On Network Similarities and Their Applications

Network representations of biological systems have provided a more in-depth understanding of cellular and molecular interaction mechanisms. The comparison of complex biological networks is an ongoing area of research. It is necessary to identify similarity measures which are particularly suited for biological network analysis. We focus our survey on network similarity measures in the context of directed, weighted, and structurally similar biological networks. We summarize the underlying mechanisms and features exploited by such measures, which help to bring out the differences or similarities between networks. We also provide a brief overview of studies which have been performed using these measures on different biological network data. Finally, we show performance results obtained by applying the measures on tumor metabolic networks.

[1]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[2]  V Latora,et al.  Efficient behavior of small-world networks. , 2001, Physical review letters.

[3]  Tore Opsahl,et al.  Clustering in weighted networks , 2009, Soc. Networks.

[4]  X. Chen,et al.  Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. , 2011, The Journal of clinical investigation.

[5]  Giovanni Montana,et al.  Differential analysis of biological networks , 2015, BMC Bioinformatics.

[6]  Panos M. Pardalos,et al.  Network Design and Optimization for Smart Cities , 2017 .

[7]  A. Hill,et al.  Biologically active constituents of the secretome of human W8B2+ cardiac stem cells , 2018, Scientific Reports.

[8]  Dominik Endres,et al.  A new metric for probability distributions , 2003, IEEE Transactions on Information Theory.

[9]  Panos M. Pardalos,et al.  Assessing diversity in multiplex networks , 2018, Scientific Reports.

[10]  Reinhard Schneider,et al.  Using graph theory to analyze biological networks , 2011, BioData Mining.

[11]  Clifford Stein,et al.  Introduction to Algorithms, 2nd edition. , 2001 .

[12]  Panos M. Pardalos,et al.  Network Models in Economics and Finance , 2014 .

[13]  David K. Hammond,et al.  Graph diffusion distance: A difference measure for weighted graphs based on the graph Laplacian exponential kernel , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[14]  G. Fagiolo.  Clustering in complex directed networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  L. Bécu,et al.  Evidence for three-dimensional unstable flows in shear-banding wormlike micelles. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Yongtang Shi,et al.  Fifty years of graph matching, network alignment and network comparison , 2016, Inf. Sci..

[17]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .

[18]  Eva Carrasco,et al.  Triple negative breast cancer subtypes and pathologic complete response rate to neoadjuvant chemotherapy , 2018, Oncotarget.

[19]  Mario R. Guarracino,et al.  Model simplification for supervised classification of metabolic networks , 2019, Annals of Mathematics and Artificial Intelligence.

[20]  Rosanna Grassi,et al.  Directed clustering in weighted networks: a new perspective , 2017, ArXiv.

[21]  P. Jaccard,et al.  Etude comparative de la distribution florale dans une portion des Alpes et des Jura , 1901 .

[22]  Mara Sangiovanni,et al.  Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer , 2019, BMC Bioinformatics.

[23]  Mario Rosario Guarracino,et al.  An integrated approach to infer cross-talks between intracellular protein transport and signaling pathways , 2018, BMC Bioinformatics.

[24]  Federico Carpi,et al.  Electrically tuning soft membranes to both a higher and a lower transparency , 2019, Scientific Reports.

[25]  Mario Markus,et al.  Multipeaked probability distributions of recurrence times. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  V. Latora,et al.  Efficiency of scale-free networks: error and attack tolerance , 2002, cond-mat/0205601.

[27]  Regina Berretta,et al.  Basal-like breast cancer: molecular profiles, clinical features and survival outcomes , 2017, BMC Medical Genomics.

[28]  András Jánossy,et al.  “高磁場Gd3+電子スピン共鳴により測定したYBa2Cu4O8における磁場誘起低エネルギースピン励起”へのコメント , 2001 .

[29]  A. Barabasi,et al.  Hierarchical Organization of Modularity in Metabolic Networks , 2002, Science.

[30]  Cesare Furlanello,et al.  The HIM glocal metric and kernel for network comparison and classification , 2012, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[31]  En Wang,et al.  Cut Based Method for Comparing Complex Networks , 2018, Scientific Reports.

[32]  S. Holmes,et al.  Tracking network dynamics: A survey using graph distances , 2018, The Annals of Applied Statistics.

[33]  Pietro Hiram Guzzi,et al.  Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin , 2017, Briefings Bioinform..

[34]  Flemming Topsøe,et al.  Jensen-Shannon divergence and Hilbert space embedding , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[35]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[36]  L. da F. Costa,et al.  Characterization of complex networks: A survey of measurements , 2005, cond-mat/0505185.

[37]  Matthias Dehmer,et al.  A history of graph entropy measures , 2011, Inf. Sci..

[38]  Panos M. Pardalos,et al.  Quantification of network structural dissimilarities , 2017, Nature Communications.

[39]  Mikko Kivelä,et al.  Generalizations of the clustering coefficient to weighted complex networks. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[40]  Panos M. Pardalos,et al.  Supervised Classification of Metabolic Networks , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[41]  Matthias Dehmer,et al.  Comparing Biological Networks: A Survey on Graph Classifying Techniques , 2013 .

[42]  William S. Yamamoto,et al.  AY's Neuroanatomy of C. elegans for Computation , 1992 .

[43]  Mario Rosario Guarracino,et al.  The MetaboX Library: Building Metabolic Networks from KEGG Database , 2015, IWBBIO.

[44]  Sung-Hyuk Cha.  Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions , 2007 .