An Evaluation of Various Regression Models for the Prediction of Two-Terminal Network Reliability

Analyzing network data is presently a big challenge for applied machine learning. Many model architectures have been proposed to study or extract information from network data for specific applications. In this paper, we compare the performance of autoencoders, convolutional neural networks and extreme gradient boosting decision trees with different configurations for the task of approximating two-terminal network reliability. The ground truth is generated using an analytical method. Various synthetic datasets containing networks with different configurations are used. The obtained results help us to identify the dataset factors which affect the prediction performance of these models.

[1]  Yoshua Bengio,et al.  Object Recognition with Gradient-Based Learning , 1999, Shape, Contour and Grouping in Computer Vision.

[2]  Jure Leskovec,et al.  Representation Learning on Graphs: Methods and Applications , 2017, IEEE Data Eng. Bull..

[3]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[4]  Yixin Chen,et al.  An End-to-End Deep Learning Architecture for Graph Classification , 2018, AAAI.

[5]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[6]  Bo Zong,et al.  Substructure Assembling Network for Graph Classification , 2018, AAAI.

[7]  Yang Liu,et al.  graph2vec: Learning Distributed Representations of Graphs , 2017, ArXiv.

[8]  Lise Getoor,et al.  Collective Classification in Network Data , 2008, AI Mag..

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Foster J. Provost,et al.  A Brief Survey of Machine Learning Methods for Classification in Networked Data and an Application to Suspicion Scoring , 2006, SNA@ICML.

[11]  Andry Alamsyah,et al.  Multiple Regression to Analyse Social Graph of Brand Awareness , 2017 .

[12]  Sabina-Adriana Floria,et al.  Two Approximate Approaches for Reliability Evaluation in Large Networks. Comparative Study , 2018, 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC).

[13]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[14]  Petru Cascaval,et al.  SDP algorithm for network reliability evaluation , 2017, 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA).

[15]  Fabrice Rossi,et al.  Graphs in machine learning. An introduction , 2015, ESANN.

[16]  Mahantapas Kundu,et al.  The journey of graph kernels through two decades , 2018, Comput. Sci. Rev..

[17]  David Martin,et al.  Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network , 2003, Genome Biology.

[18]  Ryan A. Rossi,et al.  Network Classification and Categorization , 2017, ArXiv.

[19]  William W. Cohen,et al.  Semi-Supervised Classification of Network Data Using Very Few Labels , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[20]  Sebastian Nowozin,et al.  gBoost: a mathematical programming approach to graph classification and regression , 2009, Machine Learning.

[21]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[22]  Prabhat,et al.  Graph Neural Networks for IceCube Signal Classification , 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

[23]  Palash Goyal,et al.  Graph Embedding Techniques, Applications, and Performance: A Survey , 2017, Knowl. Based Syst..