Link prediction for interdisciplinary collaboration via coauthorship network
 533 Downloads
Abstract
We analyse the Publication and Research data set of University of Bristol collected between 2008 and 2013. Using the existing coauthorship network and academic information thereof, we propose a new link prediction methodology, with the specific aim of identifying potential interdisciplinary collaboration in a universitywide collaboration network.
Keywords
Coauthorship network Interdisciplinary collaboration Bipartite network Link prediction1 Introduction

complex modern problems, such as climate change and resource security, require many types of expertise across multiple disciplines;

scientific discoveries are more likely to be made on the boundaries between fields, with the influence of big data science on many disciplines as an example; and

encounters with others fields benefit single disciplines and broaden their horizons.
As a way of promoting interdisciplinary research, Brown et al. (2015) suggested ‘the institutions to identify research strengths that show potential for interdisciplinary collaboration and incentivise it through seed grants’. Faced with the problem of utilising limited resources, decision makers in academic organisations may focus on promoting existing collaborations between different disciplines. However, it could also be of interest to identify the disciplines that have not yet collaborated to this date but have the potential to develop and benefit from collaborative research given the nurturing environment.
Thus motivated, the current paper has a twofold goal: from the perspective of methodological development, we introduce new methods for predicting edges in a network; from the policy making perspective, we provide decision makers a systematic way of introducing or evaluating calls for interdisciplinary research, based on the potential for interdisciplinary collaboration detected from the existing coauthorship network. In doing so, we analyse the University of Bristol’s research output data set, which contains the coauthorship network among the academic staff and information on their academic membership, including the (main) disciplines where their research lies in.
Link prediction is a fundamental problem in network statistics. Besides the applications to coauthorship networks, link prediction problems are of increasing interests for friendship recommendation in social networks (e.g. LibenNowell and Kleinberg 2007), exploring collaboration in academic contexts (e.g. Kuzmin et al. 2016; Wang and Sukthankar 2013), discovering unobserved relationships in food webs (e.g. Wang et al. 2014), understanding the protein–protein interactions (e.g. Martńez et al. 2014) and gene regulatory networks (e.g. Turki and Wang 2015), to name but a few. Due to the popularity of link prediction in a wide range of applications, many efforts have been made in developing statistical methods for link prediction problems. LibenNowell and Kleinberg (2007), Lü and Zhou (2011) and Martínez et al. (2016), among others, are some recent survey papers on this topic. The methods developed can be roughly categorised into modelfree and modelbased methods.
Among the modelfree methods, some are based on information from neighbours (e.g. LibenNowell and Kleinberg 2007; Adamic and Adar 2003; Zhou et al. 2009) to form similarity measures and predict linkage; some are based on geodesic path information (e.g. Katz 1953; Leicht et al. 2006); some use the spectral properties of adjacency matrices (e.g. Fouss et al. 2007). Among the modelbased methods, some exploit random walks on the graphs to predict future linkage (e.g. Page et al. 1999; Jeh and Widom 2002; Liu and Lü 2010); some predict links based on probabilistic models (e.g. Geyer 1992); some estimate the network structure via maximum likelihood estimation (e.g. Guimerá and SalesPardo 2009); others utilise the community detection methods (e.g. Clauset et al. 2008).
The link prediction problem in this paper shares similarity with the abovementioned ones. However, we also note on the fundamental difference that we collect the data at the level of individual researchers for the largesize network thereof, but the conclusion we seek is for the smallsize network with nodes representing the individuals’ academic disciplines, which are given in the data set. Nodes of the smallsize network are different from communities: memberships to the communities are typically unknown and the detection of community structure is often itself of separate interest, whereas academic affiliations, which we use as a proxy for academic disciplines, are easily accessible and treated as known in our study.
The rest of the paper is organised as follows. Section 2 provides a detailed description of the Publication and Research data set collected at the University of Bristol, as well as the networks arising from the data. In Sect. 3, we propose a link prediction algorithm, compare its performance in combination with varying similarity measures for predicting the potential interdisciplinary research links via thorough study of the coauthorship network, and demonstrate the good performance of our proposed method. Section 4 concludes the paper. Appendix provides additional information about the data set.
2 Data description and experiment setup
2.1 Data set

Outputs’ titles and publication dates;

Authors’ publication names, job titles, affiliations within the University;

University organisation structures: there are 6 Faculties, and each Faculty has a few Schools and/or Centres (see Tables 1 and 3 in Appendix). We will refer to the Schools and Centres as the Schoollevel organisations, or simply Schools, in the rest of the paper.

2926 staff, 20 of which have multiple Faculty affiliations, and 36 of which have multiple Schoollevel affiliations;

20740 outputs, including 3002 outputs in Year 2008, 3084 in 2009, 3371 in 2010, 3619 in 2011, 3797 in 2012, and 3867 in 2013.
Organisation hierarchy structure within the University, full names of which can be found in Table 3 in Appendix
UNIV  

FSCI  FSSL  FMVS  FOAT  FMDY  FENG  
GELY  GEOG  PSYC  MATH  PHYS  EDUC  LAWD  SPAI  PHPH  BIOC  PANM  MODL  HUMS  SART  VESC  SOCS  ORDS  SSCM  QUEN  MVEN  EENG 
CHEM  BISC  NSQI  SCIF  EFIM  SPOL  SSLF  MSAD  MVSF  LANG  ARTF  MEED  CHSE  MDYF  GSEN  ENGF 
Note that this data set only includes all the authors within the University, i.e. if a paper has authors outside the University, (disciplines of) these authors are not reflected in the data set nor the analysis conducted in this paper. Also, we omit from our analysis any contribution to books and anthologies, conference proceedings and software. In Summer 2017, the University has renamed the Schools in the Faculty of Engineering and Faculty of Health Sciences and merged SOCS and SSCM as Bristol Medical School (see Table 3). In this paper, we keep the structure and names used for the data period.
2.2 Experiment setup and notation
In order to investigate the prediction performance of the proposed methods, we split the whole data set into training and test sets, which contain the research outputs published in Years 2008–2010 and Years 2011–2013, respectively.

Coauthorship network: the nodes are individual researchers (\(\mathcal I\)), and the edges connecting pairs of researchers indicate that they have joint publications.

Researcherjournal network: in this bipartite network, the nodes are researchers (\(\mathcal I\)) and journals (\(\mathcal J\)), and there is an edge connecting a researcher and a journal if the researcher has published in the journal.

School network: the nodes are Schoollevel organisations (\(\mathcal O\)), and the edges connecting pairs of organisations indicate that they have collaboration in ways which are to be specified; we wish to predict links in this network.
Then, \(E^{\mathrm {new}} = E^{\mathrm {train}} {\setminus } E^{\mathrm {test}}\) denotes the collection of new Schoollevel collaborative links appearing in the test set only. In this data set, there are 260 pairs of Schools which have no collaborations in the training set, and \(E^{\mathrm {new}} = m_{\mathrm {new}} = 37\) new pairs of Schools which have developed collaborations in the test set. Our aim is to predict as many edges in \(E^{\mathrm {new}}\) as possible using the training set, without incurring too many false positives. We would like to point out that false positives can also be interpreted as potential collaboration which has not be materialised in the whole data set.
3 Link prediction
3.1 Methodology
 (i)
By observing the potential for future collaboration among the individuals and then aggregating the scores according to their affiliations for link prediction in the School network, or
 (ii)
by forming the School network based on the existing coauthorship network (namely, \((\mathcal O, E^{\text{ train }})\)) and predicting the links thereof.
 Step 1

Obtain the similarity scores for the pairs of individuals as \(\{w^0_{ij}; \, i, j \in \mathcal {I}\}\) using the training data.
 Step 2

Assign weights \(w_{kl}\) to the edges in the School network by aggregating \(w^0_{ij}\) for i with \(\mathcal {S}(i) = k\) and j with \(\mathcal {S}(j) = l\).
 Step 3
 Select the set of predicted edges asfor a given threshold \(\pi\).$$\begin{aligned} E^{\mathrm {pred}} = \{(k, l):\, w_{kl} > \pi \hbox { and } (k, l) \notin E^{\mathrm {train}}\}, \end{aligned}$$
We propose two different methods for assigning the similarity scores \(w^0_{ij}\) to the pairs of individual researchers in Step 1, and aggregating them into the School network edge weights \(w_{kl}\) in Step 2. We first compute \(w^0_{ij}\) using the coauthorship network only (Sect. 3.1.1), and explore ways of further integrating the additional layer of information by adopting the bipartite network between the individuals and journals (Sect. 3.1.2).
3.1.1 Similarity scores based on the coauthorship network
As noted in Clauset et al. (2008), neighbour or pathbased methods have been known to work well in link prediction for strongly assortative networks such as collaboration and citation networks. If researchers A and B have both collaborated with researcher C in the past, it is reasonable to expect the collaboration between A and B if they have not done so yet. In the same spirit, one can also predict linkage based on other functions of neighbourhood.
 (a)
Length2 geodesic path. Set \(w^0_{ij} = 1\) if there is a length2 geodesic path connecting i and j based on \(A^{\mathrm {train}}\).
 (b)Number of common direct neighbours. Let \(w^0_{ij}\) be the number of distinct length2 geodesic paths linking i and j based on \(A^{\mathrm {train}}\), i.e.where \(\mathcal {N}^{\mathrm {train}}(i) = \{k: A^{\mathrm {train}}_{ik} > 0\}\).$$\begin{aligned} w^0_{ij} = \mid \mathcal {N}^{\mathrm {train}}(i) \cap \mathcal {N}^{\mathrm {train}}(j)\mid , \end{aligned}$$
 (c)Number of common order2 neighbourhood. Let \(w^0_{ij}\) be the number of common order2 neighbours of i and j; in other words,$$\begin{aligned} w^0_{ij} =&\mid \left( \mathcal {N}^{\mathrm {train}}(i) \cup \{k: \, k\in \mathcal {N}^{\mathrm {train}}(l), \, l \in \mathcal {N}^{\mathrm {train}} (i)\}\right) \\ \cap&\left( \mathcal {N}^{\mathrm {train}}(j) \cup \{k: \, k\in \mathcal {N}^{\mathrm {train}}(l), \, l \in \mathcal {N}^{\mathrm {train}}(j)\}\right) \mid . \end{aligned}$$
 (d)Sum of weights of path edges. Let \(w^0_{ij}\) be the sum of the \(A^{\mathrm {train}}\) weights of all the length2 geodesic paths linking i and j, i.e. listing all length2 geodesic paths connecting i and j as \(\{i, k_1, j\}, \{i, k_2, j\}, \ldots , \{i, k_m, j\}\), \(m \ge 1\), we set$$\begin{aligned} w^0_{ij} = \sum _{s = 1}^m (A^{\mathrm {train}}_{i, k_s} + A^{\mathrm {train}}_{k_s, j}). \end{aligned}$$
3.1.2 Similarity scores based on the bipartite network
In the research output data set, we have additional information, namely the journals in which the research outputs have been published, which can augment the coauthorship network for School network link prediction. Our motivation comes from the observation that when researchers from different organisations publish their research outputs in the same (or similar) journals but have not collaborated yet to this date, it indicates that they have the potential to form interdisciplinary collaboration with each other. A similar idea has been adopted in e.g. Kuzmin et al. (2016) for identifying the potential for scientific collaboration among molecular researchers, by adding the layer of the paths of molecular interactions to the coauthorship network.
Recall the incidence matrix for the researcherjournal bipartite network in the training set, \(I^{\text{ train }}\). In the bipartite network, we define the neighbours of the researcher i as the journals in which i has published, and denote the set of neighbours by \(\mathcal J^{\text{ train }}(i) = \{j \in \mathcal {J}: \, I^{\text{ train }}_{ij} \ne 0\}\). Analogously, for journal j, its neighbours are those researchers who have published in the journal, and its set of neighbours is denoted by \(\mathcal I^{\text{ train }}(j) = \{i \in \mathcal {I}: \, I^{\text{ train }}_{ij} \ne 0\}\).
 Jaccard’s coefficient The Jaccard coefficient that measures the similarity between finite sets, is extended to compare the neighbours of two individual researchers asThis definition simply counts the number of journals shared by i and \(i'\), and hence gives more weights to a pair of researchers who, for example, each published one paper in two common journals, than those who published multiple papers in a single common journal, given that \(\vert \mathcal J(i) \cup \mathcal J(i') \vert\) remains the same. Therefore, we propose a slightly modified definition which takes into account the number of publications:$$\begin{aligned} \sigma _{\text{ Jaccard }}^1(i, i') = \frac{\vert \mathcal J(i) \cap \mathcal J(i') \vert }{\vert \mathcal J(i) \cup \mathcal J(i') \vert }. \end{aligned}$$$$\begin{aligned} \sigma _{\text{ Jaccard }}^2(i, i') = \frac{\sum _{j \in \mathcal J(i) \cap \mathcal J(i')} (I_{ij} + I_{i'j})}{\sum _{j \in \mathcal J(i) \cup \mathcal J(i')} (I_{ij} + I_{i'j})}. \end{aligned}$$
 Adamic and Adar (2003) The rarer a journal is (in terms of total publications made in the journal), two researchers that share the journal may be deemed more similar. Hence, we adopt the similarity measure originally proposed in Adamic and Adar (2003) for measuring the similarity between two personal home pages based on the common features, which refines the simple counting of common features by weighting rarer features more heavily:$$\begin{aligned} \sigma _{\text{ AA }}(i, i') = \sum _{j \in \mathcal J(i) \cap \mathcal J(i')} \frac{1}{\log (\sum _{l \in \mathcal I(j)} I_{lj})} \end{aligned}$$
 Cooccurrence We note the resemblance between the problem of edge prediction in a coauthorship network and that of stochastic language modelling for unseen bigrams (pairs of words that cooccur in a test corpus but not in the training corpus), and adapt the ‘smoothing’ approach of Essen and Steinbiss (1992). We first compute the similarity between journals using \(\sigma ^k_{\text{ Jaccard }}, \, k = 1, 2\) and augment the similarity score between a pair of researchers by taking into account not only those journals directly shared by the two, but also those which are close to those journals:$$\begin{aligned} \sigma ^k_{\text{ cooc }}(i, i')= & {} \sum _{j \in \mathcal J(i)}\sum _{j' \in \mathcal J(i')} \frac{I_{ij}}{\sum _l I_{il}} \cdot \frac{I_{i'j'}}{\sum _l I_{i'l}} \cdot \sigma ^k_{\text{ Jaccard }}(j, j'), \quad k = 1, 2. \end{aligned}$$
3.2 Results
Summary of the links predicted with the similarity measures and the thresholds chosen with \(p \in \{1, 0.4, 0.3, 0.2\}\) as described in Sect. 3.1.1, and those described in Sect. 3.1.2 with \(d \in \{\hbox {NA}, \infty , 10, 4\}\), in comparison with the links predicted by a modularitymaximising community detection method (comm. detect.) with varying number of communities N. There are 37 pairs of Schools which have developed new collaborations in the test set, out of 260 pairs that have no collaborations in the training set
Sect. 3.1.1  Sect. 3.1.2  Comm. detect.  

p  (a)  (b)  (c)  (d)  d  \(\sigma ^1_{\text{ Jaccard }}\)  \(\sigma ^2_{\text{ Jaccard }}\)  \(\sigma _{\text{ AA }}\)  \(\sigma ^1_{\text{ cooc }}\)  \(\sigma ^2_{\text{ cooc }}\)  N  
# Of edges  1  80  80  80  80  NA  43  45  44  33  28  5  31 
Accuracy  .338  .338  .338  .338  .488  .489  .432  .606  .679  0.129  
Recall  .365  .365  .365  .365  .284  .298  .257  .270  .257  0.054  
# Of edges  0.4  49  32  32  33  \(\infty\)  20  18  26  26  17  6  25 
Accuracy  .388  .500  .469  .424  .650  .667  .615  .769  .824  0.160  
Recall  .257  .216  .203  .189  .176  .162  .217  .270  .189  0.054  
# Of edges  0.3  24  24  24  25  10  18  18  23  27  17  7  24 
Accuracy  .541  .583  .500  .480  .667  .722  .652  .704  .824  0.166  
Recall  .176  .189  .162  .162  .162  .176  .203  .257  .189  0.050  
# Of edges  0.2  24  16  16  21  4  4  4  5  16  5  8  21 
Accuracy  .541  .625  .586  .523  .500  .750  .800  .688  .800  0.095  
Recall  .176  .135  .122  .149  .027  .041  .054  .149  .054  0.027 
Table 2 shows that the performance of the link prediction algorithm, combined with the similarity scores based on the coauthorship network, is not sensitive to the choice of the weights (a)–(d) nor the threshold (p): all 16 combinations outperform the random choice, and do not differ too much among themselves. Only counting the length2 geodesic path pairs, the score (a) predicts the most edges among them, and when no thresholding is applied (\(p = 1\)), all (a)–(d) select the same cohort of edges. From Fig. 2, it is observable that the four similarity scores still differ by preferring different edges. For instance, with (b) and (c), the edge between SSCM and GEOG is assigned a relatively larger weight than when (a) is used.
It is evident that by taking into account the additional layer of information on journals enhances the prediction accuracy considerably, returning a larger proportion of true positives among a fewer number of predicted edges in general (thus fewer false positives). In particular, combining the similarity measure \(\sigma ^1_{\text{ cooc }}\), which takes into account the similarity among the journals as well, with the choice \(d \in \{\infty , 10\}\) returns a set of predicted edges that is comparable to the set of edges predicted with the scores from Sect. 3.1.1 in terms of its size, while achieving higher prediction accuracy and recall. Among possible values for d, most scores perform the best with \(d =10\), which aggregates the similarities between two individuals in forming School network edge weights, provided that their geodesic distance in the coauthorship network is less than 10; an exception is \(\sigma ^1_{\text{ cooc }}\), where slight improvement is observed with \(d = \infty\).
For comparison, Table 2 also reports the results from applying a modularitymaximising hierarchical community detection method to the School network constructed from \(A^{\mathrm {train}}\). Here, we assign an edge between Schools k and l, \(k, l \in \mathcal {O}\) with the number of publications between the researchers from the two Schools as its weight, and the prediction is made by linking all the members (Schools) in the same communities. Modularity optimisation algorithms are known to suffer from the resolution limit, and strong connections among a small number of nodes in large networks are not well detected by such methods (Fortunato and Barthelemy 2007; Alzahrani and Horadam 2016). Noting the nature of interdisciplinary research collaboration, which is often driven by a small number of individuals, we choose to apply the community detection method to the School network of smaller size rather than to the coauthorship network, following the approach described in (ii) at the beginning of Sect. 3.1.
The optimal cut results in 21 different communities at the School level, which leads to too few predicted edges. We therefore trace back in the dendrogram and show the results corresponding to the cases in which there are 5–8 communities. It is clearly seen from the outcome that our proposed method outperforms the community detection method regardless of the choice of similarity scores or other parameters. In fact, community detection often performs worse than random guessing in link prediction. This may be attributed to modularity maximisation assuming all communities in a network to be statistically similar Newman 2016, whereas the PURE data set is highly unbalanced with regard to both the numbers of academic staff and publications at different Schools, see Fig. 1. On the other hand, our proposed method observes the potential for collaborative research at the individual level and then aggregates the resulting scores to infer the interdisciplinary collaboration potential, and hence can predict the links between, e.g., a relatively small organisation (BIOC) and a large one (SSCM) as well as that between BIOC and another organisation of similar size (PSYC), see the bottom right panel of Fig. 3.
Our proposed method predicts edges which do not appear in the test data set. On one hand, this can be interpreted as false positive prediction, but on the other, it may be due to the time scale limitation, i.e. these edges may appear after Year 2013, or the Schools connected still have the potential to form collaborative links which are yet to be realised.
4 Discussion
In this paper, we tackle the problem of predicting potential interdisciplinary research by transforming it to a membership network link prediction problem. Two types of similarity scores have been proposed in this paper, one employing only the coauthorship network and the other integrating additional information which is naturally available for the research output data. As expected, when we have more information in hand, the prediction accuracy improves. Within each type of scores, different choices of scores or parameters do not differ by much in their performance when applied to the PURE data set. However, this does not guarantee that the same robustness can be expected when different data sets are used.
We would like to suggest that the practitioners make their own choice according to the aim of the analysis, and different behaviours of different metrics used may reflect the underlying properties of specific data set. For example, when using the coauthor relationship only, if we also care about the amount of joint publications, then the similarity score (b) is more suitable. When additional information is available, \(\sigma ^1_{\text{ cooc }}\) returns the best prediction accuracy by taking into account not only those journals directly shared by two individuals, but also the journals which are similar to them. Also, the scores proposed in Sect. 3.1.2 tend to return fewer edges and, consequently, fewer false positives which, for some applications, may be a more important criterion than the measure of prediction accuracy used in this paper.
We would also like to point out one main limitation of this paper. The problem here is to predict linkage between disciplines within a university. However, due to the lack of information, it is not possible to map all individuals to disciplines, and therefore, we equate disciplines with academic organisations within the university. In most situations, this remedy works well, especially in traditional disciplines such as civil engineering, pure mathematics and languages, among others, which are all categorised well within the School framework. Relatively newer disciplines, however, do not have clear School boundaries, e.g., there are statisticians working in the School of Mathematics, School of Social and Community Medicine and School of Engineering. This situation, on the other hand, also means mathematics, public health and engineering have shared interests in the modern world.
Finally, the paper focuses on predicting academic collaboration links from the coauthorship network, but we would like to point out that the proposed method and similarity scores per se are not limited to a single organisation or, indeed, an application area. For example, we may suggest interaction between different communities based on their members’ Facebook networks, using both Facebook friend lists and additional information such as their taste in music or films.
Notes
Acknowledgements
We thank the PURE team and the Jean Golding Institute at the University of Bristol for providing the data set. We thank Professor Jonathan C. Rougier for all the constructive discussions, comments and his input in the data analysis. We also thank the Editor and the two referees for their constructive suggestions.
References
 Adamic LA, Adar E (2003) Friends and neighbors on the web. Soc. Netw 25:211–230CrossRefGoogle Scholar
 Alzahrani T, Horadam KJ (2016) Community detection in bipartite networks: algorithms and case studies. Complex systems and networks. Springer, Berlin, Heidelberg, pp 25–50Google Scholar
 Brown RR, Deletic A, Wong THF (2015) How to catalyse collaboration. Nature 525:315–317CrossRefGoogle Scholar
 Chamberlain S, Boettiger C, Hart T, Ram K (2014) rcrossref: R Client for Various CrossRef APIs. R package version 0.3.0 https://github.com/ropensci/rcrossref
 Clauset A, Moore C, Newman ME (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453:98CrossRefGoogle Scholar
 Elsevier (2015) A review of the UK’s interdisciplinary research using a citationbased approach. http://www.hefce.ac.uk/pubs/rereports/year/2015/interdisc/
 Essen U, Steinbiss V (1992) Cooccurrence smoothing for stochastic language modeling. Proc IEEE Int Conf Acoust Speech Signal Process 1:161–164Google Scholar
 Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci USA 104:36–41CrossRefGoogle Scholar
 Fouss F, Pirotte A, Renders JM, Saerens M (2007) Randomwalk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19:355–369CrossRefGoogle Scholar
 Geyer CJ (1992) Practical Markov chain Monte Carlo. Stat Sci 7:473–483CrossRefGoogle Scholar
 Guimerá R, SalesPardo M (2009) Missing and spurious interactions and the reconstruction of complex networks. Proc Natil Acad Sci 106:22073–22078CrossRefGoogle Scholar
 Jeh G, Widom J (2002) SimRank: a measure of structuralcontext similarity. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (KDD02), ACM, pp 538–543Google Scholar
 Katz L (1953) A new status index derived from sociometric analysis. Psychometrika 18:39–43CrossRefzbMATHGoogle Scholar
 Kuzmin K, Lu X, Mukherjee PS, Zhuang J, Gaiteri C, Szymanski BK (2016) Supporting novel biomedical research via multilayer collaboration networks. Appl Netw Sci 1:11CrossRefGoogle Scholar
 Ledford H (2015) Tean science. Nature 525:308–311CrossRefGoogle Scholar
 Leicht EA, Holme P, Newman MEJ (2006) Vertex similarity in networks. Phys Rev E 73:026120CrossRefGoogle Scholar
 LibenNowell D, Kleinberg J (2007) The linkprediction problem for social networks. J Assoc Inf Sci Technol 58:1019–31CrossRefGoogle Scholar
 Liu W, Lü L (2010) Link prediction based on local random walk. EPL (Europhysics Letters) 89:58007CrossRefGoogle Scholar
 Lü L, Zhou T (2011) Link prediction in complex networks: a survey. Phys A Stat Mech Appl 390:1150–70CrossRefGoogle Scholar
 Martínez V, Berzal F, Cubero JC (2016) A survey of link prediction in complex networks. ACM Comput Surv (CSUR) 49:69CrossRefGoogle Scholar
 Martńez V, Cano C, Blanco A (2014) ProphNet: a generic prioritization method through propagation of information. BMC Bioinform 15:S5CrossRefGoogle Scholar
 Nature (2015) A special issue on interdisciplinary research. https://www.nature.com/news/interdisciplinarity1.18295
 Newman M E J (2016) Community detection in networks: Modularity optimization and maximum likelihood are equivalent. arXiv preprint arXiv:1606.02319
 Page L, Brin S, Motwani R, Winograd T (1999) The PageRank citation ranking: bringing order to the web. Technical Report 1999–66. Stanford InfoLabGoogle Scholar
 Rylance R (2015) Global funders to focus on interdisciplinarity. Nature 525:313–315CrossRefGoogle Scholar
 Turki T, Wang J T L (2015). A new approach to link prediction in gene regulatory networks. In: International conference on intelligent data engineering and automated learning. Springer International Publishing, pp 404–415Google Scholar
 Wang L, Hu K, Tang Y (2014) Robustness of linkprediction algorithm based on similarity and application to biological networks. Curr Bioinform 9:246–252CrossRefGoogle Scholar
 Wang X, Sukthankar G (2013) Link prediction in multirelational collaboration networks. In Proceedings of the 2013 IEEE/ACM international conference on advances in social networks analysis and mining, ACM, pp 1445–1447Google Scholar
 Woelert P, Millar V (2013) The ‘paradox of interdisciplinarity’ in Australian research governance. High Educ 66:755–767CrossRefGoogle Scholar
 Zhou T, Lü L, Zhang YC (2009) Predicting missing links via local information. Eur Phys J B 71:623–630CrossRefzbMATHGoogle Scholar
Copyright information
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.