Update README.md

Author: AlexWorldD

README.md

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 # NetEmbs
 [![Build Status](https://travis-ci.com/AlexWorldD/NetEmbs.svg?token=KxxnGy2fzypoq5mv4Y2J&branch=master)](https://travis-ci.com/AlexWorldD/NetEmbs) [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
-### TODO before next meeting
-- [ ] Mathematical definition of node's context. Compare it with context definition for words
-- [ ] Modify RandomWalk procedure according to new definition of context
-- [ ] Fix simulation code
-### Initial steps
-- [x] Install *simpy*. Fix Marcel's model. Get sample dataset
-- [x] Install *networkx*, play with it.
-- [x] Implement split for debit/credit and normalization functions.
-- [x] Implement basic visualisation
-- [x] Implement Neighborhoods functions: IN/OUT edges.
-- [x] Implement visualisation of neighbors (highlight IN/OUT context).
-- [ ] Define the architecture of Python module
-### Questions (15.02.2019)
-- [x] Is it possible that the same financial account could be debited during one set of BPs and credited during another set of BP? // **YES**
------
-## Literature
-1. Boersma M. et al. Financial statement networks: an application of network theory in the audit // Draft of paper. 2019. P. 1–33.
-2. Perozzi B., Al-Rfou R., Skiena S. DeepWalk: Online Learning of Social Representations. 2014.
-Grover A., Leskovec J. node2vec // Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’16. New York, New York, USA: ACM Press, 2016. P. 855–864.
-3. Dong Y., Chawla N. V., Swami A. metapath2vec // Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17. New York, New York, USA: ACM Press, 2017. P. 135–144
-4. Gao M. et al. BiNE // The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR ’18. 2018. P. 715–724.
 
+### Framework for Representation Learning on Financial Statement Networks.
+
+Keywords – REPRESENTATION LEARNING, FINANCIAL STATEMENTS, NETWORKS, AUDIT, SAMPLING STRATEGY, SKIP-GRAM MODEL, TRANSACTION DATA
+
+The solution relies on both modelling techniques and machine learning. We give a detail definition of sampling strategy, **fin**Walk on a Financial statement network. The novelty of it is to follow directions of relationships on the network rather than directions of edges. As a result, after learning embeddings, one allows merging a large number of business processes into groups as well as revealing an actual meaning of these groups. 
+
+In the experiments, we demonstrate the results of applying our coarse-graining procedure to simulated. Moreover, we establish the fact that plausible relationship models considering the predicted labels have the same order of accuracy as the models operating with expert labels. Owing to the framework for data simulation (Simulation folder), we ensure the repeatability of our findings and encourage further investigation and improvements. 
+
+-----
+## Used literature
+1. Marcel Boersma et al. “Financial statement networks: an application of network theory in the audit”. In: Journal of Network Theory in Finance 4 (2018), pp. 59–85. ISSN: 0033-3174. DOI: [10.21314/JNTF.2018.048](http://dx.doi.org/10.21314/JNTF.2018.048)
+2. Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. “DeepWalk: Online Learning of Social Representations”. In: Proceedings of the 20th ACM SIGKDD interna- tional conference on Knowledge discovery and data mining - KDD ’14. New York, New York, USA: ACM Press, Mar. 2014, pp. 701–710. ISBN: 9781450329569. DOI: [10.1145/2623330.2623732](https://doi.org/10.1145/3132847.3132959)
+3. Yuxiao Dong, Nitesh V. Chawla, and Ananthram Swami. “metapath2vec”. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’17. New York, New York, USA: ACM Press, 2017, pp. 135–144. ISBN: 9781450348874. DOI: [10.1145/3097983.3098036](https://doi.org/10.1145/3097983.3098036)