Identifying Missing and Spurious Interactions in Directed Networks

Recent years, the studies of link prediction have been overwhelmingly emphasizing on undirected networks. Compared with it, how to identify missing and spurious interactions in directed networks has received less attention and still is not well understood. In this paper, we make use of classical link prediction indices for undirected networks, adapt them to directed version which could predict both the existence and direction of an arc between two nodes, and investigate their prediction ability on six real-world directed networks. Experimental results demonstrate that those modified indices perform quite well in directed networks. Compared with Bi-fan predictor, some of them can provide more accurate predictions.

[1]  Gueorgi Kossinets.  Effects of missing data in social networks , 2006, Soc. Networks.

[2]  M. Newman.  Clustering and preferential attachment in growing networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Sophie Ahrens,et al.  Recommender Systems , 2012 .

[4]  D. Baird,et al.  Assessment of spatial and temporal variability in ecosystem attributes of the St Marks national wildlife refuge, Apalachee bay, Florida , 1998 .

[5]  Linyuan Lü,et al.  Predicting missing links via local information , 2009, 0901.0553.

[6]  Roger Guimerà,et al.  Missing and spurious interactions and the reconstruction of complex networks , 2009, Proceedings of the National Academy of Sciences.

[7]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[8]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[9]  Xiaojun Duan,et al.  Systems Science: Methodological Approaches , 2012 .

[10]  Linyuan Lu,et al.  Potential Theory for Directed Networks , 2012, PloS one.

[11]  Giulio Cimini,et al.  Removing spurious interactions in complex networks , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[13]  Taieb Znati,et al.  The Complexity of Channel Scheduling in Multi-Radio Multi-Channel Wireless Networks , 2009, IEEE INFOCOM 2009.

[14]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[15]  Dongyun Yi,et al.  Degree-corrected stochastic block models and reliability in networks , 2014 .

[16]  Ke Xu,et al.  Link prediction in complex networks: a clustering perspective , 2011, The European Physical Journal B.

[17]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[18]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

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

[20]  M. Newman,et al.  Hierarchical structure and the prediction of missing links in networks , 2008, Nature.

[21]  Tao Zhou,et al.  Scale-free networks without growth , 2008 .

[22]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[23]  J. Schafer,et al.  Missing data: our view of the state of the art. , 2002, Psychological methods.