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The importance of the use of networks to model and analyse biological dataand the interplay of bio-molecules is widely recognised. Consequently, manyalgorithms for the analysis and the comparison of networks (such as alignmentalgorithms) have been developed in the past. Recently, many differentapproaches tried to integrate into a single model the interplay of differentmolecules, such as genes, transcription factors and microRNAs. A possibleformalism to model such scenario comes from node coloured networks (orheterogeneous networks) implemented as node/ edge-coloured graphs.Consequently, the need for the introduction of alignment algorithms able toanalyse heterogeneous networks arises. To the best of our knowledge, all theexisting algorithms are not able to mine heterogeneous networks. We propose atwo-step alignment strategy that receives as input two heterogeneous networks(node-coloured graphs) and a similarity function among nodes of two networksextending the previous formulations. We first build a single alignment graph.Then we mine this graph extracting relevant subgraphs. Despite this simpleapproach, the analysis of such networks relies on graph and subgraphisomorphism and the size of the data is still growing. Therefore the use ofhigh-performance data analytics framework is needed. We here presentHetNetAligner a framework built on top of Apache Spark. We also implemented ouralgorithm, and we tested it on some selected heterogeneous biological networks.Preliminary results confirm that our method may extract relevant knowledge frombiological data reducing the computational time.

HetNetAligner: Design and Implementation of an algorithm for heterogeneous network alignment on Apache Spark