Social Science ETH Zürich Clausiusstraße 50 Zürich8092 Switzerland Laboratory for Machine Learning and Knowledge Representations Rudjer Bošković Institute Zagreb Croatia

The robustness of complex networks under targeted attacks is deeply connected to the resilience of complex systems, i.e., the ability to make appropriate responses to the attacks. In this article, we investigated the state-of-the-art targeted node attack algorithms and demonstrate that they become very inefficient when the cost of the attack is taken into consideration. In this paper, we made explicit assumption that the cost of removing a node is proportional to the number of adjacent links that are removed, i.e., higher degree nodes have higher cost. Finally, for the case when it is possible to attack links, we propose a simple and efficient edge removal strategy named Hierarchical Power Iterative Normalized cut (HPI-Ncut). The results on real and artificial networks show that the HPI-Ncut algorithm outperforms all the node removal and link removal attack algorithms when the cost of the attack is taken into consideration. In addition, we show that on sparse networks, the complexity of this hierarchical power iteration edge removal algorithm is only O(nlog2+Ε(n)). Copyright © 2017, The Authors. All rights reserved.

关键词： Complex networks

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Inferring short-term volatility indicators from the Bitcoin blockchain

arXiv

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arXiv 2018年

作者： Antulov-Fantulin, Nino Tolic, Dijana Piskorec, Matija Ce, Zhang Vodenska, Irena Computational Social Science ETH Zurich Switzerland Laboratory for Machine Learning and Knowledge Representations Institute Ruder Bošković Croatia Systems Group ETH Zurich Switzerland Metropolitan College Boston University United States

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume. Copyright © 2018, The Authors. All rights reserved.

关键词： Blockchain

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A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering

arXiv

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arXiv 2017年

作者： Tolic, Dijana Antulov-Fantulin, Nino Kopriva, Ivica Laboratory for Machine Learning and Knowledge Representations Ruder Boškovic Institute Bijenicka cesta 54 Zagreb10000 Croatia ETH Zürich Swiss Federal Institute of Technology COSS CLU Clausiusstrasse 50 Zürich8092 Switzerland

A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernelbased orthogonal multiplicative update rules to solve the subspace clustering problem. In nonlinear orthogonal NMF framework, we propose two subspace clustering algorithms, named kernel-based nonnegative subspace clustering KNSC-Ncut and KNSC-Rcut and establish their connection with spectral normalized cut and ratio cut clustering. We further extend the nonlinear orthogonal NMF framework and introduce a graph regularization to obtain a factorization that respects a local geometric structure of the data after the nonlinear mapping. The proposed NMF-based approach to subspace clustering takes into account the nonlinear nature of the manifold, as well as its intrinsic local geometry, which considerably improves the clustering performance when compared to the several recently proposed state-of-the-art methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Clustering algorithms

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Coastal water quality prediction based on machine learning with feature interpretation and Spatio-temporal analysis

arXiv

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arXiv 2021年

作者： Grbčić, Luka Družeta, Siniša Mauša, Goran Lipić, Tomislav Lušić, Darija Vukić Alvir, Marta Lučin, Ivana Sikirica, Ante Davidović, Davor Travaš, Vanja Kalafatović, Daniela Pikelj, Kristina Fajković, Hana Holjević, Toni Kranjčević, Lado Department of Fluid Mechanics and Computational Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Department of Computer Engineering Faculty of Engineering University of Rijeka Vukovarska 58 Rijeka51000 Croatia Center for Advanced Computing and Modelling University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Department of Hydrotechnics and Geotechnics Faculty of Civil Engineering University of Rijeka Radmile Matejčić 3 Rijeka51000 Croatia Department of Environmental Health Faculty of Medicine University of Rijeka Braće Branchetta 20/1 Rijeka51000 Croatia Department of Environmental Health Teaching Institute of Public Health of Primorje-Gorski Kotar County Krešimirova 52a Rijeka51000 Croatia Department of Biotechnology University of Rijeka Radmile Matejčić 2 Rijeka51000 Croatia Laboratory for Machine Learning and Knowledge Representations Rudjer Boskovic Institute Biljenička cesta 54 Zagreb10000 Croatia Center for Informatics and Computing Ruđer Bošković Institute Biljenička cesta 54 Zagreb10000 Croatia Department of Geology Faculty of Science University of Zagreb Horvatovac 102a Zagreb10000 Croatia

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial *** and enterococci models achieved strong R2 values of 0.85 and 0.83, while the temporal models achieved R2 values of 0.74 and 0.67. The temporal model also achieved moderate R2 values of 0.44 and 0.46 at a site with historically high coastal water quality. © 2021, CC BY-NC-ND.

关键词： machine learning

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