This book describes Python3 programming resources for implementing decision aiding algorithms in the context of a bipolarvalued outranking approach. These computing resources made available under the name Digraph3 are useful in the field of Algorithmic Decision Theory and more specifically in outrankingbased MultipleCriteria Decision Aiding MCDA. The first part of the book presents a set of tutorials introducing the Digraph3 collection of Python3 modules and its main objects such as bipolarvalued digraphs and outranking digraphs. In eight methodological chapters the second part illustrates multiplecriteria evaluation modelsampnbspand decision algorithms. These chapters are largely problemoriented and demonstrate how to edit a new multiplecriteria performance tableau how to build a best choice recommendation how to compute the winner of an election and how to make rankings or ratings using incommensurable criteria. The books third part presents three realworld decision case studies while the fourth part addresses more advanced topics such as computing ordinal correlations between bipolarvalued outranking digraphs computing kernels in bipolarvalued digraphs testing for confidence or stability of outranking statements when facing uncertain or solely ordinal criteria significance weights and tempering plurality tyranny effects in social choice problems. The fifth and last part is more specifically focused on working with undirected graphs tree graphs and forests. The closing chapter explores comparability split interval and permutation graphs. The book is primarily intended for graduate students in management sciences computational statistics and operations research. The chapters presenting algorithms for ranking multicriteria performance records will be of computational interest for designers of web recommender systems. Similarly the relative and absolute quantilerating algorithms discussed and illustrated in several chapters will be of practical interest to public